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Rossen Valkanov

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," NBER Working Papers 10914, National Bureau of Economic Research, Inc.

    Cited by:

    1. Hautsch, Nikolaus & Voigt, Stefan, 2017. "Large-scale portfolio allocation under transaction costs and model uncertainty," CFS Working Paper Series 582, Center for Financial Studies (CFS).
    2. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    3. Etienne, Xiaoli, 2015. "Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices," 2015 Conference, August 9-14, 2015, Milan, Italy 211626, International Association of Agricultural Economists.
    4. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    5. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 265-286, March.
    6. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. Adam Clements & Yin Liao, 2014. "The role in index jumps and cojumps in forecasting stock index volatility: Evidence from the Dow Jones index," NCER Working Paper Series 101, National Centre for Econometric Research.
    9. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    10. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    11. Aharon, David Y. & Qadan, Mahmoud, 2020. "When do retail investors pay attention to their trading platforms?," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    12. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    13. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    14. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    15. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    16. Byun, Suk Joon & Kim, Jun Sik, 2013. "The information content of risk-neutral skewness for volatility forecasting," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 142-161.
    17. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    18. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    19. Hooper, Vincent J. & Ng, Kevin & Reeves, Jonathan J., 2008. "Quarterly beta forecasting: An evaluation," International Journal of Forecasting, Elsevier, vol. 24(3), pages 480-489.
    20. Christopher F. Baum & Mustafa Caglayan & Oleksandr Talavera, 2006. "On the Sensitivity of Firms' Investment to Cash Flow and Uncertainty," Boston College Working Papers in Economics 638, Boston College Department of Economics, revised 26 Apr 2008.
    21. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    22. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    23. Babii, Andrii & Florens, Jean-Pierre, 2020. "Is completeness necessary? Estimation in nonidentified linear models," TSE Working Papers 20-1091, Toulouse School of Economics (TSE).
    24. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    25. Tsiakas, Ilias & Zhang, Haibin, 2021. "Economic fundamentals and the long-run correlation between exchange rates and commodities," Global Finance Journal, Elsevier, vol. 49(C).
    26. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    27. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    28. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    29. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    30. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    31. González-Sánchez, Mariano & Nave, Juan & Rubio, Gonzalo, 2020. "Effects of uncertainty and risk aversion on the exposure of investment-style factor returns to real activity," Research in International Business and Finance, Elsevier, vol. 53(C).
    32. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    33. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    34. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    35. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.
    36. Nguyen, Hoang & Javed, Farrukh, 2021. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Working Papers 2021:15, Örebro University, School of Business.
    37. Ole E. Barndorff-Nielsen & Sven Erik Graversen & Jean Jacod & Neil Shephard, 2005. "Limit theorems for bipower variation in financial econometrics," OFRC Working Papers Series 2005fe09, Oxford Financial Research Centre.
    38. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    39. Chang, Tsangyao & Hsu, Chen-Min & Chen, Sheng-Tung & Wang, Mei-Chih & Wu, Cheng-Feng, 2023. "Revisiting economic growth and CO2 emissions nexus in Taiwan using a mixed-frequency VAR model," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 319-342.
    40. Anthony S. Tay, 2006. "Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth," Macroeconomics Working Papers 22480, East Asian Bureau of Economic Research.
    41. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.
    42. Asai, Manabu & Brugal, Ivan, 2013. "Forecasting volatility via stock return, range, trading volume and spillover effects: The case of Brazil," The North American Journal of Economics and Finance, Elsevier, vol. 25(C), pages 202-213.
    43. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    44. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    45. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    46. Shuichi Nagata, 2012. "Consistent Estimation of Integrated Volatility Using Intraday Absolute Returns for SV Jump Diffusion Processes," Economics Bulletin, AccessEcon, vol. 32(1), pages 306-314.
    47. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
    48. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," PIER Working Paper Archive 05-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    49. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    50. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
    51. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
    52. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    53. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    54. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    55. Hideyuki Takamizawa, 2015. "Predicting Interest Rate Volatility Using Information on the Yield Curve," International Review of Finance, International Review of Finance Ltd., vol. 15(3), pages 347-386, September.
    56. Ryan T. Ball & Lindsey Gallo & Eric Ghysels, 2019. "Tilting the evidence: the role of firm-level earnings attributes in the relation between aggregated earnings and gross domestic product," Review of Accounting Studies, Springer, vol. 24(2), pages 570-592, June.
    57. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.
    58. Zea Bermudez, Patrícia de & Marín Díazaraque, Juan Miguel & Rue, Havard & Lopes Moreira Da Veiga, María Helena, 2021. "Integrated nested Laplace approximations for threshold stochastic volatility models," DES - Working Papers. Statistics and Econometrics. WS 31804, Universidad Carlos III de Madrid. Departamento de Estadística.
    59. Henryk Gurgul & Roland Mestel & Robert Syrek, 2017. "MIDAS models in banking sector – systemic risk comparison," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 18(2), pages 165-181.
    60. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    61. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," Economics Papers 2005-W16, Economics Group, Nuffield College, University of Oxford.
    62. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, vol. 18(C), pages 123-140.
    63. Sarah Goldman & Virginia Zhelyazkova, 2023. "CO2 Emissions and GDP: A Revisited Kuznets Curve Version via a Panel Threshold MIDAS-VAR Model in Europe for a Recent Period," Economic Research Guardian, Weissberg Publishing, vol. 13(2), pages 82-99, December.
    64. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    65. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    66. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    67. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
    68. Alain Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Mico Loretan, 2008. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," BIS Working Papers 249, Bank for International Settlements.
    69. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    70. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    71. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 257-271, January.
    72. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    73. Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print hal-00741630, HAL.
    74. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    75. Serdengeçti, Süleyman & Sensoy, Ahmet & Nguyen, Duc Khuong, 2021. "Dynamics of return and liquidity (co) jumps in emerging foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    76. Tian, Fengping & Yang, Ke & Chen, Langnan, 2017. "Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity," International Journal of Forecasting, Elsevier, vol. 33(1), pages 132-152.
    77. Michael P. Clements & Ana Beatriz Galvão, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206, November.
    78. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    79. Andrew J. Patton & Tarun Ramadorai, 2013. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," Journal of Finance, American Finance Association, vol. 68(2), pages 597-635, April.
    80. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    81. Bai, Yiyi & Okullo, Samuel J., 2023. "Drivers and pass-through of the EU ETS price: Evidence from the power sector," Energy Economics, Elsevier, vol. 123(C).
    82. Wing Hong Chan & Ranjini Jha & Madhu Kalimipalli, 2009. "The Economic Value Of Using Realized Volatility In Forecasting Future Implied Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 231-259, September.
    83. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    84. El-Shagi, Makram, 2016. "Much ado about nothing: Sovereign ratings and government bond yields in the OECD," IWH Discussion Papers 22/2016, Halle Institute for Economic Research (IWH).
    85. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    86. Jad Beyhum & Jonas Striaukas, 2023. "Sparse plus dense MIDAS regressions and nowcasting during the COVID pandemic," Papers 2306.13362, arXiv.org, revised Dec 2023.
    87. Guillaume Bagnarosa & Mark Cummins & Michael Dowling & Fearghal Kearney, 2022. "Commodity risk in European dairy firms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 151-181.
    88. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    89. Guo, Xiaozhu & Huang, Dengshi & Li, Xiafei & Liang, Chao, 2023. "Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 672-693.
    90. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 94-138.
    91. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    92. Sucarrat, Genaro & Grønneberg, Steffen, 2016. "Models of Financial Return With Time-Varying Zero Probability," MPRA Paper 68931, University Library of Munich, Germany.
    93. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    94. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    95. Andersen, Torben G. & Bollerslev, Tim & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies (CFS).
    96. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc.
    97. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    98. Fang, Libing & Yu, Honghai & Huang, Yingbo, 2018. "The role of investor sentiment in the long-term correlation between U.S. stock and bond markets," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 127-139.
    99. Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik, 2021. "Dimensionality reduction in forecasting with temporal hierarchies," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1127-1146.
    100. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    101. Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023. "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, vol. 5(2), pages 295-365, June.
    102. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    103. Philip Hans Franses, 2019. "On inflation expectations in the NKPC model," Empirical Economics, Springer, vol. 57(6), pages 1853-1864, December.
    104. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    105. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andre Lucas, 2011. "Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," Tinbergen Institute Discussion Papers 11-042/2/DSF16, Tinbergen Institute.
    106. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    107. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 77-124.
    108. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    109. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, Department of Economics and Business Economics, Aarhus University.
    110. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    111. Herwartz, Helmut & Golosnoy, Vasyl, 2007. "Semiparametric Approaches to the Prediction of Conditional Correlation Matrices in Finance," Economics Working Papers 2007-23, Christian-Albrechts-University of Kiel, Department of Economics.
    112. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    113. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2009. "Jackknife Estimator for Tracking Error Variance of Optimal Portfolios," Management Science, INFORMS, vol. 55(6), pages 990-1002, June.
    114. Andreou, Elena & Ghysels, Eric, 2021. "Predicting the VIX and the volatility risk premium: The role of short-run funding spreads Volatility Factors," Journal of Econometrics, Elsevier, vol. 220(2), pages 366-398.
    115. Maojun Zhang & Yang Zhao & Jiangxia Nan, 2022. "Economic policy uncertainty and volatility of treasury futures," Review of Derivatives Research, Springer, vol. 25(1), pages 93-107, April.
    116. León Valle Ángel & Nave Pineda Juan & Rubio Irigoyen Gonzalo, 2005. "The Relationship between Risk and Expected Return in Europe," Working Papers 201025, Fundacion BBVA / BBVA Foundation.
    117. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    118. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    119. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    120. H. J. Turtle & Kainan Wang, 2014. "Modeling Conditional Covariances With Economic Information Instruments," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 217-236, April.
    121. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    122. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    123. Becker Ralf & Clements Adam E & Hurn Stan, 2011. "Semi-Parametric Forecasting of Realized Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-23, May.
    124. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    125. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
    126. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    127. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
    128. Amendola, Alessandra & Braione, Manuela & Candila, Vincenzo & Storti, Giuseppe, 2020. "A Model Confidence Set approach to the combination of multivariate volatility forecasts," International Journal of Forecasting, Elsevier, vol. 36(3), pages 873-891.
    129. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
    130. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    131. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    132. Ramazan Gencay & Nikola Gradojevic & Faruk Selcuk & Brandon Whitcher, 2010. "Asymmetry of information flow between volatilities across time scales," Quantitative Finance, Taylor & Francis Journals, vol. 10(8), pages 895-915.
    133. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    134. Visser, Marcel P., 2008. "Garch Parameter Estimation Using High-Frequency Data," MPRA Paper 9076, University Library of Munich, Germany.
    135. Ooft, Gavin & Bhaghoe, Sailesh & Hans Franses, Philip, 2021. "Forecasting annual inflation in Suriname," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    136. Wong, Wing-Keung & McAleer, Michael, 2009. "Mapping the Presidential Election Cycle in US stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(11), pages 3267-3277.
    137. Ayinde, Taofeek O. & Olaniran, Abeeb O. & Abolade, Onomeabure C. & Ogbonna, Ahamuefula Ephraim, 2023. "Technology shocks - Gold market connection: Is the effect episodic to business cycle behaviour?," Resources Policy, Elsevier, vol. 84(C).
    138. Fernandes, Leonardo H.S. & Silva, José W.L. & de Araujo, Fernando H.A. & Ferreira, Paulo & Aslam, Faheem & Tabak, Benjamin Miranda, 2022. "Interplay multifractal dynamics among metal commodities and US-EPU," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    139. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    140. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    141. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    142. Anindya Biswas, 2015. "The output gap and inflation in U.S. data: an empirical note," Economics Bulletin, AccessEcon, vol. 35(2), pages 841-845.
    143. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    144. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    145. Huiling Yuan & Yong Zhou & Zhiyuan Zhang & Xiangyu Cui, 2019. "Forecasting security's volatility using low-frequency historical data, high-frequency historical data and option-implied volatility," Papers 1907.02666, arXiv.org.
    146. Talavera, Oleksandr & Tsapin, Andriy & Zholud, Oleksandr, 2012. "Macroeconomic uncertainty and bank lending: The case of Ukraine," Economic Systems, Elsevier, vol. 36(2), pages 279-293.
    147. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    148. Bauer, Gregory H. & Vorkink, Keith, 2011. "Forecasting multivariate realized stock market volatility," Journal of Econometrics, Elsevier, vol. 160(1), pages 93-101, January.
    149. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    150. Huiling Yuan & Guodong Li & Junhui Wang, 2022. "High-Frequency-Based Volatility Model with Network Structure," Papers 2204.12933, arXiv.org.
    151. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    152. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    153. Lu, Fei & Ma, Feng & Guo, Qiang, 2023. "Less is more? New evidence from stock market volatility predictability," International Review of Financial Analysis, Elsevier, vol. 89(C).
    154. Bandi, Federico M. & Russell, Jeffrey R., 2011. "Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations," Journal of Econometrics, Elsevier, vol. 160(1), pages 145-159, January.
    155. Clements, Adam & Liao, Yin, 2017. "Forecasting the variance of stock index returns using jumps and cojumps," International Journal of Forecasting, Elsevier, vol. 33(3), pages 729-742.
    156. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    157. Isao Ishida & Virmantas Kvedaras, 2015. "Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity," Econometrics, MDPI, vol. 3(1), pages 1-53, January.
    158. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    159. Alexander Subbotin & Thierry Chauveau & Kateryna Shapovalova, 2009. "Volatility Models: from GARCH to Multi-Horizon Cascades," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00390636, HAL.
    160. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    161. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    162. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    163. Qifa Xu & Zezhou Wang & Cuixia Jiang & Yezheng Liu, 2023. "Deep learning on mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2099-2120, December.
    164. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    165. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
    166. Tobias Eckernkemper & Bastian Gribisch, 2021. "Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 883-910, August.
    167. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    168. Eric Girardin & Roselyne Joyeux, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Post-Print hal-01499615, HAL.
    169. Alexander Correa, 2021. "Forecasting Tourist Arrivals to Colombia from Google Trends Search Criteria," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 105-134, July-Dece.
    170. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    171. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    172. Elena Andreou & Andros Kourtellos, 2015. "The State and the Future of Cyprus Macroeconomic Forecasting," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 73-90, June.
    173. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    174. Jong-Min Kim & Hojin Jung & Li Qin, 2017. "A new generalized volatility proxy via the stochastic volatility model," Applied Economics, Taylor & Francis Journals, vol. 49(23), pages 2259-2268, May.
    175. Ioannis Kasparis & Peter C.B. Phillips, 2009. "Dynamic Misspecification in Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1700, Cowles Foundation for Research in Economics, Yale University.
    176. Alexander Aue & Lajos Horváth & Clifford M. Hurvich & Philippe Soulier, 2014. "Limit Laws in Transaction-Level Asset Price Models," Post-Print hal-00583372, HAL.
    177. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    178. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    179. Kunst, Robert M. & Franses, Philip Hans, 2010. "Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data," Economics Series 252, Institute for Advanced Studies.
    180. Lee A. Smales, 2021. "The effect of treasury auctions on 10‐year Treasury note futures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 1517-1555, April.
    181. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    182. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    183. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    184. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    185. Benoît Sévi, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Post-Print hal-01500860, HAL.
    186. Lucian-Liviu Albu & Radu Lupu & Adrian Cantemir Calin, 2015. "Interactions between financial markets and macroeconomic variables in EU: a nonlinear modeling approach," ERSA conference papers ersa15p685, European Regional Science Association.
    187. Ghysels, Eric & Ball, Ryan, 2017. "Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?," CEPR Discussion Papers 12179, C.E.P.R. Discussion Papers.
    188. MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 112974, University Library of Munich, Germany.
    189. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    190. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    191. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    192. Alessandra Amendola & Vincenzo Candila & Fabrizio Cipollini & Giampiero M. Gallo, 2020. "Doubly Multiplicative Error Models with Long- and Short-run Components," Papers 2006.03458, arXiv.org.
    193. Adam Clements & Annastiina Silvennoinen, 2009. "On the economic benefit of utility based estimation of a volatility model," NCER Working Paper Series 44, National Centre for Econometric Research.
    194. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    195. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    196. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    197. Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.
    198. Ghysels, Eric & Sohn, Bumjean, 2009. "Which power variation predicts volatility well?," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 686-700, September.
    199. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    200. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Cholesky-MIDAS model for predicting stock portfolio volatility," Centre for Growth and Business Cycle Research Discussion Paper Series 149, Economics, The University of Manchester.
    201. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    202. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    203. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    204. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    205. Marcin Kacperczyk & Paul Damien & Stephen G. Walker, 2013. "A new class of Bayesian semi-parametric models with applications to option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 13(6), pages 967-980, May.
    206. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    207. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    208. Kerssenfischer, Mark & Schmeling, Maik, 2022. "What moves markets?," Discussion Papers 16/2022, Deutsche Bundesbank.
    209. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series 80, National Centre for Econometric Research.
    210. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    211. Afees A. Salisu & Raymond Swaray, 2020. "Forecasting the Return Volatility of Energy Prices: A GARCH-MIDAS Approach," World Scientific Book Chapters, in: Stéphane Goutte & Duc Khuong Nguyen (ed.), HANDBOOK OF ENERGY FINANCE Theories, Practices and Simulations, chapter 3, pages 47-71, World Scientific Publishing Co. Pte. Ltd..
    212. Belén Nieto & Alfonso Novales Cinca & Gonzalo Rubio, 2014. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns," Documentos de Trabajo del ICAE 2014-25, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    213. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    214. Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023. "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    215. Smales, L.A., 2021. "Macroeconomic news and treasury futures return volatility: Do treasury auctions matter?," Global Finance Journal, Elsevier, vol. 48(C).
    216. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    217. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    218. Ralf Becker & Denise R. Osborn, 2012. "Weighted Smooth Transition Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 795-811, August.
    219. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
    220. Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    221. Qian, Hang, 2010. "Vector autoregression with varied frequency data," MPRA Paper 34682, University Library of Munich, Germany.
    222. Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    223. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    224. Cheng, Mingmian & Swanson, Norman R. & Yang, Xiye, 2021. "Forecasting volatility using double shrinkage methods," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 46-61.
    225. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    226. Christian T. Brownlees & Giampiero Gallo, 2007. "Volatility Forecasting Using Explanatory Variables and Focused Selection Criteria," Econometrics Working Papers Archive wp2007_04, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    227. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Oxford University Press, vol. 8(1), pages 29-56, Winter.
    228. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    229. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    230. Nobuyuki Hanaki & Cars Hommes & Dávid Kopányi & Anita Kopányi-Peuker & Jan Tuinstra, 2023. "Forecasting returns instead of prices exacerbates financial bubbles," Experimental Economics, Springer;Economic Science Association, vol. 26(5), pages 1185-1213, November.
    231. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española / Review of Public Economics, IEF, vol. 211(4), pages 117-146, December.
    232. Wang, Zijun & Khan, M. Moosa, 2017. "Market states and the risk-return tradeoff," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 314-327.
    233. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    234. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    235. Teresa Leal & Diego Pedregal & Javier Pérez, 2011. "Short-term monitoring of the Spanish government balance," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(1), pages 97-119, March.
    236. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je & Gau, Yin-Feng, 2022. "Risk-return trade-off in the Australian Securities Exchange: Accounting for overnight effects, realized higher moments, long-run relations, and fractional cointegration," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 384-401.
    237. Afees A. Salisu & Rangan Gupta, 2019. "How do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Working Papers 201946, University of Pretoria, Department of Economics.
    238. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    239. Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
    240. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    241. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    242. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    243. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    244. Krüger Fabian & Pohlmeier Winfried & Mokinski Frieder, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 63-81, February.
    245. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    246. Adediran, Idris A. & Swaray, Raymond, 2023. "Carbon trading amidst global uncertainty: The role of policy and geopolitical uncertainty," Economic Modelling, Elsevier, vol. 123(C).
    247. Mengxi He & Xianfeng Hao & Yaojie Zhang & Fanyi Meng, 2021. "Forecasting stock return volatility using a robust regression model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1463-1478, December.
    248. Yimin Yang & Fei Jia & Haoran Li, 2023. "Estimation of Panel Data Models with Mixed Sampling Frequencies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 514-544, June.
    249. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    250. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    251. Joe Hirschberg & Jenny Lye, 2021. "Estimating risk premiums for regulated firms when accounting for reference-day variation and high-order moments of return volatility," Environment Systems and Decisions, Springer, vol. 41(3), pages 455-467, September.
    252. Golosnoy, Vasyl & Hamid, Alain & Okhrin, Yarema, 2014. "The empirical similarity approach for volatility prediction," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 321-329.
    253. Wolfgang Härdle & Julius Mungo, 2007. "Long Memory Persistence in the Factor of Implied Volatility Dynamics," SFB 649 Discussion Papers SFB649DP2007-027, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    254. Vasilis Sarafidis & Tom Wansbeek, 2020. "Celebrating 40 Years of Panel Data Analysis: Past, Present and Future," Monash Econometrics and Business Statistics Working Papers 6/20, Monash University, Department of Econometrics and Business Statistics.
    255. Xiafei Li & Dongxin Li & Xuhui Zhang & Guiwu Wei & Lan Bai & Yu Wei, 2021. "Forecasting regular and extreme gold price volatility: The roles of asymmetry, extreme event, and jump," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1501-1523, December.
    256. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    257. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    258. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    259. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
    260. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    261. Dewandaru, Ginanjar & Masih, Rumi & Bacha, Obiyathulla & Masih, A. Mansur M., 2014. "Combining Momentum, Value, and Quality for the Islamic Equity Portfolio: Multi-style Rotation Strategies using Augmented Black Litterman Factor Model," MPRA Paper 56965, University Library of Munich, Germany.
    262. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
    263. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    264. Francis X. Diebold & Kamil Yilmaz, 2008. "Macroeconomic Volatility and Stock Market Volatility, World-Wide," PIER Working Paper Archive 08-031, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    265. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    266. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 73-96, September.
    267. Michael P. Clements & Ana Beatriz Galvão, 2014. "Measuring Macroeconomic Uncertainty: US Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-04, Henley Business School, University of Reading.
    268. Proelss, Juliane & Schweizer, Denis & Seiler, Volker, 2020. "The economic importance of rare earth elements volatility forecasts," International Review of Financial Analysis, Elsevier, vol. 71(C).
    269. Chao Liang & Yan Li & Feng Ma & Yaojie Zhang, 2022. "Forecasting international equity market volatility: A new approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1433-1457, November.
    270. Ralf Becker & Adam Clements, 2007. "Forecasting stock market volatility conditional on macroeconomic conditions," NCER Working Paper Series 18, National Centre for Econometric Research.
    271. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    272. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    273. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    274. Wenting Liu & Zhaozhong Gui & Guilin Jiang & Lihua Tang & Lichun Zhou & Wan Leng & Xulong Zhang & Yujiang Liu, 2023. "Stock Volatility Prediction Based on Transformer Model Using Mixed-Frequency Data," Papers 2309.16196, arXiv.org.
    275. Henker, Thomas & Husodo, Zaäfri A., 2010. "Noise and efficient variance in the Indonesia Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 18(2), pages 199-216, April.
    276. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2013. "On the Benefits of Equicorrelation for Portfolio Allocation," NCER Working Paper Series 99, National Centre for Econometric Research.
    277. Pérez, Javier J. & Pedregal, Diego J., 2008. "Should quarterly government finance statistics be used for fiscal surveillane in Europe?," Working Paper Series 937, European Central Bank.
    278. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    279. Audrino, Francesco, 2014. "Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 43-60.
    280. Qu, Hui & Chen, Wei & Niu, Mengyi & Li, Xindan, 2016. "Forecasting realized volatility in electricity markets using logistic smooth transition heterogeneous autoregressive models," Energy Economics, Elsevier, vol. 54(C), pages 68-76.
    281. Huang, Xiaozhou & Wang, Yubao & Song, Juan, 2023. "The Chinese oil futures volatility: Evidence from high-low estimator information," Finance Research Letters, Elsevier, vol. 56(C).
    282. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    283. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    284. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    285. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, vol. 147(1), pages 34-46, November.
    286. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    287. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2007. "Valuation in US Commercial Real Estate," European Financial Management, European Financial Management Association, vol. 13(3), pages 472-497, June.
    288. Degiannakis, Stavros & Filis, George, 2023. "Oil price assumptions for macroeconomic policy," Energy Economics, Elsevier, vol. 117(C).
    289. Baur, Dirk G. & Dimpfl, Thomas, 2016. "Googling gold and mining bad news," Resources Policy, Elsevier, vol. 50(C), pages 306-311.
    290. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 326-360, Summer.
    291. Anthony S. Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Development Economics Working Papers 22482, East Asian Bureau of Economic Research.
    292. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    293. Bhanu Pratap & Nalin Priyaranjan, 2023. "Macroeconomic effects of uncertainty: a Google trends-based analysis for India," Empirical Economics, Springer, vol. 65(4), pages 1599-1625, October.
    294. D. Schneller & S. Heiden & M. Heiden & A. Hamid, 2018. "Home is Where You Know Your Volatility – Local Investor Sentiment and Stock Market Volatility," German Economic Review, Verein für Socialpolitik, vol. 19(2), pages 209-236, May.
    295. Manabu Asai, 2013. "Heterogeneous Asymmetric Dynamic Conditional Correlation Model with Stock Return and Range," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 469-480, August.
    296. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    297. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    298. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    299. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    300. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    301. Leon, Angel & Nave, Juan M. & Rubio, Gonzalo, 2007. "The relationship between risk and expected return in Europe," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 495-512, February.
    302. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    303. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    304. Pacifico, Antonio, 2020. "Bayesian Fuzzy Clustering with Robust Weighted Distance for Multiple ARIMA and Multivariate Time-Series," MPRA Paper 104379, University Library of Munich, Germany.
    305. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    306. Robin de Vilder & Marcel P. Visser, 2007. "Proxies for daily volatility," PSE Working Papers halshs-00588307, HAL.
    307. Aharon, David Y. & Qadan, Mahmoud, 2018. "What drives the demand for information in the commodity market?," Resources Policy, Elsevier, vol. 59(C), pages 532-543.
    308. Damien Kunjal & Faeezah Peerbhai & Paul-Francois Muzindutsi, 2022. "Political, economic, and financial country risks and the volatility of the South African Exchange Traded Fund market: A GARCH-MIDAS approach," Risk Management, Palgrave Macmillan, vol. 24(3), pages 236-258, September.
    309. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    310. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    311. Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
    312. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, Department of Economics and Business Economics, Aarhus University.
    313. Christoffersen, Peter & Mazzotta, Stefano, 2004. "The informational content of over-the-counter currency options," Working Paper Series 366, European Central Bank.
    314. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    315. Belcaid, Karim & El Ghini, Ahmed, 2019. "U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    316. Adam E Clements & Yin Liao, 2013. "Modeling and forecasting realized volatility: getting the most out of the jump component," NCER Working Paper Series 93, National Centre for Econometric Research.
    317. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    318. Clements, A. & Silvennoinen, A., 2013. "Volatility timing: How best to forecast portfolio exposures," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 108-115.
    319. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    320. Fuertes, Ana-Maria & Izzeldin, Marwan & Kalotychou, Elena, 2009. "On forecasting daily stock volatility: The role of intraday information and market conditions," International Journal of Forecasting, Elsevier, vol. 25(2), pages 259-281.
    321. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    322. Keiichi Goshima & Hiroshi Ishijima & Mototsugu Shintani & Hiroki Yamamoto, 2019. "Forecasting Japanese inflation with a news-based leading indicator of economic activities," CARF F-Series CARF-F-458, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    323. Virmantas Kvedaras & Alfredas Račkauskas, 2010. "Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 600-620, October.
    324. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    325. Tseng Tseng-Chan & Chung Huimin & Huang Chin-Sheng, 2009. "Modeling Jump and Continuous Components in the Volatility of Oil Futures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-30, May.
    326. Ojogho, Osaihiomwan & Egware, Robert Awotu, 2015. "Price Generating Process And Volatility In Nigerian Agricultural Commodities Market," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(4), pages 1-10, October.
    327. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    328. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    329. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    330. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    331. Gong, Xu & Sun, Yi & Du, Zhili, 2022. "Geopolitical risk and China's oil security," Energy Policy, Elsevier, vol. 163(C).
    332. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    333. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    334. Xiafei Li & Yu Wei & Xiaodan Chen & Feng Ma & Chao Liang & Wang Chen, 2022. "Which uncertainty is powerful to forecast crude oil market volatility? New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4279-4297, October.
    335. Dimitra Lamprou, 2015. "Nowcasting GDP in Greece: A Note on Forecasting Improvements from the Use of Bridge Models," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 13(1), pages 85-100.
    336. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
    337. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
    338. Chan-Guk Huh & Jie Wu, 2015. "Linkage between US monetary policy and emerging economies: the case of Korea?s financial market and monetary policy," International Journal of Economic Sciences, International Institute of Social and Economic Sciences, vol. 4(3), pages 1-18, September.
    339. Wang, Jianxin & Yang, Minxian, 2009. "Asymmetric volatility in the foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 597-615, October.
    340. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
    341. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    342. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    343. Zhang, Ning & Su, Xiaoman & Qi, Shuyuan, 2023. "An empirical investigation of multiperiod tail risk forecasting models," International Review of Financial Analysis, Elsevier, vol. 86(C).
    344. Łukasz Lenart & Agnieszka Leszczyńska-Paczesna, 2016. "Do market prices improve the accuracy of inflation forecasting in Poland? A disaggregated approach," Bank i Kredyt, Narodowy Bank Polski, vol. 47(5), pages 365-394.
    345. Eunjeong Choi & Soohwan Cho & Dong Keun Kim, 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability," Sustainability, MDPI, vol. 12(3), pages 1-14, February.
    346. Emmanuel Mamatzakis & Mike G. Tsionas & Steven Ongena, 2023. "Why do households repay their debt in UK during the COVID-19 crisis?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(8), pages 1789-1823, April.
    347. Andrianady, Josué R. & Rajaonarison, Njakanasandratra R. & Razanajatovo, Yves H., 2023. "Estimating Madagascar economic growth using the Mixed Data Sampling (MIDAS) approach," MPRA Paper 118267, University Library of Munich, Germany.
    348. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Lasisi, Lukman & Olaniran, Abeeb, 2022. "Geopolitical risk and stock market volatility in emerging markets: A GARCH – MIDAS approach," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    349. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    350. Jian Zhou, 2017. "Forecasting REIT volatility with high-frequency data: a comparison of alternative methods," Applied Economics, Taylor & Francis Journals, vol. 49(26), pages 2590-2605, June.
    351. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    352. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
    353. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    354. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    355. Ryan T. Ball, 2013. "Does Anticipated Information Impose a Cost on Risk‐Averse Investors? A Test of the Hirshleifer Effect," Journal of Accounting Research, Wiley Blackwell, vol. 51(1), pages 31-66, March.
    356. Brownlees Christian T. & Vannucci Marina, 2013. "A Bayesian approach for capturing daily heterogeneity in intra-daily durations time series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 21-46, February.
    357. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    358. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.
    359. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    360. Tumala, Mohammed M. & Salisu, Afees A. & Atoi, Ngozi V., 2022. "Oil-growth nexus in Nigeria: An ADL-MIDAS approach," Resources Policy, Elsevier, vol. 77(C).
    361. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.
    362. Visser, Marcel P., 2008. "Forecasting S&P 500 Daily Volatility using a Proxy for Downward Price Pressure," MPRA Paper 11100, University Library of Munich, Germany.
    363. Huang, Lin & Wang, Zijun, 2014. "Is the investment factor a proxy for time-varying investment opportunities? The US and international evidence," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 219-232.
    364. Anders B. Trolle & Eduardo S. Schwartz, 2010. "An Empirical Analysis of the Swaption Cube," NBER Working Papers 16549, National Bureau of Economic Research, Inc.
    365. Cenesizoglu, Tolga & Timmermann, Allan, 2012. "Do return prediction models add economic value?," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 2974-2987.
    366. Adlai Fisher & Charles Martineau & Jinfei Sheng, 2022. "Macroeconomic Attention and Announcement Risk Premia," The Review of Financial Studies, Society for Financial Studies, vol. 35(11), pages 5057-5093.
    367. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    368. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    369. Xinjie Lu & Feng Ma & Jiqian Wang & Jing Liu, 2022. "Forecasting oil futures realized range‐based volatility with jumps, leverage effect, and regime switching: New evidence from MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 853-868, July.
    370. Matěj Liberda, 2017. "Mixed-frequency Drivers of Precious Metal Prices," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(6), pages 2007-2015.
    371. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
    372. Maghyereh Aktham & Sweidan Osama & Awartani Basel, 2020. "Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach," Review of Economics, De Gruyter, vol. 71(2), pages 81-99, August.
    373. Alberto Plazzi & Walter Torous & Rossen Valkanov, 2008. "The Cross‐Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(3), pages 403-439, September.
    374. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    375. Lv, Wendai & Qi, Jipeng & Feng, Jing, 2023. "Economic policy uncertainty and environmental governance company volatility: Evidence from China," Research in International Business and Finance, Elsevier, vol. 64(C).
    376. Berger, Philip G., 2011. "Challenges and opportunities in disclosure research—A discussion of ‘the financial reporting environment: Review of the recent literature’," Journal of Accounting and Economics, Elsevier, vol. 51(1), pages 204-218.
    377. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    378. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2011. "The Merit of High-Frequency Data in Portfolio Allocation," SFB 649 Discussion Papers SFB649DP2011-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    379. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    380. Byounghyun Jeon & Sung Won Seo & Jun Sik Kim, 2020. "Uncertainty and the volatility forecasting power of option‐implied volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(7), pages 1109-1126, July.
    381. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
    382. LUPU, Radu & CALIN, Adrian Cantemir, 2014. "A Mixed Frequency Analysis Of Connections Between Macroeconomic Variables And Stock Markets In Central And Eastern Europe," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(2), pages 69-79.
    383. Torun, Erdost & Chang, Tzu-Pu & Chou, Ray Y., 2020. "Causal relationship between spot and futures prices with multiple time horizons: A nonparametric wavelet Granger causality test," Research in International Business and Finance, Elsevier, vol. 52(C).
    384. Qian, Hang, 2010. "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA Paper 32686, University Library of Munich, Germany.
    385. Zhao, Ling, 2023. "Global economic policy uncertainty and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 54(C).
    386. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
    387. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    388. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    389. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    390. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    391. Abdul-Aziz Ibn Musah & Jianguo Du & Hira Salah Ud din Khan & Alhassan Alolo Abdul-Rasheed Akeji, 2018. "The Asymptotic Decision Scenarios of an Emerging Stock Exchange Market: Extreme Value Theory and Artificial Neural Network," Risks, MDPI, vol. 6(4), pages 1-24, November.
    392. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    393. Lee A. Smales, 2022. "The influence of policy uncertainty on exchange rate forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 997-1016, August.
    394. Shuting Liu & Qifa Xu & Cuixia Jiang, 2021. "Systemic risk of China’s commercial banks during financial turmoils in 2010-2020: A MIDAS-QR based CoVaR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 28(18), pages 1600-1609, October.
    395. Adam Clements & Ralf Becker, 2009. "A nonparametric approach to forecasting realized volatility," NCER Working Paper Series 43, National Centre for Econometric Research.
    396. Virbickaitė, Audronė & Ausín, M. Concepción & Galeano, Pedro, 2020. "Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction," Energy Economics, Elsevier, vol. 92(C).
    397. George Filis & Stavros Degiannakis & Zacharias Bragoudakis, 2022. "Forecasting macroeconomic indicators for Eurozone and Greece: How useful are the oil price assumptions?," Working Papers 296, Bank of Greece.
    398. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    399. Robert Akunga & Ahmad Hassan Ahmad & Simeon Coleman, 2023. "Financial market integration in sub‐Saharan Africa: How important is contagion?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3637-3653, October.
    400. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    401. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
    402. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.
    403. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.
    404. Todorova, Neda & Souček, Michael, 2014. "The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range," Economic Modelling, Elsevier, vol. 36(C), pages 332-340.
    405. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
    406. Holmberg, Johan, 2021. "Earnings and Employment Dynamics: Capturing Cyclicality using Mixed Frequency Data," Umeå Economic Studies 991, Umeå University, Department of Economics.

  2. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "There is a Risk-Return Tradeoff After All," NBER Working Papers 10913, National Bureau of Economic Research, Inc.

    Cited by:

    1. Song, Zefang & Song, Xinyuan & Li, Yuan, 2023. "Bayesian Analysis of ARCH-M model with a dynamic latent variable," Econometrics and Statistics, Elsevier, vol. 28(C), pages 47-62.
    2. Antonia Lopez-Villavicencio & Valérie Mignon, 2016. "Exchange Rate Pass-through in Emerging Countries: Do the Inflation Environment, Monetary Policy Regime and Institutional Quality Matter?," Working Papers 2016-07, CEPII research center.
    3. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    4. Hui Guo & Robert F. Whitelaw, 2003. "Uncovering the Risk-Return Relation in the Stock Market," NBER Working Papers 9927, National Bureau of Economic Research, Inc.
    5. Escanciano, Juan Carlos & Pardo-Fernandez, Juan Carlos & Van Keilegom, Ingrid, 2013. "Semiparametric Estimation of Risk-return Relationships," LIDAM Discussion Papers ISBA 2013035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Wang, Wenzhao, 2020. "Institutional investor sentiment, beta, and stock returns," Finance Research Letters, Elsevier, vol. 37(C).
    7. Cardak, Buly A. & Martin, Vance L., 2023. "Household willingness to take financial risk: Stockmarket movements and life‐cycle effects," Journal of Banking & Finance, Elsevier, vol. 149(C).
    8. Anisha Ghosh & Oliver Linton, 2019. "Estimation with Mixed Data Frequencies: A Bias-Correction Approach," CeMMAP working papers CWP65/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    10. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in?finite order," CeMMAP working papers CWP53/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
    12. Prabheesh, K.P. & Sasongko, Aryo & Indawan, Fiskara, 2023. "Did the policy responses influence credit and business cycle co-movement during the COVID-19 crisis? Evidence from Indonesia," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 243-255.
    13. Tim Bollerslev & Uta Kretschmer & Christian Pigorsch & George Tauchen, 2010. "A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects," Working Papers 10-06, Duke University, Department of Economics.
    14. Marianne Andries & Valentin Haddad, 2017. "Information Aversion," NBER Working Papers 23958, National Bureau of Economic Research, Inc.
    15. Yao, Jing & Yang, Yiwen, 2023. "Risk-return tradeoff and serial correlation in the Chinese stock market: A bailout-driven crash feedback hypothesis," Economic Modelling, Elsevier, vol. 129(C).
    16. Pástor, Luboš & Sinha, Meenakshi & Swaminathan, Bhaskaran, 2006. "Estimating the Intertemporal Risk-Return Tradeoff Using the Implied Cost of Capital," CEPR Discussion Papers 5462, C.E.P.R. Discussion Papers.
    17. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    18. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    19. Chiang, Thomas C., 2019. "Empirical analysis of intertemporal relations between downside risks and expected returns—Evidence from Asian markets," Research in International Business and Finance, Elsevier, vol. 47(C), pages 264-278.
    20. Christensen, Bent Jesper & Dahl, Christian M. & Iglesias, Emma M., 2012. "Semiparametric inference in a GARCH-in-mean model," Journal of Econometrics, Elsevier, vol. 167(2), pages 458-472.
    21. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    22. Li, Dandan & Ghoshray, Atanu & Morley, Bruce, 2012. "Measuring the risk premium in uncovered interest parity using the component GARCH-M model," International Review of Economics & Finance, Elsevier, vol. 24(C), pages 167-176.
    23. Martin, Vance L. & Tang, Chrismin & Yao, Wenying, 2021. "Forecasting the volatility of asset returns: The informational gains from option prices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 862-880.
    24. Hong, Seok Young & Linton, Oliver, 2020. "Nonparametric estimation of infinite order regression and its application to the risk-return tradeoff," Journal of Econometrics, Elsevier, vol. 219(2), pages 389-424.
    25. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    26. John Cotter & Enrique Salvador, 2014. "The non-linear trade-off between return and risk: a regime-switching multi-factor framework," Working Papers 201414, Geary Institute, University College Dublin.
    27. González-Sánchez, Mariano & Nave, Juan & Rubio, Gonzalo, 2020. "Effects of uncertainty and risk aversion on the exposure of investment-style factor returns to real activity," Research in International Business and Finance, Elsevier, vol. 53(C).
    28. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    29. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    30. Jin, Xiaoye, 2017. "Time-varying return-volatility relation in international stock markets," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 157-173.
    31. Chiang, Thomas C. & Chen, Xiaoyu, 2016. "Stock returns and economic fundamentals in an emerging market: An empirical investigation of domestic and global market forces," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 107-120.
    32. Antonio Díaz & Carlos Esparcia, 2021. "Dynamic optimal portfolio choice under time-varying risk aversion," International Economics, CEPII research center, issue 166, pages 1-22.
    33. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2017. "Economic Policy Uncertainty and Long-Run Stock Market Volatility and Correlation," CREATES Research Papers 2018-12, Department of Economics and Business Economics, Aarhus University.
    34. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    35. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    36. Yuming Li, 2017. "Risks and rewards for momentum and reversal portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 289-315, August.
    37. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    38. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," CEPR Discussion Papers 11307, C.E.P.R. Discussion Papers.
    39. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    40. Angelos Kanas, 2013. "The risk-return relation and VIX: evidence from the S&P 500," Empirical Economics, Springer, vol. 44(3), pages 1291-1314, June.
    41. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    42. Marks, Joseph M. & Nam, Kiseok, 2018. "Intertemporal risk-return tradeoff in the short-run," Economics Letters, Elsevier, vol. 172(C), pages 81-84.
    43. Hui Guo & Robert Savickas, 2003. "Does idiosyncratic risk matter: another look," Working Papers 2003-025, Federal Reserve Bank of St. Louis.
    44. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    45. Hideyuki Takamizawa, 2015. "Predicting Interest Rate Volatility Using Information on the Yield Curve," International Review of Finance, International Review of Finance Ltd., vol. 15(3), pages 347-386, September.
    46. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    47. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2012. "On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation," Working Papers 0525, University of Heidelberg, Department of Economics.
    48. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, vol. 18(C), pages 123-140.
    49. Mark J. Jensen & John M. Maheu, 2014. "Risk, Return and Volatility Feedback: A Bayesian Nonparametric Analysis," Working Paper series 31_14, Rimini Centre for Economic Analysis.
    50. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    51. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
    52. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    53. Ashby, M. & Linton, O. B., 2022. "Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns?," Cambridge Working Papers in Economics 2259, Faculty of Economics, University of Cambridge.
    54. John M Maheu & Thomas H McCurdy, 2008. "Do high-frequency measures of volatility improve forecasts of return distributions?," Working Papers tecipa-324, University of Toronto, Department of Economics.
    55. Vozlyublennaia, Nadia & Meshcheryakov, Artem, 2014. "Dynamic correlation structure and security risk," Journal of Economics and Business, Elsevier, vol. 73(C), pages 48-64.
    56. Ernst Konrad, 2009. "The impact of monetary policy surprises on asset return volatility: the case of Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 23(2), pages 111-135, June.
    57. Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
    58. Bai, Yiyi & Okullo, Samuel J., 2023. "Drivers and pass-through of the EU ETS price: Evidence from the power sector," Energy Economics, Elsevier, vol. 123(C).
    59. Kiseok Nam & Joshua Krausz & Augustine C. Arize, 2014. "Revisiting the intertemporal risk-return relation: asymmetrical effect of unexpected volatility shocks," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2193-2203, December.
    60. Ekaterina Smetanina, 2017. "Real-Time GARCH," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 561-601.
    61. Wensheng Kang & Ronald A. Ratti & Kyung Hwan Yoon, 2014. "The Impact of Oil Price Shocks on the Stock Market Return and Volatility Relationship," CAMA Working Papers 2014-71, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    62. Juan M. Londono & Nancy R. Xu, 2019. "Variance Risk Premium Components and International Stock Return Predictability," International Finance Discussion Papers 1247, Board of Governors of the Federal Reserve System (U.S.).
    63. Bernardo K. Pagnoncelli & Domingo Ramírez & Hamed Rahimian & Arturo Cifuentes, 2023. "A Synthetic Data-Plus-Features Driven Approach for Portfolio Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 187-204, June.
    64. Marine Carrasco & Barbara Rossi, 2016. "In-Sample Inference and Forecasting in Misspecified Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, July.
    65. Hui Guo & Jason Higbee & Christopher J. Neely, 2006. "Foreign exchange volatility is priced in equities," Working Papers 2004-029, Federal Reserve Bank of St. Louis.
    66. Guillaume Bagnarosa & Mark Cummins & Michael Dowling & Fearghal Kearney, 2022. "Commodity risk in European dairy firms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 151-181.
    67. Getachew, Yoseph Yilma, 2016. "Credit constraints, growth and inequality dynamics," Economic Modelling, Elsevier, vol. 54(C), pages 364-376.
    68. Hunjra, Ahmed Imran & Azam, Muhammad & Niazi, Ghulam Shabbir Khan & Butt, Babar Zaheer & Rehman, Kashif-Ur- & Azam, Rauf i, 2010. "Risk and return relationship in stock market and commodity prices: a comprehensive study of Pakistani markets," MPRA Paper 40662, University Library of Munich, Germany.
    69. Guo, Hui & Savickas, Robert, 2006. "Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 43-56, January.
    70. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    71. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    72. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc.
    73. Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Alagidede, Imhotep Paul & Gil-Alana, Luis Alberiko, 2022. "Re-examination of risk-return dynamics in international equity markets and the role of policy uncertainty, geopolitical risk and VIX: Evidence using Markov-switching copulas," Finance Research Letters, Elsevier, vol. 47(PA).
    74. Kannyiri Thadious Banyen & Joseph Kofi Nkuah, 2015. "Limited Stock Market Participation in Ghana: A Behavioral Explanation," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 3(6), pages 286-305, June.
    75. Kinnunen, Jyri, 2014. "Risk-return trade-off and serial correlation: Do volume and volatility matter?," Journal of Financial Markets, Elsevier, vol. 20(C), pages 1-19.
    76. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    77. Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023. "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, vol. 5(2), pages 295-365, June.
    78. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    79. Ghysels, Eric & Ball, Ryan & Zhou, Huan, 2014. "Can we Automate Earnings Forecasts and Beat Analysts?," CEPR Discussion Papers 10186, C.E.P.R. Discussion Papers.
    80. Haibin Xie & Shouyang Wang, 2015. "Risk-return trade-off, information diffusion, and U.S. stock market predictability," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-20, December.
    81. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2009. "Jackknife Estimator for Tracking Error Variance of Optimal Portfolios," Management Science, INFORMS, vol. 55(6), pages 990-1002, June.
    82. León Valle Ángel & Nave Pineda Juan & Rubio Irigoyen Gonzalo, 2005. "The Relationship between Risk and Expected Return in Europe," Working Papers 201025, Fundacion BBVA / BBVA Foundation.
    83. Osman Kilic & Joseph M. Marks & Kiseok Nam, 2022. "Predictable asset price dynamics, risk-return tradeoff, and investor behavior," Review of Quantitative Finance and Accounting, Springer, vol. 59(2), pages 749-791, August.
    84. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    85. Jim Hanly, 2017. "Managing Energy Price Risk using Futures Contracts: A Comparative Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    86. Bali, Turan G. & Cakici, Nusret & Chabi-Yo, Fousseni, 2015. "A new approach to measuring riskiness in the equity market: Implications for the risk premium," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 101-117.
    87. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021.
    88. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    89. Gui, Zhengqing & Huang, Yangguang & Zhao, Xiaojian, 2021. "Whom to educate? Financial literacy and investor awareness," China Economic Review, Elsevier, vol. 67(C).
    90. Amendola, Alessandra & Candila, Vincenzo & Scognamillo, Antonio, 2015. "On the influence of the U.S. monetary policy on the crude oil price volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207860, Italian Association of Agricultural and Applied Economics (AIEAA).
    91. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
    92. Santa-Clara, Pedro & Yan, Shu, 2004. "Jump and Volatility Risk and Risk Premia: A New Model and Lessons from S&P 500 Options," University of California at Los Angeles, Anderson Graduate School of Management qt5dv8v999, Anderson Graduate School of Management, UCLA.
    93. Cho, Sungjun, 2014. "What drives stochastic risk aversion?," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 44-63.
    94. Eva Ferreira & Mónica Gago & Angel León & Gonzalo Rubio, 2005. "An empirical comparison of the performance of alternative option pricing models," Investigaciones Economicas, Fundación SEPI, vol. 29(3), pages 483-523, September.
    95. Ang, Andrew & Liu, Jun, 2007. "Risk, return, and dividends," Journal of Financial Economics, Elsevier, vol. 85(1), pages 1-38, July.
    96. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
    97. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    98. Elena Andreou, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," University of Cyprus Working Papers in Economics 03-2016, University of Cyprus Department of Economics.
    99. Zhihui Lv & Amanda M. Y. Chu & Wing Keung Wong & Thomas C. Chiang, 2021. "The maximum-return-and-minimum-volatility effect: evidence from choosing risky and riskless assets to form a portfolio," Risk Management, Palgrave Macmillan, vol. 23(1), pages 97-122, June.
    100. Bali, Turan G., 2008. "The intertemporal relation between expected returns and risk," Journal of Financial Economics, Elsevier, vol. 87(1), pages 101-131, January.
    101. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    102. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    103. Ghosh, Anisha & Linton, Oliver, 2007. "Consistent estimation of the risk-return tradeoff in the presence of measurement error," LSE Research Online Documents on Economics 24506, London School of Economics and Political Science, LSE Library.
    104. Felix Holzmeister & Jürgen Huber & Michael Kirchler & Florian Lindner & Utz Weitzel & Stefan Zeisberger, 2019. "What Drives Risk Perception? A Global Survey withFinancial Professionals and Lay People," Working Papers 2019-05, Faculty of Economics and Statistics, Universität Innsbruck.
    105. Tobias Adrian & Richard K. Crump & Erik Vogt, 2019. "Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds," Journal of Finance, American Finance Association, vol. 74(4), pages 1931-1973, August.
    106. Pollet, Joshua M. & Wilson, Mungo, 2010. "Average correlation and stock market returns," Journal of Financial Economics, Elsevier, vol. 96(3), pages 364-380, June.
    107. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    108. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    109. Kumari Ranjita & Kumar Nishant, 2020. "Ownership Structure and the Risk: Analysis of Indian Firms," Acta Universitatis Sapientiae, Economics and Business, Sciendo, vol. 8(1), pages 39-52, October.
    110. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "An information diffusion-based model of oil futures price," Energy Economics, Elsevier, vol. 36(C), pages 518-525.
    111. Chen, Yong & Eaton, Gregory W. & Paye, Bradley S., 2018. "Micro(structure) before macro? The predictive power of aggregate illiquidity for stock returns and economic activity," Journal of Financial Economics, Elsevier, vol. 130(1), pages 48-73.
    112. Ali F. Darrat & Bin Li & Omar Benkato, 2011. "The Relationship between Volatility and Expected Returns: Some Evidence for Australia," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 10(1), pages 27-43, April.
    113. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    114. Hui Guo & Robert Savickas, 2006. "The relation between time-series and cross-sectional effects of idiosyncratic variance on stock returns in G7 countries," Working Papers 2006-036, Federal Reserve Bank of St. Louis.
    115. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
    116. Tim Bollerslev & Hao Zhou, 2003. "Volatility puzzles: a unified framework for gauging return-volatility regressions," Finance and Economics Discussion Series 2003-40, Board of Governors of the Federal Reserve System (U.S.).
    117. Xingchen Lv & Jun Meng & Qiufeng Wu, 2022. "Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    118. Cotter, John & Hanly, Jim, 2010. "Time-varying risk aversion: An application to energy hedging," Energy Economics, Elsevier, vol. 32(2), pages 432-441, March.
    119. Dave Berger & H. J. Turtle, 2009. "Time Variability In Market Risk Aversion," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 32(3), pages 285-307, September.
    120. Jan Schulz & Mishael Milaković, 2023. "How Wealthy are the Rich?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(1), pages 100-123, March.
    121. Söhnke M. Bartram & Gregory Brown & René M. Stulz, 2017. "Why Does Idiosyncratic Risk Increase with Market Risk?," CESifo Working Paper Series 6560, CESifo.
    122. Ma, Chaoqun & Mi, Xianhua & Cai, Zongwu, 2020. "Nonlinear and time-varying risk premia," China Economic Review, Elsevier, vol. 62(C).
    123. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205161, HAL.
    124. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    125. Andreou, Christoforos K. & Lambertides, Neophytos & Savvides, Andreas, 2020. "Sovereign credit risk and global equity fund returns in emerging markets," Journal of International Money and Finance, Elsevier, vol. 107(C).
    126. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    127. Kanas, Angelos, 2012. "Modelling the risk–return relation for the S&P 100: The role of VIX," Economic Modelling, Elsevier, vol. 29(3), pages 795-809.
    128. Rachidi Kotchoni, 2018. "Detecting and Measuring Nonlinearity," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    129. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.
    130. Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Intertemporal risk–return relationships in bull and bear markets," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 308-325.
    131. Hui Guo & Robert Savickas, 2006. "Aggregate idiosyncratic volatility in G7 countries," Working Papers 2004-027, Federal Reserve Bank of St. Louis.
    132. Lanne, Markku & Luoto, Jani, 2007. "Robustness of the Risk-Return Relationship in the U.S. Stock Market," MPRA Paper 3879, University Library of Munich, Germany.
    133. Xingchen Lv & Weijun Lin & Jun Meng & Linan Mo, 2024. "Spillover Effect of Network Public Opinion on Market Prices of Small-Scale Agricultural Products," Mathematics, MDPI, vol. 12(4), pages 1-17, February.
    134. Matthew Spiegel & Xiaotong Wang, 2005. "Cross-sectional Variation in Stock Returns: Liquidity and Idiosyncratic Risk," Yale School of Management Working Papers amz2540, Yale School of Management, revised 01 Mar 2006.
    135. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    136. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    137. Thuy Thi Thu Truong & Jungmu Kim, 2019. "Premiums for Non-Sustainable and Sustainable Components of Market Volatility: Evidence from the Korean Stock Market," Sustainability, MDPI, vol. 11(18), pages 1-15, September.
    138. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    139. Müller, Gernot & Durand, Robert B. & Maller, Ross A., 2011. "The risk-return tradeoff: A COGARCH analysis of Merton's hypothesis," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 306-320, March.
    140. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    141. Malamud, Semyon & Vilkov, Grigory, 2018. "Non-myopic betas," Journal of Financial Economics, Elsevier, vol. 129(2), pages 357-381.
    142. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    143. John Cotter & Jim Hanly, 2014. "Performance of Utility Based Hedges," Working Papers 201404, Geary Institute, University College Dublin.
    144. Maake, Tebogo & Bonga-Bonga, Lumengo, 2019. "The relationship between carry trade and asset markets in South Africa," MPRA Paper 96667, University Library of Munich, Germany.
    145. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    146. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    147. Jianjian Jin, 2013. "Jump-Diffusion Long-Run Risks Models, Variance Risk Premium and Volatility Dynamics," Staff Working Papers 13-12, Bank of Canada.
    148. Jeong‐Hoon Kim & Jungwoo Lee & Song‐Ping Zhu & Seok‐Hyon Yu, 2014. "A multiscale correction to the Black–Scholes formula," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 30(6), pages 753-765, November.
    149. Eric Girardin & Roselyne Joyeux, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Post-Print hal-01499615, HAL.
    150. Jennie Bai & Turan G. Bali & Quan Wen, 2019. "Is There a Risk-Return Tradeoff in the Corporate Bond Market? Time-Series and Cross-Sectional Evidence," NBER Working Papers 25995, National Bureau of Economic Research, Inc.
    151. Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2022. "The conditional impact of investor sentiment in global stock markets: A two-channel examination," Journal of Banking & Finance, Elsevier, vol. 138(C).
    152. Jia, Yun & Yang, Chunpeng, 2017. "Disagreement and the risk-return relation," Economic Modelling, Elsevier, vol. 64(C), pages 97-104.
    153. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    154. Ioannis Kasparis & Peter C.B. Phillips, 2009. "Dynamic Misspecification in Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1700, Cowles Foundation for Research in Economics, Yale University.
    155. Hui Guo & Christopher J. Neely, 2006. "Investigating the intertemporal risk-return relation in international stock markets with the component GARCH model," Working Papers 2006-006, Federal Reserve Bank of St. Louis.
    156. Bai, Jennie & Bali, Turan G. & Wen, Quan, 2021. "Is there a risk-return tradeoff in the corporate bond market? Time-series and cross-sectional evidence," Journal of Financial Economics, Elsevier, vol. 142(3), pages 1017-1037.
    157. Chari, Murali D.R. & David, Parthiban & Duru, Augustine & Zhao, Yijiang, 2019. "Bowman's risk-return paradox: An agency theory perspective," Journal of Business Research, Elsevier, vol. 95(C), pages 357-375.
    158. Kunst, Robert M. & Franses, Philip Hans, 2010. "Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data," Economics Series 252, Institute for Advanced Studies.
    159. Jiranyakul, Komain, 2011. "On the Risk-Return Tradeoff in the Stock Exchange of Thailand: New Evidence," MPRA Paper 45583, University Library of Munich, Germany.
    160. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    161. Thomas C. Chiang & Jiandong Li, 2012. "Stock Returns and Risk: Evidence from Quantile," JRFM, MDPI, vol. 5(1), pages 1-39, December.
    162. Ernest Gyapong & Daniel Gyimah & Ammad Ahmed, 2021. "Religiosity, borrower gender and loan losses in microfinance institutions: a global evidence," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 657-692, August.
    163. Benoît Sévi, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Post-Print hal-01500860, HAL.
    164. Esben Hedegaard & Robert J. Hodrick, 2014. "Estimating the Risk-Return Trade-off with Overlapping Data Inference," NBER Working Papers 19969, National Bureau of Economic Research, Inc.
    165. Byrne, Joseph P. & Sakemoto, Ryuta, 2021. "The conditional volatility premium on currency portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    166. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    167. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    168. Kanas, Angelos & Molyneux, Philip, 2020. "Do measures of systemic risk predict U.S. corporate bond default rates?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    169. Imma Valentina Curato & Simona Sanfelici, 2019. "Stochastic leverage effect in high-frequency data: a Fourier based analysis," Papers 1910.06660, arXiv.org, revised Mar 2021.
    170. Salamaliki, Paraskevi K. & Venetis, Ioannis A., 2013. "Energy consumption and real GDP in G-7: Multi-horizon causality testing in the presence of capital stock," Energy Economics, Elsevier, vol. 39(C), pages 108-121.
    171. Martin Ewen, 2018. "Where is the Risk Reward? The Impact of Volatility-Based Fund Classification on Performance," Risks, MDPI, vol. 6(3), pages 1-20, August.
    172. Fulvio Corsi & Roberto Renò, 2012. "Discrete-Time Volatility Forecasting With Persistent Leverage Effect and the Link With Continuous-Time Volatility Modeling," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 368-380, January.
    173. Joseph, Byrne & Sakemoto, Ryuta, 2020. "The Conditional Risk and Return Trade-Off on Currency Portfolios," MPRA Paper 99497, University Library of Munich, Germany.
    174. Hui Guo & Zijun Wang & Jian Yang, 2006. "Does aggregate relative risk aversion change countercyclically over time? evidence from the stock market," Working Papers 2006-047, Federal Reserve Bank of St. Louis.
    175. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    176. Wu, Jie & Zhao, Ruizeng & Sun, Jiasen & Zhou, Xuewei, 2023. "Impact of geopolitical risks on oil price fluctuations: Based on GARCH-MIDAS model," Resources Policy, Elsevier, vol. 85(PB).
    177. He, Zhifang, 2022. "Asymmetric impacts of individual investor sentiment on the time-varying risk-return relation in stock market," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 177-194.
    178. Hoerova, Marie & Bekaert, Geert, 2014. "The VIX, the variance premium and stock market volatility," Working Paper Series 1675, European Central Bank.
    179. Simlai, Prodosh, 2014. "Persistence of ex-ante volatility and the cross-section of stock returns," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 253-261.
    180. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    181. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    182. Christoffersen, Peter F. & Diebold, Francis X., 2003. "Financial asset returns, direction-of-change forecasting, and volatility dynamics," CFS Working Paper Series 2004/08, Center for Financial Studies (CFS).
    183. Lee Jihyun & Kim Tong S & Lee Hoe Kyung, 2010. "Return-Volatility Relationship in High Frequency Data: Multiscale Horizon Dependency," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(1), pages 1-43, December.
    184. Cathy Yi†Hsuan Chen & Thomas C. Chiang, 2016. "Empirical Analysis of the Intertemporal Relationship between Downside Risk and Expected Returns: Evidence from Time†varying Transition Probability Models," European Financial Management, European Financial Management Association, vol. 22(5), pages 749-796, November.
    185. Yin, Libo & Zhou, Yimin, 2016. "What drives long-term oil market volatility? Fundamentals versus Speculation," Economics Discussion Papers 2016-2, Kiel Institute for the World Economy (IfW Kiel).
    186. Cenedese, Gino & Sarno, Lucio & Tsiakas, Ilias, 2014. "Foreign exchange risk and the predictability of carry trade returns," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 302-313.
    187. Turan Bali & Kamil Yilmaz, 2009. "The Intertemporal Relation between Expected Return and Risk on Currency," Koç University-TUSIAD Economic Research Forum Working Papers 0909, Koc University-TUSIAD Economic Research Forum, revised Nov 2009.
    188. Jiang, Cuixia & Ding, Xiaoyi & Xu, Qifa & Tong, Yongbo, 2020. "A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    189. Liu, Xiaochun, 2017. "Can macroeconomic dynamics explain the time variation of risk–return trade-offs in the U.S. financial market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 275-293.
    190. Till Strohsal & Enzo Weber, 2014. "Mean-variance cointegration and the expectations hypothesis," Quantitative Finance, Taylor & Francis Journals, vol. 14(11), pages 1983-1997, November.
    191. Kim, Jun Sik & Ryu, Doojin & Seo, Sung Won, 2014. "Investor sentiment and return predictability of disagreement," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 166-178.
    192. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    193. Belén Nieto & Alfonso Novales Cinca & Gonzalo Rubio, 2014. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns," Documentos de Trabajo del ICAE 2014-25, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    194. Qiu, Rui & Liu, Jing & Li, Yan, 2023. "Long-term adjusted volatility: Powerful capability in forecasting stock market returns," International Review of Financial Analysis, Elsevier, vol. 86(C).
    195. Iacopini, Matteo & Poon, Aubrey & Rossini, Luca & Zhu, Dan, 2023. "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP," Journal of Economic Dynamics and Control, Elsevier, vol. 157(C).
    196. Pan, Beier, 2023. "The asymmetric dynamics of stock–bond liquidity correlation in China: The role of macro-financial determinants," Economic Modelling, Elsevier, vol. 124(C).
    197. Cotter, John & Hanly, Jim, 2012. "A utility based approach to energy hedging," Energy Economics, Elsevier, vol. 34(3), pages 817-827.
    198. Abdul Rashid & Saba Kausar, 2019. "Testing the Monthly Calendar Anomaly of Stock Returns in Pakistan: A Stochastic Dominance Approach," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 58(1), pages 83-104.
    199. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2016. "Risk-return trade-off for European stock markets," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 84-103.
    200. Jahan-Parvar, Mohammad R. & Mohammadi, Hassan, 2013. "Risk and return in the Tehran stock exchange," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(3), pages 238-256.
    201. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
    202. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    203. Jyri Kinnunen & Minna Martikainen, 2017. "Expected Returns and Idiosyncratic Risk: Industry-Level Evidence from Russia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2528-2544, November.
    204. Polzin, Friedemann & Egli, Florian & Steffen, Bjarne & Schmidt, Tobias S., 2019. "How do policies mobilize private finance for renewable energy?—A systematic review with an investor perspective," Applied Energy, Elsevier, vol. 236(C), pages 1249-1268.
    205. Jang, Jeewon & Kang, Jangkoo, 2017. "An intertemporal CAPM with higher-order moments," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 314-337.
    206. Pentti Saikkonen & Markku Lanne, 2004. "A Skewed GARCH-in-Mean Model: An Application to U.S. Stock Returns," Econometric Society 2004 North American Summer Meetings 469, Econometric Society.
    207. Lee, Kiryoung & Choi, Eunseon & Kim, Minki, 2023. "Twitter-based Chinese economic policy uncertainty," Finance Research Letters, Elsevier, vol. 53(C).
    208. Londono Yarce, J.M., 2011. "Essays on asset pricing," Other publications TiSEM 744a2ac5-7ada-4fa8-a7aa-e, Tilburg University, School of Economics and Management.
    209. Francisco Alonso & Roberto Blanco & Gonzalo Rubio, 2005. "Testing the forecasting performace of IBEX 35 option implied risk neutral densities," Working Papers 0504, Banco de España.
    210. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    211. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    212. Octavio Portolano Machado & Adriana Bruscato Bortoluzzo & Sérgio Ricardo Martins & Antonio Zoratto Sanvicente, 2013. "Inter-temporal CAPM: an empirical test with Brazilian market data," Brazilian Review of Finance, Brazilian Society of Finance, vol. 11(2), pages 149-180.
    213. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    214. Herold, Michael & Kanz, Andreas & Muck, Matthias, 2021. "Do opinion polls move stock prices? Evidence from the US presidential election in 2016," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 665-690.
    215. Shirley J. Huang & Qianqiu Liu & Jun Yu, 2007. "Realized Daily Variance of S&P 500 Cash Index: A Revaluation of Stylized Facts," Annals of Economics and Finance, Society for AEF, vol. 8(1), pages 33-56, May.
    216. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    217. Hui Guo & Robert Savickas & Zijun Wang & Jian Yang, 2006. "Is value premium a proxy for time-varying investment opportunities: some time series evidence," Working Papers 2005-026, Federal Reserve Bank of St. Louis.
    218. Kim, Eung-Bin & Byun, Suk-Joon, 2021. "Risk, ambiguity, and equity premium: International evidence," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 321-335.
    219. Wang, Zijun & Khan, M. Moosa, 2017. "Market states and the risk-return tradeoff," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 314-327.
    220. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    221. Seok Young Hong & Oliver Linton, 2016. "Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in finite order," CeMMAP working papers 53/16, Institute for Fiscal Studies.
    222. Kiseok Nam & Shahriar Khaksari & Moonsoo Kang, 2017. "Trend in aggregate idiosyncratic volatility," Review of Financial Economics, John Wiley & Sons, vol. 35(1), pages 11-28, November.
    223. Bhattacharya, Abhi & Misra, Shekhar & Sardashti, Hanieh, 2019. "Strategic orientation and firm risk," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 509-527.
    224. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    225. Tariq Aziz & Valeed Ahmad Ansari, 2017. "Idiosyncratic volatility and stock returns: Indian evidence," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1420998-142, January.
    226. Andreou, Elena & Kasparis, Ioannis & Phillips, Peter C. B., 2013. "Nonparametric Predictive Regression," CEPR Discussion Papers 9570, C.E.P.R. Discussion Papers.
    227. Ashby, M. & Linton, O. B., 2022. "Do Consumption-based Asset Pricing Models Explain Own-history Predictability in Stock Market Returns?," Janeway Institute Working Papers 2226, Faculty of Economics, University of Cambridge.
    228. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    229. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    230. Kanniainen, Juho & Piché, Robert, 2013. "Stock price dynamics and option valuations under volatility feedback effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 722-740.
    231. Tobias Adrian & Joshua V. Rosenberg, 2006. "Stock returns and volatility: pricing the short-run and long-run components of market risk," Staff Reports 254, Federal Reserve Bank of New York.
    232. Wang, Jianxin & Yang, Minxian, 2013. "On the risk return relationship," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 132-141.
    233. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    234. Miguel A. Ferreira & Pedro Santa-Clara, 2008. "Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole," NBER Working Papers 14571, National Bureau of Economic Research, Inc.
    235. Chang, Kuang-Liang, 2016. "Does the return-state-varying relationship between risk and return matter in modeling the time series process of stock return?," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 72-87.
    236. He, Zhongzhi (Lawrence) & Zhu, Jie & Zhu, Xiaoneng, 2015. "Multi-factor volatility and stock returns," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 132-149.
    237. Wang, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    238. Wang, Wenzhao, 2021. "The mean–variance relation: A 24-hour story," Economics Letters, Elsevier, vol. 208(C).
    239. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    240. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    241. Gilles de Truchis & Elena Ivona Dumitrescu, 2019. "Narrow-band Weighted Nonlinear Least Squares Estimation of Unbalanced Cointegration Systems," EconomiX Working Papers 2019-14, University of Paris Nanterre, EconomiX.
    242. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    243. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    244. Liu, Jingzhen, 2019. "Impacts of lagged returns on the risk-return relationship of Chinese aggregate stock market: Evidence from different data frequencies," Research in International Business and Finance, Elsevier, vol. 48(C), pages 243-257.
    245. Miralles-Marcelo, José Luis & Miralles-Quirós, María del Mar & Miralles-Quirós, José Luis, 2012. "Asset pricing with idiosyncratic risk: The Spanish case," International Review of Economics & Finance, Elsevier, vol. 21(1), pages 261-271.
    246. Anthony W. Lynch & Jessica A. Wachter, 2008. "Using Samples of Unequal Length in Generalized Method of Moments Estimation," NBER Working Papers 14411, National Bureau of Economic Research, Inc.
    247. Dimitrios Koutmos, 2015. "Is there a Positive Risk†Return Tradeoff? A Forward†Looking Approach to Measuring the Equity Premium," European Financial Management, European Financial Management Association, vol. 21(5), pages 974-1013, November.
    248. Hao Liu & Shihan Shen & Tianyi Wang & Zhuo Huang, 2016. "Revisiting the risk-return relation in the Chinese stock market: Decomposition of risk premium and volatility feedback effect," China Economic Journal, Taylor & Francis Journals, vol. 9(2), pages 140-153, May.
    249. Shanken, Jay & Tamayo, Ane, 2012. "Payout yield, risk, and mispricing: A Bayesian analysis," Journal of Financial Economics, Elsevier, vol. 105(1), pages 131-152.
    250. Yueh-Neng Lin & Ken Hung, 2008. "Is Volatility Priced?," Annals of Economics and Finance, Society for AEF, vol. 9(1), pages 39-75, May.
    251. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    252. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    253. Eric Jacquier & Cedric Okou, 2013. "Disentangling Continuous Volatility from Jumps in Long-Run Risk-Return Relationships," CIRANO Working Papers 2013s-14, CIRANO.
    254. Bollerslev, Tim & Zhou, Hao, 2006. "Volatility puzzles: a simple framework for gauging return-volatility regressions," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 123-150.
    255. Mili, Mehdi, 2019. "The impact of tradeoff between risk and return on mean reversion in sovereign CDS markets," Research in International Business and Finance, Elsevier, vol. 48(C), pages 187-200.
    256. Bandi, Federico M. & Perron, Benoît, 2008. "Long-run risk-return trade-offs," Journal of Econometrics, Elsevier, vol. 143(2), pages 349-374, April.
    257. Gregory Connor & Anita Suurlaht, 2012. "Dynamic Stock Market Covariances in the Eurozone," Economics Department Working Paper Series n222-12.pdf, Department of Economics, National University of Ireland - Maynooth.
    258. Hossein Asgharian & Charlotte Christiansen & Rangan Gupta & Ai Jun Hou, 2016. "Effects of Economic Policy Uncertainty Shocks on the Long-Run US-UK Stock Market Correlation," CREATES Research Papers 2016-29, Department of Economics and Business Economics, Aarhus University.
    259. Vít Pošta & Zdeněk Pikhart, 2015. "Financial Risk and Real Variables: Evidence Based on a SVAR Analysis of the Czech Economy," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(5), pages 516-537.
    260. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    261. Jiang, Xiaoquan & Lee, Bong-Soo, 2014. "The intertemporal risk-return relation: A bivariate model approach," Journal of Financial Markets, Elsevier, vol. 18(C), pages 158-181.
    262. Ung, Sze Nie & Gebka, Bartosz & Anderson, Robert D.J., 2023. "Is sentiment the solution to the risk–return puzzle? A (cautionary) note," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    263. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    264. Boguth, Oliver & Carlson, Murray & Fisher, Adlai & Simutin, Mikhail, 2011. "Conditional risk and performance evaluation: Volatility timing, overconditioning, and new estimates of momentum alphas," Journal of Financial Economics, Elsevier, vol. 102(2), pages 363-389.
    265. Gerrit Reher & Bernd Wilfling, 2016. "A nesting framework for Markov-switching GARCH modelling with an application to the German stock market," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 411-426, March.
    266. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    267. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2007. "Valuation in US Commercial Real Estate," European Financial Management, European Financial Management Association, vol. 13(3), pages 472-497, June.
    268. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    269. Choe, Kwang-il & Choi, Pilsun & Nam, Kiseok & Vahid, Farshid, 2012. "Testing financial contagion on heteroskedastic asset returns in time-varying conditional correlation," Pacific-Basin Finance Journal, Elsevier, vol. 20(2), pages 271-291.
    270. Koutmos, Gregory & Knif, Johan & Philippatos, George C., 2008. "Modeling common volatility characteristics and dynamic risk premia in European equity markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(3), pages 567-578, August.
    271. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    272. Wang, Wenzhao, 2018. "Investor sentiment and the mean-variance relationship: European evidence," Research in International Business and Finance, Elsevier, vol. 46(C), pages 227-239.
    273. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun & Wang, Weining, 2021. "Long- and short-run components of factor betas: Implications for stock pricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    274. Neaime, Simon, 2012. "The global financial crisis, financial linkages and correlations in returns and volatilities in emerging MENA stock markets," Emerging Markets Review, Elsevier, vol. 13(3), pages 268-282.
    275. Manh Cuong Nguyen & Viet Anh Dang & Tri Tri Nguyen, 2023. "The transfer of risk taking along the supply chain," Review of Quantitative Finance and Accounting, Springer, vol. 61(4), pages 1341-1378, November.
    276. Jin, Xing & Wang, Leping & Yu, Jun, 2007. "Temporal aggregation and risk-return relation," Finance Research Letters, Elsevier, vol. 4(2), pages 104-115, June.
    277. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    278. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2018. "Downside risk and stock returns in the G7 countries: An empirical analysis of their long-run and short-run dynamics," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 21-32.
    279. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    280. Jiang, Cuixia & Li, Yuqian & Xu, Qifa & Liu, Yezheng, 2021. "Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 386-398.
    281. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    282. Guo, Hui & Qiu, Buhui, 2014. "Options-implied variance and future stock returns," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 93-113.
    283. Jonathan Readshaw & Stefano Giani, 2020. "Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations," Papers 2006.12426, arXiv.org.
    284. Til Schuermann & Kevin J. Stiroh, 2006. "Visible and hidden risk factors for banks," Staff Reports 252, Federal Reserve Bank of New York.
    285. Hui Guo & Robert Savickas, 2006. "Idiosyncratic volatility, economic fundamentals, and foreign exchange rates," Working Papers 2005-025, Federal Reserve Bank of St. Louis.
    286. Wang, Wenzhao, 2018. "The mean–variance relation and the role of institutional investor sentiment," Economics Letters, Elsevier, vol. 168(C), pages 61-64.
    287. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    288. Benoît Sévi & César Baena, 2013. "The explanatory power of signed jumps for the risk-return tradeoff," Economics Bulletin, AccessEcon, vol. 33(2), pages 1029-1046.
    289. Ederington, Louis H. & Guan, Wei, 2010. "How asymmetric is U.S. stock market volatility?," Journal of Financial Markets, Elsevier, vol. 13(2), pages 225-248, May.
    290. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    291. Brennan, M.J. & Taylor, Alex P., 2023. "Expected returns and risk in the stock market," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 276-300.
    292. DasGupta, Ranjan & Deb, Soumya G., 2022. "Role of corporate governance in moderating the risk-return paradox: Cross country evidence," Journal of Contemporary Accounting and Economics, Elsevier, vol. 18(2).
    293. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    294. Aghamolla, Cyrus & An, Byeong-Je, 2021. "Voluntary disclosure with evolving news," Journal of Financial Economics, Elsevier, vol. 140(1), pages 21-53.
    295. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    296. Semih Emre Çekin & Victor J. Valcarcel, 2020. "Inflation volatility and inflation in the wake of the great recession," Empirical Economics, Springer, vol. 59(4), pages 1997-2015, October.
    297. Nyberg, Henri, 2010. "QR-GARCH-M Model for Risk-Return Tradeoff in U.S. Stock Returns and Business Cycles," MPRA Paper 23724, University Library of Munich, Germany.
    298. Apergis, Nicholas, 2015. "Newswire messages and sovereign credit ratings: Evidence from European countries under austerity reform programmes," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 54-62.
    299. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    300. Chen Xilong & Ghysels Eric & Wang Fangfang, 2011. "HYBRID GARCH Models and Intra-Daily Return Periodicity," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-28, February.
    301. Jiawen Xu & Yixuan Li & Kai Liu & Tao Chen, 2023. "Portfolio selection: from under-diversification to concentration," Empirical Economics, Springer, vol. 64(4), pages 1539-1557, April.
    302. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    303. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    304. Matthias M. M. Buehlmaier & Kit Pong Wong, 2020. "Should investors join the index revolution? Evidence from around the world," Journal of Asset Management, Palgrave Macmillan, vol. 21(3), pages 192-218, May.
    305. Dotsis, George, 2017. "The market price of risk of the variance term structure," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 41-52.
    306. Gong, Xu & Sun, Yi & Du, Zhili, 2022. "Geopolitical risk and China's oil security," Energy Policy, Elsevier, vol. 163(C).
    307. Hui Guo & Robert Savickas, 2006. "Understanding stock return predictability," Working Papers 2006-019, Federal Reserve Bank of St. Louis.
    308. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    309. Barroso, Pedro & Santa-Clara, Pedro, 2015. "Momentum has its moments," Journal of Financial Economics, Elsevier, vol. 116(1), pages 111-120.
    310. Benoît Sévi & César Baena, 2012. "A reassessment of the risk-return tradeoff at the daily horizon," Economics Bulletin, AccessEcon, vol. 32(1), pages 190-203.
    311. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
    312. Chen, Xiaoyu & Chiang, Thomas C., 2016. "Stock returns and economic forces—An empirical investigation of Chinese markets," Global Finance Journal, Elsevier, vol. 30(C), pages 45-65.
    313. Philippe Masset & Martin Wallmeier, 2010. "A High†Frequency Investigation of the Interaction between Volatility and DAX Returns," European Financial Management, European Financial Management Association, vol. 16(3), pages 327-344, June.
    314. Umutlu, Mehmet, 2019. "Does idiosyncratic volatility matter at the global level?," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 252-268.
    315. Corradi, Valentina & Distaso, Walter & Fernandes, Marcelo, 2013. "Conditional alphas and realized betas," Textos para discussão 341, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    316. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
    317. Masud Alam, 2021. "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return," Papers 2107.10455, arXiv.org.
    318. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    319. Chatrath, Arjun & Miao, Hong & Ramchander, Sanjay & Wang, Tianyang, 2016. "An examination of the flow characteristics of crude oil: Evidence from risk-neutral moments," Energy Economics, Elsevier, vol. 54(C), pages 213-223.
    320. Hiroyuki Kawakatsu, 2022. "Modeling Realized Variance with Realized Quarticity," Stats, MDPI, vol. 5(3), pages 1-25, September.
    321. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).
    322. Gilles de Truchis & Elena Ivona Dumitrescu, 2019. "Narrow-band Weighted Nonlinear Least Squares Estimation of Unbalanced Cointegration Systems," Working Papers hal-04141871, HAL.
    323. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2019. "Volatility-dependent correlations: further evidence of when, where and how," Empirical Economics, Springer, vol. 57(2), pages 505-540, August.
    324. Roi D. Taussig, 2017. "Stickiness of employee expenses and implications for stock returns," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 7(2), pages 297-309, August.
    325. Christian Brownlees & Benjamin Chabot & Eric Ghysels & Christopher J. Kurz, 2015. "Backtesting Systemic Risk Measures During Historical Bank Runs," Working Paper Series WP-2015-9, Federal Reserve Bank of Chicago.
    326. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    327. Andreou, Elena, 2016. "On the use of high frequency measures of volatility in MIDAS regressions," Journal of Econometrics, Elsevier, vol. 193(2), pages 367-389.
    328. Alonso, Francisco & Blanco, Roberto & Rubio Irigoyen, Gonzalo, 2005. "Option-Implied Preferences Adjustments and Risk-Neutral Density Forecasts," DFAEII Working Papers 1988-088X, University of the Basque Country - Department of Foundations of Economic Analysis II.
    329. David P. Brown & Miguel A. Ferreira, 2016. "Idiosyncratic Volatility of Small Public Firms and Entrepreneurial Risk," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-59, March.
    330. Lanter, David & Hirsch, Stefan & Finger, Robert, 2018. "Profitability and Competition in EU Food Retailing," 2018 Annual Meeting, August 5-7, Washington, D.C. 274202, Agricultural and Applied Economics Association.
    331. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    332. Choi, Jaewon & Richardson, Matthew, 2016. "The volatility of a firm's assets and the leverage effect," Journal of Financial Economics, Elsevier, vol. 121(2), pages 254-277.
    333. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.
    334. Travis L Johnson, 2019. "A Fresh Look at Return Predictability Using a More Efficient Estimator," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 9(1), pages 1-46.
    335. Bansal, Naresh & Stivers, Chris, 2022. "Bond risk’s role in the equity risk-return tradeoff," Journal of Financial Markets, Elsevier, vol. 60(C).
    336. Ryan T. Ball & Jonathan Bonham & Thomas Hemmer, 2020. "Does it pay to ‘Be Like Mike’? Aspiratonal peer firms and relative performance evaluation," Review of Accounting Studies, Springer, vol. 25(4), pages 1507-1541, December.
    337. Huang, Lin & Wang, Zijun, 2014. "Is the investment factor a proxy for time-varying investment opportunities? The US and international evidence," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 219-232.
    338. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    339. Jianjian Jin, 2015. "Jump-Diffusion Long-Run Risks Models, Variance Risk Premium, and Volatility Dynamics," Review of Finance, European Finance Association, vol. 19(3), pages 1223-1279.
    340. Liu, Dehong & Gu, Hongmei & Lung, Peter, 2016. "The equity mispricing: Evidence from China's stock market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 211-223.
    341. Pierpaolo Andriani & Bill McKelvey, 2009. "Perspective ---From Gaussian to Paretian Thinking: Causes and Implications of Power Laws in Organizations," Organization Science, INFORMS, vol. 20(6), pages 1053-1071, December.
    342. Kinnunen, Jyri, 2013. "Dynamic return predictability in the Russian stock market," Emerging Markets Review, Elsevier, vol. 15(C), pages 107-121.
    343. Reschenhofer, Erhard & Mangat, Manveer Kaur & Stark, Thomas, 2020. "Volatility forecasts, proxies and loss functions," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 133-153.
    344. Dirk Swagerman & Ivan Novakovic, 2010. "Multi-National Evidence On Calendar Patterns In Stock Returns: An Empirical Case Study On Investment Strategy And The Halloween Effect," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(4), pages 23-42.
    345. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    346. Tseng, Tseng-Chan & Lee, Chien-Chiang & Chen, Mei-Ping, 2015. "Volatility forecast of country ETF: The sequential information arrival hypothesis," Economic Modelling, Elsevier, vol. 47(C), pages 228-234.
    347. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R. & Rothman, Philip, 2010. "An empirical investigation of stock market behavior in the Middle East and North Africa," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 413-427, June.
    348. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    349. Mathijs Cosemans & Rik Frehen & Peter C. Schotman & Rob Bauer, 2016. "Estimating Security Betas Using Prior Information Based on Firm Fundamentals," The Review of Financial Studies, Society for Financial Studies, vol. 29(4), pages 1072-1112.
    350. Yang, Chunpeng & Jia, Yun, 2016. "Buy-sell imbalance and the mean-variance relation," Pacific-Basin Finance Journal, Elsevier, vol. 40(PA), pages 49-58.
    351. Cathy Yi-Hsuan Chen & Thomas C. Chiang & Wolfgang Karl Härdle, 2016. "Downside risk and stock returns: An empirical analysis of the long-run and short-run dynamics from the G-7 Countries," SFB 649 Discussion Papers SFB649DP2016-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    352. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    353. Ryuta Sakemoto, 2018. "The intertemporal relation between expected returns and conditional correlations between precious metals and the stock market," Economics and Business Letters, Oviedo University Press, vol. 7(1), pages 24-35.
    354. Yao, Can-Zhong & Li, Min-Jian, 2023. "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    355. Huang, Teng-Ching & Wu, Ching-Chih & Lin, Bing-Huei, 2016. "Institutional herding and risk–return relationship," Journal of Business Research, Elsevier, vol. 69(6), pages 2073-2080.
    356. Hatemi-J, Abdulnasser & Irandoust, Manuchehr, 2011. "The dynamic interaction between volatility and returns in the US stock market using leveraged bootstrap simulations," Research in International Business and Finance, Elsevier, vol. 25(3), pages 329-334, September.
    357. Rafique, Amir & Iqbal, Khurram & Zakaria, Muhammad & Mujtaba, Ghulam, 2019. "Investigating ICAPM with mean-reverting dynamic conditional correlation: Evidence from an emerging stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 514-523.
    358. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yuan, Jing, 2018. "Measuring systemic risk of the banking industry in China: A DCC-MIDAS-t approach," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 13-31.
    359. Bent Jesper Christensen & Morten Ørregaard Nielsen, 2007. "The Effect of Long Memory in Volatility on Stock Market Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 684-700, November.
    360. Pedro Piccoli & Newton C. A. da Costa & Wesley Vieira da Silva & June A. W. Cruz, 2018. "Investor sentiment and the risk–return tradeoff in the Brazilian market," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 599-618, November.
    361. Long Chen & Hui Guo & Lu Zhang, 2006. "Equity market volatility and expected risk premium," Working Papers 2006-007, Federal Reserve Bank of St. Louis.
    362. Michael D. Boldin & Jonathan H. Wright, 2015. "Weather-adjusting employment data," Working Papers 15-5, Federal Reserve Bank of Philadelphia.
    363. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    364. Brenner, Menachem & Izhakian, Yehuda, 2018. "Asset pricing and ambiguity: Empirical evidence⁎," Journal of Financial Economics, Elsevier, vol. 130(3), pages 503-531.
    365. Farooq Malik, 2015. "Revisiting the relationship between risk and return," Review of Quantitative Finance and Accounting, Springer, vol. 44(1), pages 25-40, January.
    366. Tan, Zhengxun & Xiao, Binuo & Huang, Yilong & Zhou, Li, 2021. "Value at risk and return in Chinese and the US stock markets: Double long memory and fractional cointegration," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    367. Bali, Turan G. & Engle, Robert F., 2010. "The intertemporal capital asset pricing model with dynamic conditional correlations," Journal of Monetary Economics, Elsevier, vol. 57(4), pages 377-390, May.
    368. Chiang, Thomas C. & Li, Huimin & Zheng, Dazhi, 2015. "The intertemporal risk-return relationship: Evidence from international markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 156-180.
    369. Francisco Alonso & Roberto Blanco & Gonzalo Rubio, 2006. "Option-implied preferences adjustments, density forecasts, and the equity risk premium," Working Papers 0630, Banco de España.
    370. Minxian Yang, 2014. "The Risk Return Relationship: Evidence from Index Return and Realised Variance Series," Discussion Papers 2014-16, School of Economics, The University of New South Wales.
    371. Symitsi, Efthymia & Symeonidis, Lazaros & Kourtis, Apostolos & Markellos, Raphael, 2018. "Covariance forecasting in equity markets," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 153-168.
    372. Cenesizoglu, Tolga, 2022. "Return decomposition over the business cycle," Journal of Banking & Finance, Elsevier, vol. 143(C).
    373. Chevapatrakul, Thanaset, 2013. "Return sign forecasts based on conditional risk: Evidence from the UK stock market index," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2342-2353.
    374. Curato, Imma Valentina & Sanfelici, Simona, 2022. "Stochastic leverage effect in high-frequency data: a Fourier based analysis," Econometrics and Statistics, Elsevier, vol. 23(C), pages 53-82.
    375. Vozlyublennaia, Nadia, 2013. "Do firm characteristics matter for the dynamics of idiosyncratic risk?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 35-46.
    376. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    377. Durham, Garland B., 2007. "SV mixture models with application to S&P 500 index returns," Journal of Financial Economics, Elsevier, vol. 85(3), pages 822-856, September.
    378. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
    379. Michael William Ashby & Oliver Bruce Linton, 2024. "Do Consumption-Based Asset Pricing Models Explain the Dynamics of Stock Market Returns?," JRFM, MDPI, vol. 17(2), pages 1-42, February.
    380. Yang, Minxian, 2019. "The risk return relationship: Evidence from index returns and realised variances," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.

  3. Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2004. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns," NBER Working Papers 10996, National Bureau of Economic Research, Inc.

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    2. Miguel Antón & Christopher Polk, 2014. "Connected Stocks," Journal of Finance, American Finance Association, vol. 69(3), pages 1099-1127, June.
    3. Balbás, Alejandro & Laborda Herrero, Ricardo, 2017. "Interest Rate Future Quality Options and Negative Interest Rates," INDEM - Working Paper Business Economic Series 24859, Instituto para el Desarrollo Empresarial (INDEM).
    4. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    5. Laborda, Ricardo & Laborda, Juan, 2017. "Can tree-structured classifiers add value to the investor?," Finance Research Letters, Elsevier, vol. 22(C), pages 211-226.
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    7. Schäfer, Larissa, 2015. "Essays in banking and international finance," Other publications TiSEM 54db9c22-05fa-4444-97d5-1, Tilburg University, School of Economics and Management.
    8. Mohammed Bouaddi & Abderrahim Taamouti, 2012. "Portfolio risk management in a data-rich environment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(4), pages 469-494, December.
    9. Ricardo Laborda & Jose Olmo, 2020. "Optimal portfolio choices using financial leverage," Bulletin of Economic Research, Wiley Blackwell, vol. 72(2), pages 146-166, April.
    10. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2017. "On the gains of using high frequency data and higher moments in Portfolio Selection," CeBER Working Papers 2017-02, Centre for Business and Economics Research (CeBER), University of Coimbra.
    11. Peter Christoffersen & Xuhui (Nick) Pan, 2014. "Equity Portfolio Management Using Option Price Information," CREATES Research Papers 2015-05, Department of Economics and Business Economics, Aarhus University.
    12. Ralph S. J. Koijen & Motohiro Yogo, 2015. "A Demand System Approach to Asset Pricing," Staff Report 510, Federal Reserve Bank of Minneapolis.
    13. Aït-Sahalia, Yacine & Xiu, Dacheng, 2017. "Using principal component analysis to estimate a high dimensional factor model with high-frequency data," Journal of Econometrics, Elsevier, vol. 201(2), pages 384-399.
    14. Pedro Barroso & Jurij-Andrei Reichenecker & Marco J. Menichetti, 2022. "Hedging with an Edge: Parametric Currency Overlay," Management Science, INFORMS, vol. 68(1), pages 669-689, January.
    15. Ricardo Laborda & Ramiro Losada, 2017. "Why is investors'mutual fund market allocation far from the optimum?," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
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    17. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," PIER Working Paper Archive 05-007, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    18. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2016. "Efficient skewness/semivariance portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 331-346, September.
    19. Sait Tunc & Mehmet A. Donmez & Suleyman S. Kozat, 2012. "Optimal Investment Under Transaction Costs," Papers 1203.4153, arXiv.org, revised Jul 2012.
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    21. Fan, Jianqing & Liao, Yuan & Shi, Xiaofeng, 2013. "Risks of large portfolios," MPRA Paper 44206, University Library of Munich, Germany.
    22. Tim A. Kroencke & Felix Schindler & Andreas Schrimpf, 2011. "International Diversification Benefits with Foreign Exchange Investment Styles," CREATES Research Papers 2011-10, Department of Economics and Business Economics, Aarhus University.
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    24. Hossein Rad & Rand Kwong Yew Low & Joelle Miffre & Robert Faff, 2022. "The Strategic Allocation to Style-Integrated Portfolios of Commodity Futures," Post-Print hal-03881976, HAL.
    25. Fuertes, Ana-Maria & Zhao, Nan, 2023. "A Bayesian perspective on commodity style integration," Journal of Commodity Markets, Elsevier, vol. 30(C).
    26. Ammann, Manuel & Coqueret, Guillaume & Schade, Jan-Philip, 2016. "Characteristics-based portfolio choice with leverage constraints," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 23-37.
    27. Constantinos Kardaras & Hyeng Keun Koo & Johannes Ruf, 2022. "Estimation of growth in fund models," Papers 2208.02573, arXiv.org.
    28. Yunus Emre Ergemen & Abderrahim Taamouti, 2015. "Parametric Portfolio Policies with Common Volatility Dynamics," CREATES Research Papers 2015-41, Department of Economics and Business Economics, Aarhus University.
    29. Fays, Boris & Papageorgiou, Nicolas & Lambert, Marie, 2021. "Risk optimizations on basis portfolios: The role of sorting," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 136-163.
    30. Hong, Harrison & Xu, Jiangmin, 2019. "Inferring latent social networks from stock holdings," Journal of Financial Economics, Elsevier, vol. 131(2), pages 323-344.
    31. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova, 2018. "Exchange rate forecasting and the performance of currency portfolios," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 519-540, August.
    32. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Miffre, Joelle, 2021. "The risk premia of energy futures," Energy Economics, Elsevier, vol. 102(C).
    33. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    34. Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
    35. Juarez-Torres, Miriam & Sanchez, Leonardo & Vedenov, Dmitry V., 2012. "Effectiveness of Weather Derivatives as Cross-Hedging Instrument against Climate Change: The Cases of Reservoir Water Allocation Management in Guanajuato, Mexico," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124813, Agricultural and Applied Economics Association.
    36. Fuertes, Ana-Maria & Zhao, Nan, 2022. "A Bayesian Perspective on Commodity Style Integration," MPRA Paper 117831, University Library of Munich, Germany, revised 2023.
    37. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2016. "Portfolio Choice with High Frequency Data: CRRA Preferences and the Liquidity Effect," GEMF Working Papers 2016-13, GEMF, Faculty of Economics, University of Coimbra.
    38. Adam Farago & Erik Hjalmarsson, 2023. "Small Rebalanced Portfolios Often Beat the Market over Long Horizons," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 13(2), pages 307-342.
    39. Ni, Xuanming & Zheng, Tiantian & Zhao, Huimin & Zhu, Shushang, 2023. "High-dimensional portfolio optimization based on tree-structured factor model," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    40. Meucci, A. & Nicolosi, M., 2016. "Dynamic portfolio management with views at multiple horizons," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 495-518.
    41. Xia, Hui & Min, Xinyu & Deng, Shijie, 2015. "Effectiveness of earnings forecasts in efficient global portfolio construction," International Journal of Forecasting, Elsevier, vol. 31(2), pages 568-574.
    42. Chi-Lin Yang & Jung-Ho Lai, 2021. "Influence of Cross-Listing on the Relationship between Financial Leverage and R&D Investment: A Sustainable Development Strategy," Sustainability, MDPI, vol. 13(18), pages 1-14, September.
    43. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Efficient Skewness/Semivariance Portfolios," GEMF Working Papers 2015-05, GEMF, Faculty of Economics, University of Coimbra.
    44. Allen, David & Lizieri, Colin & Satchell, Stephen, 2020. "A comparison of non-Gaussian VaR estimation and portfolio construction techniques," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 356-368.
    45. Tony Guida & Guillaume Coqueret, 2019. "Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework," Post-Print hal-02311104, HAL.
    46. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    47. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Annals of Operations Research, Springer, vol. 288(1), pages 181-221, May.
    48. Gonzalo, Jesús & Olmo, José, 2016. "Long-term optimal portfolio allocation under dynamic horizon-specific risk aversion," UC3M Working papers. Economics 23599, Universidad Carlos III de Madrid. Departamento de Economía.
    49. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    50. Nikan Firoozye & Vincent Tan & Stefan Zohren, 2022. "Canonical Portfolios: Optimal Asset and Signal Combination," Papers 2202.10817, arXiv.org, revised Jul 2023.
    51. Fabrizio Cipollini & Giampiero M. Gallo & Alessandro Palandri, 2020. "A dynamic conditional approach to portfolio weights forecasting," Papers 2004.12400, arXiv.org.
    52. Kazuhiro Hiraki & George Skiadopoulos, 2023. "The Contribution of Transaction Costs to Expected Stock Returns: A Novel Measure," Working Papers 946, Queen Mary University of London, School of Economics and Finance.
    53. Marc S. Paolella, 2017. "The Univariate Collapsing Method for Portfolio Optimization," Econometrics, MDPI, vol. 5(2), pages 1-33, May.
    54. Wang, Christina Dan & Chen, Zhao & Lian, Yimin & Chen, Min, 2022. "Asset selection based on high frequency Sharpe ratio," Journal of Econometrics, Elsevier, vol. 227(1), pages 168-188.
    55. Hjalmarsson, Erik & Manchev, Petar, 2012. "Characteristic-based mean-variance portfolio choice," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1392-1401.
    56. Kozak, Serhiy & Nagel, Stefan & Santosh, Shrihari, 2020. "Shrinking the cross-section," Journal of Financial Economics, Elsevier, vol. 135(2), pages 271-292.
    57. Uppal, Raman & DeMiguel, Victor & Plyakha, Yuliya & Vilkov, Grigory, 2010. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," CEPR Discussion Papers 7686, C.E.P.R. Discussion Papers.
    58. Christopher G. Lamoureux & Huacheng Zhang, 2021. "An Empirical Assessment of Characteristics and Optimal Portfolios," Papers 2104.12975, arXiv.org, revised Feb 2024.
    59. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
    60. Laborda, Ricardo & Muñoz, Fernando, 2016. "Optimal allocation of government bond funds through the business cycle. Is money smart?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 46-67.
    61. Michael Curran & Patrick O'Sullivan & Ryan Zalla, 2020. "Can Volatility Solve the Naive Portfolio Puzzle?," Papers 2005.03204, arXiv.org, revised Feb 2022.
    62. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    63. Zaremba, Adam & Andreu, Laura, 2018. "Paper profits or real money? Trading costs and stock market anomalies in country ETFs," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 181-192.
    64. Sanne de Boer, 2010. "Factor tilting for expected utility maximization," Journal of Asset Management, Palgrave Macmillan, vol. 11(1), pages 31-42, April.
    65. Füss, Roland & Miebs, Felix & Trübenbach, Fabian, 2014. "A jackknife-type estimator for portfolio revision," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 14-28.
    66. Moura, Guilherme V. & Santos, André A. P. & Ruiz Ortega, Esther, 2019. "Comparing Forecasts of Extremely Large Conditional Covariance Matrices," DES - Working Papers. Statistics and Econometrics. WS 29291, Universidad Carlos III de Madrid. Departamento de Estadística.
    67. Marcelo C. Medeiros & Artur M. Passos & Gabriel F. R. Vasconcelos, 2014. "Parametric Portfolio Selection: Evaluating and Comparing to Markowitz Portfolios," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(2), pages 257-284.
    68. Schüssler, Rainer & Beckmann, Joscha & Koop, Gary & Korobilis, Dimitris, 2018. "Exchange rate predictability and dynamic Bayesian learning," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181523, Verein für Socialpolitik / German Economic Association.
    69. Lassance, Nathan & Vrins, Frédéric, 2021. "Portfolio selection with parsimonious higher comoments estimation," LIDAM Reprints LFIN 2021005, Université catholique de Louvain, Louvain Finance (LFIN).
    70. Joachim Inkmann & Zhen Shi, 2015. "Parametric Portfolio Policies in the Surplus Consumption Ratio," International Review of Finance, International Review of Finance Ltd., vol. 15(2), pages 257-282, June.
    71. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    72. Behr, Patrick & Guettler, Andre & Truebenbach, Fabian, 2012. "Using industry momentum to improve portfolio performance," Journal of Banking & Finance, Elsevier, vol. 36(5), pages 1414-1423.
    73. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    74. De Santis, Roberto A. & Lührmann, Melanie, 2009. "On the determinants of net international portfolio flows: A global perspective," Journal of International Money and Finance, Elsevier, vol. 28(5), pages 880-901, September.
    75. Lassance, Nathan & Vrins, Frédéric, 2019. "Robust portfolio selection using sparse estimation of comoment tensors," LIDAM Discussion Papers LFIN 2019007, Université catholique de Louvain, Louvain Finance (LFIN).
    76. Daniel Giamouridis & Athanasios Sakkas & Nikolaos Tessaromatis, 2017. "Dynamic Asset Allocation with Liabilities," European Financial Management, European Financial Management Association, vol. 23(2), pages 254-291, March.
    77. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    78. Beber, Alessandro & Brandt, Michael W. & Cen, Jason & Kavajecz, Kenneth A., 2021. "Mutual fund performance: Using bespoke benchmarks to disentangle mandates, constraints and skill," Journal of Empirical Finance, Elsevier, vol. 60(C), pages 74-93.
    79. Choi, Jin Ho & Suh, Sangwon, 2021. "A filtered currency carry trade," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    80. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    81. Takano, Yuichi & Gotoh, Jun-ya, 2023. "Dynamic portfolio selection with linear control policies for coherent risk minimization," Operations Research Perspectives, Elsevier, vol. 10(C).
    82. Rui Pedro Brito & Hélder Sebastião & Pedro Godinho, 2015. "Portfolio Management With Higher Moments: The Cardinality Impact," GEMF Working Papers 2015-15, GEMF, Faculty of Economics, University of Coimbra.
    83. Xavier Gerard & Ron Guido & Peter Wesselius, 2013. "Integrated alpha modelling," Journal of Asset Management, Palgrave Macmillan, vol. 14(3), pages 140-161, June.
    84. Laborda, Juan & Laborda, Ricardo & Olmo, Jose, 2014. "Optimal currency carry trade strategies," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 52-66.
    85. Fischer, Marcel & Gallmeyer, Michael F., 2016. "Heuristic portfolio trading rules with capital gain taxes," Journal of Financial Economics, Elsevier, vol. 119(3), pages 611-625.
    86. Ruchika Sehgal & Aparna Mehra, 2023. "Quantile Regression Based Enhanced Indexing with Portfolio Rebalancing," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 721-742, September.
    87. Johannes Bock, 2018. "An updated review of (sub-)optimal diversification models," Papers 1811.08255, arXiv.org.
    88. Moorman, Theodore, 2014. "An empirical investigation of methods to reduce transaction costs," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 230-246.
    89. Trung H. Le & Apostolos Kourtis & Raphael Markellos, 2023. "Modeling skewness in portfolio choice," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(6), pages 734-770, June.
    90. Gehrig, Thomas & Sögner, Leopold & Westerkamp, Arne, 2018. "Making Parametric Portfolio Policies Work," CEPR Discussion Papers 13193, C.E.P.R. Discussion Papers.
    91. Vilkovz, Grigory & Xiaox, Yan, 2013. "Option-implied information and predictability of extreme returns," SAFE Working Paper Series 5, Leibniz Institute for Financial Research SAFE.
    92. Connor, G. & Li, S. & Linton, O., 2020. "A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection," Cambridge Working Papers in Economics 20103, Faculty of Economics, University of Cambridge.
    93. N'Golo Kone, 2021. "Efficient mean-variance portfolio selection by double regularization," Working Paper 1453, Economics Department, Queen's University.
    94. De Santis, Roberto A. & Lührmann, Melanie, 2006. "On the determinants of external imbalances and net international portfolio flows: a global perspective," Working Paper Series 651, European Central Bank.
    95. Joenväärä, Juha & Kauppila, Mikko & Kahra, Hannu, 2021. "Hedge fund portfolio selection with fund characteristics," Journal of Banking & Finance, Elsevier, vol. 132(C).
    96. Golosnoy, Vasyl & Gribisch, Bastian, 2022. "Modeling and forecasting realized portfolio weights," Journal of Banking & Finance, Elsevier, vol. 138(C).
    97. Carter Davis, 2023. "The Elasticity of Quantitative Investment," Papers 2303.14533, arXiv.org.
    98. Valentin Haddad & Serhiy Kozak & Shrihari Santosh & Stijn Van Nieuwerburgh, 2020. "Factor Timing," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 1980-2018.
    99. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.
    100. Demetrescu, Matei & Golosnoy, Vasyl & Titova, Anna, 2020. "Bias corrections for exponentially transformed forecasts: Are they worth the effort?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 761-780.
    101. Olivier Ledoit & Michael Wolf, 2014. "Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks," ECON - Working Papers 137, Department of Economics - University of Zurich, revised Feb 2017.
    102. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    103. Cosemans, M. & Frehen, R.G.P. & Schotman, P.C. & Bauer, R.M.M.J., 2009. "Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice," MPRA Paper 23557, University Library of Munich, Germany.
    104. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.
    105. Le, Trung H., 2021. "International portfolio allocation: The role of conditional higher moments," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 33-57.
    106. Laborda, Ricardo, 2018. "Optimal combination of currency strategies," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 129-140.
    107. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    108. Auh, Jun Kyung & Cho, Wonho, 2023. "Factor-based portfolio optimization," Economics Letters, Elsevier, vol. 228(C).
    109. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Post-Print hal-04144665, HAL.
    110. Barroso, Pedro & Detzel, Andrew, 2021. "Do limits to arbitrage explain the benefits of volatility-managed portfolios?," Journal of Financial Economics, Elsevier, vol. 140(3), pages 744-767.
    111. Han, Chulwoo, 2020. "A nonparametric approach to portfolio shrinkage," Journal of Banking & Finance, Elsevier, vol. 120(C).
    112. Vasyl Golosnoy & Benno Hildebrandt & Steffen Köhler, 2019. "Modeling and Forecasting Realized Portfolio Diversification Benefits," JRFM, MDPI, vol. 12(3), pages 1-16, July.
    113. Li, Danyang & Zhang, Zhekai & Cerrato, Mario, 2023. "Factor investing and currency portfolio management," International Review of Financial Analysis, Elsevier, vol. 87(C).
    114. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    115. Jun Tu, 2010. "Is Regime Switching in Stock Returns Important in Portfolio Decisions?," Management Science, INFORMS, vol. 56(7), pages 1198-1215, July.
    116. Branikas, Ioannis & Hong, Harrison & Xu, Jiangmin, 2020. "Location choice, portfolio choice," Journal of Financial Economics, Elsevier, vol. 138(1), pages 74-94.
    117. Jean-Marc Le Caillec, 2022. "Hypothesis Testing Fusion for Nonlinearity Detection in Hedge Fund Price Returns," Post-Print hal-03739132, HAL.
    118. Andrea Berardi & Michael Markovich & Alberto Plazzi & Andrea Tamoni, 2021. "Mind the (Convergence) Gap: Bond Predictability Strikes Back!," Management Science, INFORMS, vol. 67(12), pages 7888-7911, December.
    119. Santos, André A.P. & Torrent, Hudson S., 2022. "Markowitz meets technical analysis: Building optimal portfolios by exploiting information in trend-following signals," Finance Research Letters, Elsevier, vol. 49(C).
    120. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    121. Ioannis Branikas & Harrison Hong & Jiangmin Xu, 2017. "Location Choice, Portfolio Choice," NBER Working Papers 23040, National Bureau of Economic Research, Inc.
    122. Ardia, David & Boudt, Kris & Wauters, Marjan, 2016. "The economic benefits of market timing the style allocation of characteristic-based portfolios," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 38-62.
    123. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J. & Uppal, Raman, 2017. "A Portfolio Perspective on the Multitude of Firm Characteristics," CEPR Discussion Papers 12417, C.E.P.R. Discussion Papers.
    124. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).
    125. Richard Martin & Torsten Schoneborn, 2011. "Mean Reversion Pays, but Costs," Papers 1103.4934, arXiv.org.
    126. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.
    127. Irina Murtazashvili & Nadia Vozlyublennaia, 2013. "Diversification Strategies: Do Limited Data Constrain Investors?," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(2), pages 215-232, June.
    128. Jiang, Chonghui & Du, Jiangze & An, Yunbi & Zhang, Jinqing, 2021. "Factor tracking: A new smart beta strategy that outperforms naïve diversification," Economic Modelling, Elsevier, vol. 96(C), pages 396-408.
    129. Fousseni Chabi-Yo & Markus Huggenberger & Florian Weigert, 2019. "Multivariate Crash Risk," Working Papers on Finance 1901, University of St. Gallen, School of Finance.
    130. Sangwon Suh, 2018. "Portfolio Selection using New Factors based on Firm Characteristics," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 43(1), pages 77-99, March.

  4. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.

    Cited by:

    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Etienne, Xiaoli, 2015. "Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices," 2015 Conference, August 9-14, 2015, Milan, Italy 211626, International Association of Agricultural Economists.
    3. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    4. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    5. Márcio Gomes Pinto Garcia & Marcelo Cunha Medeiros & Francisco Eduardo de Luna e Almeida Santos, 2014. "Economic gains of realized volatility in the Brazilian stock market," Brazilian Review of Finance, Brazilian Society of Finance, vol. 12(3), pages 319-349.
    6. Markus Leippold & Hanlin Yang, 2023. "Mixed‐frequency predictive regressions with parameter learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1955-1972, December.
    7. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    8. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-13R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2016.
    9. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    10. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    11. Prabheesh, K.P. & Sasongko, Aryo & Indawan, Fiskara, 2023. "Did the policy responses influence credit and business cycle co-movement during the COVID-19 crisis? Evidence from Indonesia," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 243-255.
    12. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    13. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    14. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).
    15. Edward S. Knotek & Saeed Zaman, 2024. "Nowcasting Inflation," Working Papers 24-06, Federal Reserve Bank of Cleveland.
    16. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    17. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    18. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    19. Harchaoui, Tarek M. & Janssen, Robert V., 2018. "How can big data enhance the timeliness of official statistics?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 225-234.
    20. Holmes, Mark J. & Iregui, Ana María & Otero, Jesús, 2021. "The effects of FX-interventions on forecasters disagreement: A mixed data sampling view," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    21. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    22. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    23. Helena Rodríguez, 2014. "Un indicador de la evolución del PIB uruguayo en tiempo real," Documentos de trabajo 2014009, Banco Central del Uruguay.
    24. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    25. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    26. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    27. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    28. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    29. Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
    30. Gregory Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Staff Working Papers 07-20, Bank of Canada.
    31. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    32. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    33. Gustavo Adolfo HERNANDEZ DIAZ & Margarita MARÍN JARAMILLO, 2016. "Pronóstico del Consumo Privado: Usando datos de alta frecuencia para el pronóstico de variables de baja frecuencia," Archivos de Economía 14828, Departamento Nacional de Planeación.
    34. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    35. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    36. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    37. Dufour, Jean-Marie & García, René & Taamouti, Abderrahim, 2008. "Measuring causality between volatility and returns with high-frequency data," UC3M Working papers. Economics we084422, Universidad Carlos III de Madrid. Departamento de Economía.
    38. Anthony S. Tay, 2006. "Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth," Macroeconomics Working Papers 22480, East Asian Bureau of Economic Research.
    39. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    40. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    41. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    42. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    43. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    44. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    45. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    46. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
    47. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    48. Langedijk, Sven & Monokroussos, George & Papanagiotou, Evangelia, 2015. "Benchmarking Liquidity Proxies: Accounting for Dynamics and Frequency Issues," MPRA Paper 61865, University Library of Munich, Germany.
    49. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    50. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    51. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
    52. Francesco Ravazzolo & Joaquin Vespignani, 2017. "World steel production: A new monthly indicator of global real economic activity," CAMA Working Papers 2017-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    53. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    54. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    55. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    56. Michelle T. Armesto & Ruben Hernandez-Murillo & Michael T. Owyang & Jeremy M. Piger, 2007. "Identifying asymmetry in the language of the Beige Book: a mixed data sampling approach," Working Papers 2007-010, Federal Reserve Bank of St. Louis.
    57. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    58. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    59. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    60. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    61. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    62. Emre Alper, C. & Fendoglu, Salih & Saltoglu, Burak, 2012. "MIDAS volatility forecast performance under market stress: Evidence from emerging stock markets," Economics Letters, Elsevier, vol. 117(2), pages 528-532.
    63. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    64. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    65. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    66. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    67. Pan, Zhiyuan & Liu, Li, 2018. "Forecasting stock return volatility: A comparison between the roles of short-term and long-term leverage effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 168-180.
    68. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    69. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    70. Nguyen, Hoang & Javed, Farrukh, 2023. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 272-292.
    71. Virk, Nader & Javed, Farrukh, 2017. "European equity market integration and joint relationship of conditional volatility and correlations," Journal of International Money and Finance, Elsevier, vol. 71(C), pages 53-77.
    72. Michael P. Clements & Ana Beatriz Galvão, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206, November.
    73. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
    74. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    75. Emami Javanmard, M. & Tang, Y. & Wang, Z. & Tontiwachwuthikul, P., 2023. "Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector," Applied Energy, Elsevier, vol. 338(C).
    76. Michael Boldin & Jonathan H. Wright, 2015. "Weather-Adjusting Economic Data," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(2 (Fall)), pages 227-278.
    77. Jonas E. Arias & Minchul Shin, 2020. "Tracking U.S. Real GDP Growth During the Pandemic," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 5(3), pages 9-14, September.
    78. Jung, Alexander, 2017. "Forecasting broad money velocity," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 421-432.
    79. Hong Shen & Qi Pan, 2022. "Risk Contagion between Commodity Markets and the Macro Economy during COVID-19: Evidence from China," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    80. J. Isaac Miller, 2010. "Cointegrating regressions with messy regressors and an application to mixed‐frequency series," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 255-277, July.
    81. Andersen, Torben G. & Bollerslev, Tim & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies (CFS).
    82. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2004. "A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1," NBER Working Papers 10447, National Bureau of Economic Research, Inc.
    83. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    84. Hamilton, James D., 2008. "Daily monetary policy shocks and new home sales," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1171-1190, October.
    85. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    86. Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023. "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, vol. 5(2), pages 295-365, June.
    87. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    88. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    89. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    90. Gopal K. Basak & Ravi Jagannathan & Tongshu Ma, 2009. "Jackknife Estimator for Tracking Error Variance of Optimal Portfolios," Management Science, INFORMS, vol. 55(6), pages 990-1002, June.
    91. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    92. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    93. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    94. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    95. Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "Constructing a financial fragility index for emerging countries," Finance Research Letters, Elsevier, vol. 11(4), pages 410-419.
    96. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
    97. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    98. Robert M. Kunst & Martin Wagner, 2020. "Economic forecasting: editors’ introduction," Empirical Economics, Springer, vol. 58(1), pages 1-5, January.
    99. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies," CIRANO Working Papers 2004s-19, CIRANO.
    100. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, October.
    101. F. Lilla, 2017. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed," Working Papers wp1099, Dipartimento Scienze Economiche, Universita' di Bologna.
    102. Zhu, Sha & Liu, Qiuhong & Wang, Yan & Wei, Yu & Wei, Guiwu, 2019. "Which fear index matters for predicting US stock market volatilities: Text-counts or option based measurement?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    103. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    104. Hanslin Grossmann, Sandra & Scheufele, Rolf, 2015. "Foreign PMIs: A reliable indicator for Swiss exports," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112830, Verein für Socialpolitik / German Economic Association.
    105. Bhadury, Soumya & Ghosh, Saurabh & Kumar, Pankaj, 2019. "Nowcasting GDP Growth Using a Coincident Economic Indicator for India," MPRA Paper 96007, University Library of Munich, Germany.
    106. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    107. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    108. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    109. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    110. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    111. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    112. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    113. Lee, Chien-Chiang & Chen, Mei-Ping & Chang, Chi-Hung, 2014. "Industry co-movement and cross-listing: Do home country factors matter?," Japan and the World Economy, Elsevier, vol. 32(C), pages 96-110.
    114. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
    115. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
    116. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    117. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    118. Ye, Wuyi & Jiang, Kunliang & Liu, Xiaoquan, 2021. "Financial contagion and the TIR-MIDAS model," Finance Research Letters, Elsevier, vol. 39(C).
    119. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    120. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    121. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    122. Tony Chernis & Taylor Webley, 2022. "Nowcasting Canadian GDP with Density Combinations," Discussion Papers 2022-12, Bank of Canada.
    123. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    124. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    125. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    126. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
    127. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
    128. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    129. Julien Chevallier, 2021. "Covid-19 Outbreak and CO2 Emissions: Macro-Financial Linkages," Working Papers 2021-004, Department of Research, Ipag Business School.
    130. Charfeddine, Lanouar & Klein, Tony & Walther, Thomas, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," QBS Working Paper Series 2018/03, Queen's University Belfast, Queen's Business School.
    131. Schreiber, Sven, 2018. "Weather-induced Short-term Fluctuations of Economic Output," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181622, Verein für Socialpolitik / German Economic Association.
    132. Sampi Bravo,James Robert Ezequiel & Jooste,Charl, 2020. "Nowcasting Economic Activity in Times of COVID-19 : An Approximation from the Google Community Mobility Report," Policy Research Working Paper Series 9247, The World Bank.
    133. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Nowcasting," Working Papers ECARES ECARES 2010-021, ULB -- Universite Libre de Bruxelles.
    134. William Barnett & Marcelle Chauvetz & Danilo Leiva-Leonx, 2014. "Real-Time Nowcasting Nominal GDP Under Structural Break," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201313, University of Kansas, Department of Economics, revised Feb 2014.
    135. S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions, Second Version," PIER Working Paper Archive 08-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 04 Apr 2008.
    136. Ioannis Kasparis & Peter C.B. Phillips, 2009. "Dynamic Misspecification in Nonparametric Cointegrating Regression," Cowles Foundation Discussion Papers 1700, Cowles Foundation for Research in Economics, Yale University.
    137. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    138. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    139. MAMATZAKIS, emmanuel & MAMATZAKIS, E, 2022. "Understanding the impact of travel on wellbeing: evidence for Great Britain during the pandemic," MPRA Paper 112974, University Library of Munich, Germany.
    140. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    141. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    142. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    143. Philip Hans Franses & Eva Janssens, 2017. "Recovering Historical Inflation Data from Postage Stamps Prices," JRFM, MDPI, vol. 10(4), pages 1-11, November.
    144. Shang, Yuhuang & Zheng, Tingguo, 2021. "Mixed-frequency SV model for stock volatility and macroeconomics," Economic Modelling, Elsevier, vol. 95(C), pages 462-472.
    145. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    146. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    147. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    148. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    149. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    150. Pradeep Mishra & Khder Alakkari & Mostafa Abotaleb & Pankaj Kumar Singh & Shilpi Singh & Monika Ray & Soumitra Sankar Das & Umme Habibah Rahman & Ali J. Othman & Nazirya Alexandrovna Ibragimova & Gulf, 2021. "Nowcasting India Economic Growth Using a Mixed-Data Sampling (MIDAS) Model (Empirical Study with Economic Policy Uncertainty–Consumer Prices Index)," Data, MDPI, vol. 6(11), pages 1-15, November.
    151. Ralf Becker & Adam Clements & Robert O'Neill, 2010. "A Cholesky-MIDAS model for predicting stock portfolio volatility," Centre for Growth and Business Cycle Research Discussion Paper Series 149, Economics, The University of Manchester.
    152. Xu Gong & Mingchao Wang & Liuguo Shao, 2022. "The impact of macro economy on the oil price volatility from the perspective of mixing frequency," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4487-4514, October.
    153. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    154. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    155. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    156. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    157. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    158. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    159. Guerrero Víctor M. & García Andrea C. & Sainz Esperanza, 2013. "Rapid Estimates of Mexico’s Quarterly GDP," Journal of Official Statistics, Sciendo, vol. 29(3), pages 397-423, June.
    160. Akbar Marvasti & Sami Dakhlia, 2021. "Minimum information management and price‐abundance relationships in a fishery," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 69(4), pages 491-518, December.
    161. Chaudhuri, Malika & Calantone, Roger J. & Voorhees, Clay M. & Cockrell, Seth, 2018. "Disentangling the effects of promotion mix on new product sales: An examination of disaggregated drivers and the moderating effect of product class," Journal of Business Research, Elsevier, vol. 90(C), pages 286-294.
    162. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    163. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    164. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    165. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    166. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    167. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    168. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    169. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
    170. Jonathan Dark & Xin Gao & Thijs van der Heijden & Federico Nardari, 2022. "Forecasting variance swap payoffs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2135-2164, December.
    171. Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    172. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    173. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    174. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    175. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    176. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    177. Chaoyi Chen & Yiguo Sun & Yao Rao, 2023. "Threshold MIDAS Forecasting of Inflation Rate," Working Papers 202314, University of Liverpool, Department of Economics.
    178. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    179. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.
    180. Dario Buono & George Kapetanios & Massimiliano Marcellino & Gianluigi Mazzi & Fotis Papailias, 2018. "Big Data Econometrics: Now Casting and Early Estimates," BAFFI CAREFIN Working Papers 1882, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    181. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    182. Chunpeng Yang & Rengui Zhang, 2014. "Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 966-972, March.
    183. Aparicio, Diego & Bertolotto, Manuel I., 2020. "Forecasting inflation with online prices," International Journal of Forecasting, Elsevier, vol. 36(2), pages 232-247.
    184. d’Aspremont, Alexandre & Arous, Simon Ben & Bricongne, Jean-Charles & Lietti, Benjamin & Meunier, Baptiste, 2024. "Satellites turn “concrete”: tracking cement with satellite data and neural networks," Working Paper Series 2900, European Central Bank.
    185. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    186. Edward S. Knotek & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.
    187. Zhang, Jin & Li, Pujiang & Zhao, Guochang, 2018. "Is power generation really the gold measure of the Chinese economy? A conceptual and empirical assessment," Energy Policy, Elsevier, vol. 121(C), pages 211-216.
    188. Frömmel, Michael & Midiliç, Murat, 2021. "Daily currency interventions in an emerging market: Incorporating reserve accumulation to the reaction function," Economic Modelling, Elsevier, vol. 97(C), pages 461-476.
    189. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    190. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    191. Wang, Xinyu & Qi, Zikang & Huang, Jianglu, 2023. "How do monetary shock, financial crisis, and quotation reform affect the long memory of exchange rate volatility? Evidence from major currencies," Economic Modelling, Elsevier, vol. 120(C).
    192. Sihong Chen & Qi Li & Qiaoyu Wang & Yu Yvette Zhang, 2023. "Multivariate models of commodity futures markets: a dynamic copula approach," Empirical Economics, Springer, vol. 64(6), pages 3037-3057, June.
    193. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methods of the Ifo short-term forecast," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    194. Naif Alsagr & Stefan F. Van Hemmen Almazor, 2020. "Oil Rent, Geopolitical Risk and Banking Sector Performance," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 305-314.
    195. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    196. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    197. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    198. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    199. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    200. Franky Juliano Galeano-Ramírez & Nicolás Martínez-Cortés & Carlos D. Rojas-Martínez, 2021. "Nowcasting Colombian Economic Activity: DFM and Factor-MIDAS approaches," Borradores de Economia 1168, Banco de la Republica de Colombia.
    201. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    202. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    203. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    204. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    205. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    206. Turhan, Ibrahim M. & Sensoy, Ahmet & Hacihasanoglu, Erk, 2015. "Shaping the manufacturing industry performance: MIDAS approach," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 286-290.
    207. Feng-Li Lin & Mei-Chih Wang, 2019. "Does economic growth cause military expenditure to go up? Using MF-VAR model," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(6), pages 3097-3117, November.
    208. Maria Begicheva & Alexey Zaytsev, 2021. "Bank transactions embeddings help to uncover current macroeconomics," Papers 2110.12000, arXiv.org, revised Dec 2021.
    209. Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers 2007.12838, arXiv.org.
    210. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    211. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    212. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    213. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    214. Murat Körs & Mehmet Baha Karan, 2023. "Stock exchange volatility forecasting under market stress with MIDAS regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 295-306, January.
    215. William A. Barnett & Marcelle Chauvet & Danilo Leiva-Leon, 2014. "Real-Time Nowcasting of Nominal GDP Under Structural Breaks," Staff Working Papers 14-39, Bank of Canada.
    216. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    217. Jian Chai & Puju Cao & Xiaoyang Zhou & Kin Keung Lai & Xiaofeng Chen & Siping (Sue) Su, 2018. "The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data," Energies, MDPI, vol. 11(6), pages 1-14, May.
    218. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 55, October.
    219. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    220. James D. Hamilton, 2008. "Daily Monetary Policy Shocks and the Delayed Response of New Home Sales," NBER Working Papers 14223, National Bureau of Economic Research, Inc.
    221. Anthony S. Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Development Economics Working Papers 22482, East Asian Bureau of Economic Research.
    222. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    223. Cleiton Guollo Taufemback, 2023. "Asymptotic Behavior of Temporal Aggregation in Mixed‐Frequency Datasets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 894-909, August.
    224. F. Lilla, 2016. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models," Working Papers wp1084, Dipartimento Scienze Economiche, Universita' di Bologna.
    225. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    226. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    227. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    228. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    229. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    230. Boriss Siliverstovs, 2015. "Dissecting the purchasing managers' index," KOF Working papers 15-376, KOF Swiss Economic Institute, ETH Zurich.
    231. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    232. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    233. Simona Boffelli & Vasiliki D. Skintzi & Giovanni Urga, 2017. "High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 62-105.
    234. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    235. Barnett, William A. & Chauvet, Marcelle & Leiva-Leon, Danilo, 2016. "Real-time nowcasting of nominal GDP with structural breaks," Journal of Econometrics, Elsevier, vol. 191(2), pages 312-324.
    236. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.
    237. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    238. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    239. Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
    240. Mahmut Gunay, 2018. "Nowcasting Annual Turkish GDP Growth with MIDAS," CBT Research Notes in Economics 1810, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    241. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    242. Modugno, Michele, 2011. "Nowcasting inflation using high frequency data," Working Paper Series 1324, European Central Bank.
    243. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    244. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    245. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    246. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    247. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    248. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    249. Gong, Xu & Sun, Yi & Du, Zhili, 2022. "Geopolitical risk and China's oil security," Energy Policy, Elsevier, vol. 163(C).
    250. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    251. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
    252. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    253. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    254. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    255. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    256. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    257. Emmanuel Apergis & Nicholas Apergis, 2021. "Can the COVID-19 Pandemic and Oil Prices Drive the US Partisan Conflict Index," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-4.
    258. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    259. Yun Liu & Yeonwoo Rho, 2018. "On the Choice of Instruments in Mixed Frequency Specification Tests," Papers 1809.05503, arXiv.org.
    260. Emmanuel Mamatzakis & Mike G. Tsionas & Steven Ongena, 2023. "Why do households repay their debt in UK during the COVID-19 crisis?," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(8), pages 1789-1823, April.
    261. Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022. "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers 02-2022, Economics Section, The Graduate Institute of International Studies.
    262. Guy P. Nason & James L. Wei, 2022. "Quantifying the economic response to COVID‐19 mitigations and death rates via forecasting purchasing managers' indices using generalised network autoregressive models with exogenous variables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1778-1792, October.
    263. Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    264. Xiaqing Su & Zhe Liu, 2021. "Sector Volatility Spillover and Economic Policy Uncertainty: Evidence from China’s Stock Market," Mathematics, MDPI, vol. 9(12), pages 1-22, June.
    265. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    266. Dr. Sandra Hanslin Grossmann & Dr. Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    267. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    268. Zhang Wu & Terence Tai-Leung Chong, 2021. "Does the macroeconomy matter to market volatility? Evidence from US industries," Empirical Economics, Springer, vol. 61(6), pages 2931-2962, December.
    269. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
    270. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    271. Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019. "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 373-402, National Bureau of Economic Research, Inc.
    272. Lee, Chien-Chiang & Chen, Mei-Ping, 2020. "Do natural disasters and geopolitical risks matter for cross-border country exchange-traded fund returns?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    273. Samuel N. Cohen & Silvia Lui & Will Malpass & Giulia Mantoan & Lars Nesheim & 'Aureo de Paula & Andrew Reeves & Craig Scott & Emma Small & Lingyi Yang, 2023. "Nowcasting with signature methods," Papers 2305.10256, arXiv.org.
    274. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    275. Bahram Adrangi & Arjun Chatrath & Kambiz Raffiee, 2023. "S&P 500 volatility, volatility regimes, and economic uncertainty," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1362-1387, October.
    276. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    277. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    278. Conefrey, Thomas & Walsh, Graeme, 2018. "A Monthly Indicator of Economic Activity for Ireland," Economic Letters 14/EL/18, Central Bank of Ireland.
    279. Matěj Liberda, 2017. "Mixed-frequency Drivers of Precious Metal Prices," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(6), pages 2007-2015.
    280. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    281. Poncela, Pilar & Guerrero, Víctor & Islas C., Alejandro & Rodríguez, Julio & Sánchez-Mangas, Rocío, 2014. "Mexico: Combining monthly inflation predictions from surveys," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    282. Wang, Ruina & Li, Jinfang, 2021. "The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    283. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    284. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    285. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    286. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    287. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    288. Roberto Steri, 2015. "Collateral-Based Asset Pricing," 2015 Meeting Papers 293, Society for Economic Dynamics.
    289. Uğurlu-Yıldırım, Ecenur & Şendeniz-Yüncü, İlkay, 2021. "Additional factor in asset-pricing: Institutional ownership," Finance Research Letters, Elsevier, vol. 40(C).
    290. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
    291. Angelos Kanas & Panagiotis D. Zervopoulos, 2020. "Systemic risk-shifting in U.S. commercial banking," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 517-539, February.
    292. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
    293. Michael D. Boldin & Jonathan H. Wright, 2015. "Weather-adjusting employment data," Working Papers 15-5, Federal Reserve Bank of Philadelphia.
    294. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    295. Thomas Walther & Lanouar Charfeddine & Tony Klein, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," Working Papers on Finance 1816, University of St. Gallen, School of Finance.
    296. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    297. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    298. Franses, Ph.H.B.F., 2016. "Yet another look at MIDAS regression," Econometric Institute Research Papers EI2016-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    299. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    300. Marc Francke & Alex Van De Minne, 2022. "Daily appraisal of commercial real estate a new mixed frequency approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(5), pages 1257-1281, September.
    301. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    302. Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
    303. Bonnier, Jean-Baptiste, 2022. "Forecasting crude oil volatility with exogenous predictors: As good as it GETS?," Energy Economics, Elsevier, vol. 111(C).
    304. Czado, Claudia & Ivanov, Eugen & Okhrin, Yarema, 2019. "Modelling temporal dependence of realized variances with vines," Econometrics and Statistics, Elsevier, vol. 12(C), pages 198-216.
    305. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    306. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    307. Yun-Yeong Kim, 2016. "Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation," Korean Economic Review, Korean Economic Association, vol. 32, pages 41-75.
    308. Liu, Min & Lee, Chien-Chiang, 2022. "Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS," Resources Policy, Elsevier, vol. 76(C).
    309. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
    310. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.
    311. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

  5. Jose Tavares & Rossen Valkanov, 2001. "The neglected effect of fiscal policy on stock and bond returns," Nova SBE Working Paper Series wp413, Universidade Nova de Lisboa, Nova School of Business and Economics.

    Cited by:

    1. Rangan Gupta & Chi Keung Marco Lau & Stephen M. Miller & Mark E. Wohar, 2017. "U.S. Fiscal Policy and Asset Prices: The Role of Partisan Conflict," Working Papers 201742, University of Pretoria, Department of Economics.
    2. Kofi A. Amoateng, 2019. "Did Tom Brady Save the US stock market? Market Anomaly or Market Efficiency?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(5), pages 128-128, May.
    3. Pasquale Foresti & Oreste Napolitano, 2016. "On the Stock Markets’ Reactions to Taxation and Public Expenditure," LEQS – LSE 'Europe in Question' Discussion Paper Series 115, European Institute, LSE.
    4. Ardagna Silvia & Caselli Francesco & Lane Timothy, 2007. "Fiscal Discipline and the Cost of Public Debt Service: Some Estimates for OECD Countries," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-35, August.
    5. Hardik A. Marfatia & Rangan Gupta & Stephen M. Miller, 2020. "125 Years of Time-Varying Effects of Fiscal Policy on Financial Markets," Working papers 2020-12, University of Connecticut, Department of Economics.
    6. Ardagna, Silvia, 2009. "Financial markets' behavior around episodes of large changes in the fiscal stance," European Economic Review, Elsevier, vol. 53(1), pages 37-55, January.
    7. Ghassen El Montasser & Rangan Gupta & Jooste Charl & Stephen M. Miller, 2020. "The Time-series Linkages between US Fiscal Policy and Asset Prices," Public Finance Review, , vol. 48(3), pages 303-339, May.
    8. Agnello, L. & Furceri, D. & R.M, Sousa., 2011. "Fiscal Policy Discretion, Private Spending, and Crisis Episodes," Working papers 354, Banque de France.
    9. Mr. Sanjeev Gupta & Mr. Carlos Mulas-Granados & Mr. Emanuele Baldacci, 2009. "How Effective is Fiscal Policy Response in Systemic Banking Crises?," IMF Working Papers 2009/160, International Monetary Fund.
    10. Taha, Roshaiza & Colombage, Sisira R.N. & Maslyuk, Svetlana & Nanthakumar, Loganathan, 2013. "Does financial system activity affect tax revenue in Malaysia? Bounds testing and causality approach," Journal of Asian Economics, Elsevier, vol. 24(C), pages 147-157.
    11. K. Peren Arin & Abdullah Mamun & Nanda Purushothman, 2009. "The effects of tax policy on financial markets: G3 evidence," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 33-46, January.
    12. Narayan Sethi & Saileja Mohanty & Sanhita Sucharita & Nanthakumar Loganathan, 2020. "Tax Reform And Economic Growth Nexus In India: Evidence From The Cointegration And Rolling-Window Causality," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 65(06), pages 1699-1725, December.
    13. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2012. "How does fiscal policy react to wealth composition and asset prices?," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 874-890.
    14. Chiwei Su & Yiru Liu & Chang Liu & Ran Tao, 2022. "The Impact of Medical and Health Fiscal Expenditures on Pharmaceutical Industry Stock Index in China," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    15. Rangan Gupta & Charl Jooste & Kanyane Matlou, 2013. "A Time-Varying Approach to Analysing Fiscal Policy and Asset Prices in South Africa," Working Papers 201303, University of Pretoria, Department of Economics.
    16. Terezie Lokajickova, 2010. "Could the Stability and Growth Pact Be Substituted by the Financial Markets?," Working Papers IES 2010/30, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Dec 2010.
    17. Luca Agnello & Davide Furceri & Ricardo Sousa, 2013. "Discretionary Government Consumption, Private Domestic Demand, and Crisis Episodes," Open Economies Review, Springer, vol. 24(1), pages 79-100, February.
    18. Alexander Zimper, 2014. "The minimal confidence levels of Basel capital regulation," Journal of Banking Regulation, Palgrave Macmillan, vol. 15(2), pages 129-143, April.
    19. Ardagna, Silvia & Caselli, Francesco & Lane, Timothy, 2005. "Fiscal discipline and the cost of public debt service: some estimates for OECD countries," LSE Research Online Documents on Economics 3562, London School of Economics and Political Science, LSE Library.
    20. Luca Agnello & Gilles Dufrénot & Ricardo M. Sousa, 2012. "Adjusting the U.S. Fiscal Policy for Asset Prices: Evidence from a TVP-MS Framework," NIPE Working Papers 20/2012, NIPE - Universidade do Minho.
    21. Anthony M. Diercks & William Waller, 2017. "Taxes and the Fed : Theory and Evidence from Equities," Finance and Economics Discussion Series 2017-104, Board of Governors of the Federal Reserve System (U.S.).
    22. BUI, Duy-Tung & LLORCA, Matthieu & BUI, Thi Mai Hoai, 2018. "Dynamics between stock market movements and fiscal policy: Empirical evidence from emerging Asian economies," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 65-74.
    23. Marfatia, Hardik A. & Gupta, Rangan & Miller, Stephen, 2020. "125 ​Years of time-varying effects of fiscal policy on financial markets," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 303-320.

Articles

  1. Alberto Plazzi & Walter Torous & Rossen Valkanov, 2010. "Expected Returns and Expected Growth in Rents of Commercial Real Estate," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3469-3519.

    Cited by:

    1. Eirini Andriopoulou & Panagiotis Tsakloglou, 2011. "Once poor, always poor? Do initial conditions matter? Evidence from the ECHP," DEOS Working Papers 1127, Athens University of Economics and Business.
    2. John V. Duca & David C. Ling, 2015. "The other (commercial) real estate boom and bust: the effects of risk premia and regulatory capital arbitrage," Working Papers 1504, Federal Reserve Bank of Dallas.
    3. Renhe Liu & Eddie Chi-man Hui & Jiaqi Lv & Yi Chen, 2017. "What Drives Housing Markets: Fundamentals or Bubbles?," The Journal of Real Estate Finance and Economics, Springer, vol. 55(4), pages 395-415, November.
    4. Timmermann, Allan & Møller, Stig & Pedersen, Thomas & Schütte, Erik Christian Montes, 2021. "Search and Predictability of Prices in the Housing Market," CEPR Discussion Papers 15875, C.E.P.R. Discussion Papers.
    5. Jarl G. Kallberg & Yoshiki Shimizu, 2023. "Acquisitions and the Opportunity Set," The Journal of Real Estate Finance and Economics, Springer, vol. 66(4), pages 904-938, May.
    6. Kraft, Holger & Munk, Claus & Weiss, Farina, 2019. "Predictors and portfolios over the life cycle," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 1-27.
    7. Zongyuan Li & Rose Neng Lai, 2021. "Not All Bank Liquidity Creation Boosts Prices-The Case of the US Housing Market," International Real Estate Review, Global Social Science Institute, vol. 24(1), pages 19-58.
    8. Engsted, Tom & Hviid, Simon J. & Pedersen, Thomas Q., 2016. "Explosive bubbles in house prices? Evidence from the OECD countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 14-25.
    9. Daisy J. HUANG & Charles Ka Yui LEUNG & Chung-Yi TSE, 2017. "What account for the differences in rent-price ratio and turnover rate? A search-and-matching approach," ISER Discussion Paper 0990, Institute of Social and Economic Research, Osaka University.
    10. Stefano Colonnello & Roberto Marfè & Qizhou Xiong, 2021. "Housing Yields," Working Papers 2021:21, Department of Economics, University of Venice "Ca' Foscari", revised 2021.
    11. Poh-Chin Lai & Si Chen & Chien-Tat Low & Ester Cerin & Robert Stimson & Pui Yun Paulina Wong, 2018. "Neighborhood Variation of Sustainable Urban Morphological Characteristics," IJERPH, MDPI, vol. 15(3), pages 1-13, March.
    12. Cenedese, Gino & Mallucci, Enrico, 2016. "What moves international stock and bond markets?," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 94-113.
    13. Haroon Mumtaz & Roman Sustek, 2023. "Global house prices since 1950," Discussion Papers 2307, Centre for Macroeconomics (CFM).
    14. Xudong An & Yongheng Deng & Joseph Nichols & Anthony Sanders, 2015. "What is Subordination About? Credit Risk and Subordination Levels in Commercial Mortgage-backed Securities (CMBS)," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 231-253, August.
    15. Engsted, Tom & Pedersen, Thomas Q., 2014. "Housing market volatility in the OECD area: Evidence from VAR based return decompositions," Journal of Macroeconomics, Elsevier, vol. 42(C), pages 91-103.
    16. Christian Rehring, 2012. "Real Estate in a Mixed‐Asset Portfolio: The Role of the Investment Horizon," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40(1), pages 65-95, March.
    17. Demetrescu, Matei & Rodrigues, Paulo M.M., 2022. "Residual-augmented IVX predictive regression," Journal of Econometrics, Elsevier, vol. 227(2), pages 429-460.
    18. Caporale, Guglielmo Maria & Sousa, Ricardo M., 2016. "Consumption, wealth, stock and housing returns: Evidence from emerging markets," Research in International Business and Finance, Elsevier, vol. 36(C), pages 562-578.
    19. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    20. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    21. Daniel Melser & Adrian D. Lee, 2014. "Estimating the Excess Returns to Housing at a Disaggregated Level: An Application to Sydney 2003–2011," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(3), pages 756-790, September.
    22. Plogmann, Jana & Mußhoff, Oliver & Odening, Martin & Ritter, Matthias, 2018. "What moves the German land market? A decomposition of the land rent-price ratio," FORLand Working Papers 05 (2018), Humboldt University Berlin, DFG Research Unit 2569 FORLand "Agricultural Land Markets – Efficiency and Regulation".
    23. Levon Goukasian & Mehdi Majbouri, 2010. "The Reaction of Real Estate–Related Industries to the Monetary Policy Actions," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 38(2), pages 355-398, June.
    24. Jianhua Gang & Liang Peng & Thomas G. Thibodeau, 2020. "Risk and Returns of Income Producing Properties: Core versus Noncore," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 476-503, June.
    25. Jędrzej Białkowski & Sheridan Titman & Garry Twite, 2023. "The Determinants of Office Cap Rates: The International Evidence," Working Papers in Economics 23/01, University of Canterbury, Department of Economics and Finance.
    26. Jim Clayton & David Ling & Andy Naranjo, 2009. "Commercial Real Estate Valuation: Fundamentals Versus Investor Sentiment," The Journal of Real Estate Finance and Economics, Springer, vol. 38(1), pages 5-37, January.
    27. Engsted, Tom & Pedersen, Thomas Q., 2015. "Predicting returns and rent growth in the housing market using the rent-price ratio: Evidence from the OECD countries," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 257-275.
    28. Jack Corgel & Crocker Liu & Robert White, 2015. "Determinants of Hotel Property Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 51(3), pages 415-439, October.
    29. Ralph S.J. Koijen & Stijn Van Nieuwerburgh, 2011. "Predictability of Returns and Cash Flows," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 467-491, December.
    30. Zifeng Feng, 2022. "How Does Local Economy Affect Commercial Property Performance?," The Journal of Real Estate Finance and Economics, Springer, vol. 65(3), pages 361-383, October.
    31. Joshua Gallin, 2008. "The Long‐Run Relationship Between House Prices and Rents," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(4), pages 635-658, December.
    32. Liu, Crocker H. & Rosenthal, Stuart S. & Strange, William C., 2018. "The vertical city: Rent gradients, spatial structure, and agglomeration economies," Journal of Urban Economics, Elsevier, vol. 106(C), pages 101-122.
    33. Ellington, Michael & Fu, Xi & Zhu, Yunyi, 2023. "Real estate illiquidity and returns: A time-varying regional perspective," International Journal of Forecasting, Elsevier, vol. 39(1), pages 58-72.
    34. Dwight Jaffee & Richard Stanton & Nancy Wallace, 2019. "Energy Factors, Leasing Structure and the Market Price of Office Buildings in the U.S," The Journal of Real Estate Finance and Economics, Springer, vol. 59(3), pages 329-371, October.
    35. Doina Chichernea & Norm Miller & Jeff Fisher & Bob White & Michael Sklarz, 2008. "ACross-Sectional Analysis of CapRates by MSA," Journal of Real Estate Research, American Real Estate Society, vol. 30(3), pages 249-292.
    36. Tom Emmerling & Crocker Liu & Yildiray Yildirim, 2017. "The Hybrid Nature of Real Estate Trusts," ERES eres2017_370, European Real Estate Society (ERES).
    37. Gregg Fisher & Eva Steiner & Sheridan Titman & Ashvin Viswanathan, 2022. "Location density, systematic risk, and cap rates: Evidence from REITs," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(2), pages 366-400, June.
    38. Frank J. Fabozzi & Iason Kynigakis & Ekaterini Panopoulou & Radu S. Tunaru, 2020. "Detecting Bubbles in the US and UK Real Estate Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 469-513, May.
    39. Kraft, Holger & Munk, Claus & Weiss, Farina, 2017. "Predictors and portfolios over the life cycle: Skill vs. luck," SAFE Working Paper Series 139, Leibniz Institute for Financial Research SAFE, revised 2017.
    40. Jędrzej Białkowski & Sheridan Titman & Garry Twite, 2023. "The determinants of office cap rates: The international evidence," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 539-572, May.
    41. Masud Alam, 2021. "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return," Papers 2107.10455, arXiv.org.
    42. Alexander N. Bogin & LaRhonda Ealey & Kirsten Landeryou & Scott Smith & Andrew Tsai, 2023. "Geographic Disaggregation of House Price Stress Paths: Implications for Single-Family Credit Risk Measurement," FHFA Staff Working Papers 23-02, Federal Housing Finance Agency.
    43. Lucas Hafemann, 2021. "Prudential Policies in the Eurozone: A Propensity Score Matching Approach," MAGKS Papers on Economics 202109, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    44. Gupta, Arpit & Van Nieuwerburgh, Stijn & Kontokosta, Constantine, 2022. "Take the Q train: Value capture of public infrastructure projects," Journal of Urban Economics, Elsevier, vol. 129(C).
    45. Eduard Hromada & Tomáš Krulický, 2021. "Investing in Real Estate in the Czech Republic and Analyzing the Dependence of Profitability and Technical and Socio-Economic Factors," Sustainability, MDPI, vol. 13(18), pages 1-12, September.
    46. Helmut Herwartz & Fang Xu, 2020. "Low Mortgage Rates and Securitization: A Distinct Perspective on the US Housing Boom," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 164-190, January.
    47. Hiebert, Paul & Sydow, Matthias, 2009. "What drives returns to euro area housing? Evidence from a dynamic dividend-discount model," Working Paper Series 1019, European Central Bank.
    48. Liang Peng & Thomas G. Thibodeau, 2020. "Interest Rates and Investment: Evidence from Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 554-586, May.
    49. Galina An & Charles Becker & Enoch Cheng, 2021. "Bubbling Away: Forecasting Real Estate Prices, Rents, and Bubbles in a Transition Economy," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 63(2), pages 263-317, June.
    50. Christian Rehring & Steffen Sebastian, 2010. "Dynamics Of Commercial Real Estate Asset Markets, Return Volatility, And The Investment Horizon," ERES eres2010_134, European Real Estate Society (ERES).
    51. James D. Shilling & C.F. Sirmans & Barrett A. Slade, 2017. "Spatial Correlation in Expected Returns in Commercial Real Estate Markets and the Role of Core Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 54(3), pages 297-337, April.
    52. Moutzouris, Ioannis C. & Nomikos, Nikos K., 2019. "Earnings yield and predictability in the dry bulk shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 140-159.
    53. Boudry, Walter I. & Kallberg, Jarl G. & Liu, Crocker H., 2013. "Investment opportunities and share repurchases," Journal of Corporate Finance, Elsevier, vol. 23(C), pages 23-38.

  2. Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2009. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns," The Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3411-3447, September.
    See citations under working paper version above.
  3. Alberto Plazzi & Walter Torous & Rossen Valkanov, 2008. "The Cross‐Sectional Dispersion of Commercial Real Estate Returns and Rent Growth: Time Variation and Economic Fluctuations," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(3), pages 403-439, September.

    Cited by:

    1. Yuming Li & Jing Yang, 2018. "House Price Dynamics and Excess Risk," International Real Estate Review, Global Social Science Institute, vol. 21(2), pages 251-274.
    2. Teulings, Coen & Lange, Rutger-Jan, 2021. "The option value of vacant land: Don't build when demand for housing is booming," CEPR Discussion Papers 16023, C.E.P.R. Discussion Papers.
    3. Stephen Lee & Giacomo Morri, 2015. "Real estate fund active management," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 33(6), pages 494-516, September.
    4. Daniel Melser & Adrian D. Lee, 2014. "Estimating the Excess Returns to Housing at a Disaggregated Level: An Application to Sydney 2003–2011," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(3), pages 756-790, September.
    5. Alexander Schätz & Steffen Sebastian, 2009. "The links between property and the economy -- evidence from the British and German markets," Journal of Property Research, Taylor & Francis Journals, vol. 26(2), pages 171-191, September.
    6. Charles Ka Yui Leung & Jun Zhang, 2011. ""Fire Sales" in Housing Market: Is the House- Search Process Similar to a Theme Park Visit?," International Real Estate Review, Global Social Science Institute, vol. 14(3), pages 311-329.
    7. Jianhua Gang & Liang Peng & Thomas G. Thibodeau, 2020. "Risk and Returns of Income Producing Properties: Core versus Noncore," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 476-503, June.
    8. Jim Clayton & David Ling & Andy Naranjo, 2009. "Commercial Real Estate Valuation: Fundamentals Versus Investor Sentiment," The Journal of Real Estate Finance and Economics, Springer, vol. 38(1), pages 5-37, January.
    9. Zifeng Feng, 2022. "How Does Local Economy Affect Commercial Property Performance?," The Journal of Real Estate Finance and Economics, Springer, vol. 65(3), pages 361-383, October.
    10. Leung, Charles Ka Yui & Zhang, Jun, 2011. "“Fire Sales” in housing market: is the house-searching process similar to a theme park visit?," MPRA Paper 29127, University Library of Munich, Germany.
    11. Tom Emmerling & Crocker Liu & Yildiray Yildirim, 2017. "The Hybrid Nature of Real Estate Trusts," ERES eres2017_370, European Real Estate Society (ERES).
    12. William G. Hardin & Xiaoquan Jiang & Zhonghua Wu, 2017. "Inflation Illusion, Expertise and Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 55(3), pages 345-369, October.
    13. Kevin C.H. Chiang & Xiaguan Jiang & Ming‐Long Lee, 2010. "REIT idiosyncratic risk," Journal of Property Research, Taylor & Francis Journals, vol. 26(4), pages 349-366, February.
    14. Christian Rehring & Steffen Sebastian, 2010. "Dynamics Of Commercial Real Estate Asset Markets, Return Volatility, And The Investment Horizon," ERES eres2010_134, European Real Estate Society (ERES).
    15. Abdulmalik Fatimah Binta & Udoekanem Namnso Bassey, 2022. "Commercial Real Estate Rental Variation in Ilorin, Nigeria," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 10(1), pages 140-155, January.
    16. Alexey Akimov & Simon Stevenson & Maxim Zagonov, 2015. "Public Real Estate and the Term Structure of Interest Rates: A Cross-Country Study," The Journal of Real Estate Finance and Economics, Springer, vol. 51(4), pages 503-540, November.

  4. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.

    Cited by:

    1. Viktor Todorov & Yang Zhang, 2022. "Information gains from using short‐dated options for measuring and forecasting volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 368-391, March.
    2. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Etienne, Xiaoli, 2015. "Financialization of Agricultural Commodity Markets: Do Financial Data Help to Forecast Agricultural Prices," 2015 Conference, August 9-14, 2015, Milan, Italy 211626, International Association of Agricultural Economists.
    4. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    5. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    6. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    7. Markus Leippold & Hanlin Yang, 2023. "Mixed‐frequency predictive regressions with parameter learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1955-1972, December.
    8. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    9. Philipp Gersing & Leopold Soegner & Manfred Deistler, 2022. "Retrieval from Mixed Sampling Frequency: Generic Identifiability in the Unit Root VAR," Papers 2204.05952, arXiv.org, revised Jul 2023.
    10. Tae-Hwy Lee & Weiping Yang, 2012. "Money–Income Granger-Causality in Quantiles," Advances in Econometrics, in: 30th Anniversary Edition, pages 385-409, Emerald Group Publishing Limited.
    11. Bell, Venetia & Co, Lai Wah & Stone, Sophie & Wallis, gavin`, 2014. "Nowcasting UK GDP growth," Bank of England Quarterly Bulletin, Bank of England, vol. 54(1), pages 58-68.
    12. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    13. Laurent Ferrara & Clément Marsilli, 2012. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Working Papers hal-04141077, HAL.
    14. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    15. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-13R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2016.
    16. Chao Liang & Yin Liao & Feng Ma & Bo Zhu, 2022. "United States Oil Fund volatility prediction: the roles of leverage effect and jumps," Empirical Economics, Springer, vol. 62(5), pages 2239-2262, May.
    17. González, Mariano & Nave, Juan & Rubio, Gonzalo, 2018. "Macroeconomic determinants of stock market betas," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 26-44.
    18. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    19. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    20. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    21. Babii, Andrii & Florens, Jean-Pierre, 2020. "Is completeness necessary? Estimation in nonidentified linear models," TSE Working Papers 20-1091, Toulouse School of Economics (TSE).
    22. Harchaoui, Tarek M. & Janssen, Robert V., 2018. "How can big data enhance the timeliness of official statistics?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 225-234.
    23. Holmes, Mark J. & Iregui, Ana María & Otero, Jesús, 2021. "The effects of FX-interventions on forecasters disagreement: A mixed data sampling view," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    24. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    25. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    26. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    27. Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
    28. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    29. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    30. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    31. Gustavo Adolfo HERNANDEZ DIAZ & Margarita MARÍN JARAMILLO, 2016. "Pronóstico del Consumo Privado: Usando datos de alta frecuencia para el pronóstico de variables de baja frecuencia," Archivos de Economía 14828, Departamento Nacional de Planeación.
    32. González-Sánchez, Mariano & Nave, Juan & Rubio, Gonzalo, 2020. "Effects of uncertainty and risk aversion on the exposure of investment-style factor returns to real activity," Research in International Business and Finance, Elsevier, vol. 53(C).
    33. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    34. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    35. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    36. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.
    37. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    38. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    39. Juselius, Mikael & Takáts, Előd, 2021. "Inflation and demography through time," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    40. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    41. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    42. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2020. "Testing a large set of zero restrictions in regression models, with an application to mixed frequency Granger causality," Journal of Econometrics, Elsevier, vol. 218(2), pages 633-654.
    43. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    44. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    45. Liu, Wei & Garrett, Ian, 2023. "Regime-dependent effects of macroeconomic uncertainty on realized volatility in the U.S. stock market," Economic Modelling, Elsevier, vol. 128(C).
    46. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    47. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    48. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    49. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    50. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    51. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Post-Print hal-01448237, HAL.
    52. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    53. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    54. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
    55. Dai, Peng-Fei & Xiong, Xiong & Duc Huynh, Toan Luu & Wang, Jiqiang, 2022. "The impact of economic policy uncertainties on the volatility of European carbon market," Journal of Commodity Markets, Elsevier, vol. 26(C).
    56. Lu Wang & Feng Ma & Guoshan Liu, 2020. "Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 797-810, August.
    57. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    58. Naimoli, Antonio & Storti, Giuseppe, 2019. "Heterogeneous component multiplicative error models for forecasting trading volumes," MPRA Paper 93802, University Library of Munich, Germany.
    59. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.
    60. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    61. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2012. "On the Macroeconomic Determinants of the Long-Term Oil-Stock Correlation," Working Papers 0525, University of Heidelberg, Department of Economics.
    62. David E. Allen & Michael McAleer & Marcel Scharth, 2010. "Realized Volatility Risk," KIER Working Papers 753, Kyoto University, Institute of Economic Research.
    63. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    64. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, vol. 18(C), pages 123-140.
    65. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    66. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    67. Peter A. Zadrozny, 2015. "Extended Yule-Walker Identification of Varma Models with Single- or Mixed- Frequency Data," Economic Working Papers 485, Bureau of Labor Statistics.
    68. Dufrénot, Gilles & Rhouzlane, Meryem & Vaccaro-Grange, Etienne, 2022. "Potential growth and natural yield curve in Japan," Journal of International Money and Finance, Elsevier, vol. 124(C).
    69. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    70. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    71. Andy Wui Wing Cheng & Iris Wing Han Yip, 2017. "China’s Macroeconomic Fundamentals on Stock Market Volatility: Evidence from Shanghai and Hong Kong," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 20(02), pages 1-57, June.
    72. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    73. David E. Allen & Michael McAleer & Marcel Scharth, 2014. "Asymmetric Realized Volatility Risk," Tinbergen Institute Discussion Papers 14-075/III, Tinbergen Institute.
    74. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese , Giovanni, 2015. "Nowcasting Indonesia," ADB Economics Working Paper Series 471, Asian Development Bank.
    75. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 257-271, January.
    76. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    77. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    78. Ba Chu & Shafiullah Qureshi, 2021. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers 21-12, Carleton University, Department of Economics.
    79. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    80. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    81. Michael P. Clements & Ana Beatriz Galvão, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206, November.
    82. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    83. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
    84. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    85. Mitsuru Igami & Takuo Sugaya, 2022. "Measuring the Incentive to Collude: The Vitamin Cartels, 1990–99 [“Extremal Equilibria of Oligopolistic Supergames”]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 89(3), pages 1460-1494.
    86. Ahiadorme, Johnson Worlanyo, 2020. "Monetary policy transmission and income inequality in Sub-Saharan Africa," MPRA Paper 104084, University Library of Munich, Germany.
    87. Morita, Hiroshi & 森田, 裕史, 2019. "Forecasting Public Investment Using Daily Stock Returns," Discussion paper series HIAS-E-88, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    88. Tony Chernis & Rodrigo Sekkel, 2017. "A dynamic factor model for nowcasting Canadian GDP growth," Empirical Economics, Springer, vol. 53(1), pages 217-234, August.
    89. Jung, Alexander, 2017. "Forecasting broad money velocity," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 421-432.
    90. Fang, Libing & Yu, Honghai & Huang, Yingbo, 2018. "The role of investor sentiment in the long-term correlation between U.S. stock and bond markets," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 127-139.
    91. Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023. "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, vol. 5(2), pages 295-365, June.
    92. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    93. Philip Hans Franses, 2019. "On inflation expectations in the NKPC model," Empirical Economics, Springer, vol. 57(6), pages 1853-1864, December.
    94. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    95. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    96. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    97. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    98. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    99. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    100. Mei, Dexiang & Ma, Feng & Liao, Yin & Wang, Lu, 2020. "Geopolitical risk uncertainty and oil future volatility: Evidence from MIDAS models," Energy Economics, Elsevier, vol. 86(C).
    101. Raul Ibarra & Luis M. Gomez-Zamudio, 2017. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 173-203, April.
    102. Maojun Zhang & Yang Zhao & Jiangxia Nan, 2022. "Economic policy uncertainty and volatility of treasury futures," Review of Derivatives Research, Springer, vol. 25(1), pages 93-107, April.
    103. Petrova, Diana & Trunin, Pavel, 2020. "Revealing the mood of economic agents based on search queries," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 71-87.
    104. Doemeland,Doerte & Estevão,Marcello & Jooste,Charl & Sampi Bravo,James Robert Ezequiel & Tsiropoulos,Vasileios, 2022. "Debt Vulnerability Analysis : A Multi-Angle Approach," Policy Research Working Paper Series 9929, The World Bank.
    105. Golosnoy, Vasyl & Gribisch, Bastian & Liesenfeld, Roman, 2010. "The conditional autoregressive wishart model for multivariate stock market volatility," Economics Working Papers 2010-07, Christian-Albrechts-University of Kiel, Department of Economics.
    106. Jiqian Wang & Rangan Gupta & Oğuzhan Çepni & Feng Ma, 2023. "Forecasting international REITs volatility: the role of oil-price uncertainty," The European Journal of Finance, Taylor & Francis Journals, vol. 29(14), pages 1579-1597, September.
    107. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
    108. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    109. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    110. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    111. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72, October.
    112. Badescu, Alexandru & Quaye, Enoch & Tunaru, Radu, 2022. "On non-negative equity guarantee calculations with macroeconomic variables related to house prices," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 119-138.
    113. Morita, Hiroshi, 2022. "Forecasting GDP growth using stock returns in Japan: A factor-augmented MIDAS approach," Discussion paper series HIAS-E-118, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    114. Deng, Yongheng & Girardin, Eric & Joyeux, Roselyne, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," China Economic Review, Elsevier, vol. 48(C), pages 205-222.
    115. Nikolaos Askitas & Klaus F. Zimmermann, 2013. "Nowcasting Business Cycles Using Toll Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 299-306, July.
    116. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    117. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    118. Valadkhani, Abbas & Smyth, Russell, 2018. "Asymmetric responses in the timing, and magnitude, of changes in Australian monthly petrol prices to daily oil price changes," Energy Economics, Elsevier, vol. 69(C), pages 89-100.
    119. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    120. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    121. Ooft, Gavin & Bhaghoe, Sailesh & Hans Franses, Philip, 2021. "Forecasting annual inflation in Suriname," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    122. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    123. Vincenzo Candila & Denis Maximov & Alexey Mikhaylov & Nikita Moiseev & Tomonobu Senjyu & Nicole Tryndina, 2021. "On the Relationship between Oil and Exchange Rates of Oil-Exporting and Oil-Importing Countries: From the Great Recession Period to the COVID-19 Era," Energies, MDPI, vol. 14(23), pages 1-18, December.
    124. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    125. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    126. O-Chia Chuang & Chenxu Yang, 2022. "Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model," Energies, MDPI, vol. 15(8), pages 1-14, April.
    127. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    128. Anindya Biswas, 2015. "The output gap and inflation in U.S. data: an empirical note," Economics Bulletin, AccessEcon, vol. 35(2), pages 841-845.
    129. Kvedaras, Virmantas & Zemlys, Vaidotas, 2012. "Testing the functional constraints on parameters in regressions with variables of different frequency," Economics Letters, Elsevier, vol. 116(2), pages 250-254.
    130. Ye, Wuyi & Jiang, Kunliang & Liu, Xiaoquan, 2021. "Financial contagion and the TIR-MIDAS model," Finance Research Letters, Elsevier, vol. 39(C).
    131. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    132. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    133. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    134. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    135. James Chapman & Ajit Desai, 2021. "Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19," Staff Working Papers 21-2, Bank of Canada.
    136. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    137. Hirashima, Ashley & Jones, James & Bonham, Carl S. & Fuleky, Peter, 2017. "Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism," Annals of Tourism Research, Elsevier, vol. 63(C), pages 191-202.
    138. Kajal Lahiri & Cheng Yang, 2021. "Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York," CESifo Working Paper Series 9365, CESifo.
    139. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    140. Yu, Honghai & Fang, Libing & Sun, Wencong, 2018. "Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 931-940.
    141. Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2019. "Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 12-45, September.
    142. Skander Slim & Ibrahim Tabche & Yosra Koubaa & Mohamed Osman & Andreas Karathanasopoulos, 2023. "Forecasting realized volatility of Bitcoin: The informative role of price duration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1909-1929, November.
    143. Evzen Kocenda & Karen Poghosyan, 2018. "Nowcasting real GDP growth with business tendency surveys data: A cross country analysis," KIER Working Papers 1002, Kyoto University, Institute of Economic Research.
    144. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    145. Ding, Shusheng & Zheng, Dandan & Cui, Tianxiang & Du, Min, 2023. "The oil price-inflation nexus: The exchange rate pass- through effect," Energy Economics, Elsevier, vol. 125(C).
    146. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    147. Julien Chevallier, 2021. "Covid-19 Outbreak and CO2 Emissions: Macro-Financial Linkages," Working Papers 2021-004, Department of Research, Ipag Business School.
    148. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    149. Bruno Feunou & Mohammad R. Jahan-Parvar & Roméo Tédongap, 2016. "Which parametric model for conditional skewness?," The European Journal of Finance, Taylor & Francis Journals, vol. 22(13), pages 1237-1271, October.
    150. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2015. "The Impacts of Exogenous Oil Supply Shocks on Mediterranean Economies," Working Papers 2015.100, Fondazione Eni Enrico Mattei.
    151. Qifa Xu & Zezhou Wang & Cuixia Jiang & Yezheng Liu, 2023. "Deep learning on mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2099-2120, December.
    152. Biswas, Anindya, 2014. "The output gap and expected security returns," Review of Financial Economics, Elsevier, vol. 23(3), pages 131-140.
    153. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    154. Charfeddine, Lanouar & Klein, Tony & Walther, Thomas, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," QBS Working Paper Series 2018/03, Queen's University Belfast, Queen's Business School.
    155. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    156. Eric Girardin & Roselyne Joyeux, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Post-Print hal-01499615, HAL.
    157. Yin, Libo & Nie, Jing & Han, Liyan, 2021. "Understanding cryptocurrency volatility: The role of oil market shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 233-253.
    158. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2022. "Assessing the impact of policy and regulation interventions in European sovereign credit risk networks: What worked best?," Journal of International Economics, Elsevier, vol. 139(C).
    159. NguyenHuua, Tam & Karaman Örsal, Deniz Dilan, 2019. "A new and benign hegemon on the horizon? The Chinese century and growth in the global South," Economics Discussion Papers 2019-60, Kiel Institute for the World Economy (IfW Kiel).
    160. Qifa Xu & Junqing Zuo & Cuixia Jiang & Yaoyao He, 2021. "A large constrained time‐varying portfolio selection model with DCC‐MIDAS: Evidence from Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3417-3435, July.
    161. Adeniji Sesan Oluseyi & Timilehin John Olasehinde & Gamaliel O. Eweke, 2017. "The Impact of Money Supply on Nigeria Economy: A Comparison of Mixed Data Sampling (MIDAS) and ARDL Approach," EuroEconomica, Danubius University of Galati, issue 2(36), pages 123-134, November.
    162. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    163. Hung-Ming Wu, 2020. "The impact of non-clean energy consumption on economic growth: Evidence from symmetric and asymmetric analyses in the US," Energy & Environment, , vol. 31(2), pages 291-307, March.
    164. Fang, Tong & Su, Zhi & Yin, Libo, 2020. "Economic fundamentals or investor perceptions? The role of uncertainty in predicting long-term cryptocurrency volatility," International Review of Financial Analysis, Elsevier, vol. 71(C).
    165. Chen, Wang & Lu, Xinjie & Wang, Jiqian, 2022. "Modeling and managing stock market volatility using MRS-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 625-635.
    166. Theu Dinh & Stéphane Goutte & Khuong Nguyen & Thomas Walther, 2022. "Economic drivers of volatility and correlation in precious metal markets," Working Papers halshs-03672469, HAL.
    167. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    168. Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2017. "Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 90-101.
    169. Jan Hanousek & Evzen Kocenda & Jan Novotny, 2014. "Price jumps on European stock markets," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 14(1), pages 10-22, March.
    170. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    171. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    172. Maas, Benedikt, 2019. "Short-term forecasting of the US unemployment rate," MPRA Paper 94066, University Library of Munich, Germany.
    173. Esben Hedegaard & Robert J. Hodrick, 2014. "Estimating the Risk-Return Trade-off with Overlapping Data Inference," NBER Working Papers 19969, National Bureau of Economic Research, Inc.
    174. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    175. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    176. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    177. Ghysels, Eric & Wright, Jonathan H., 2009. "Forecasting Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 504-516.
    178. Philip Hans Franses & Eva Janssens, 2017. "Recovering Historical Inflation Data from Postage Stamps Prices," JRFM, MDPI, vol. 10(4), pages 1-11, November.
    179. Kanas, Angelos & Molyneux, Philip, 2020. "Do measures of systemic risk predict U.S. corporate bond default rates?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    180. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    181. Alessandra Amendola & Vincenzo Candila & Fabrizio Cipollini & Giampiero M. Gallo, 2020. "Doubly Multiplicative Error Models with Long- and Short-run Components," Papers 2006.03458, arXiv.org.
    182. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.
    183. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    184. Virk, Nader & Javed, Farrukh & Awartani, Basel, 2021. "A reality check on the GARCH-MIDAS volatility models," Working Papers 2021:2, Örebro University, School of Business.
    185. Wu, Jie & Zhao, Ruizeng & Sun, Jiasen & Zhou, Xuewei, 2023. "Impact of geopolitical risks on oil price fluctuations: Based on GARCH-MIDAS model," Resources Policy, Elsevier, vol. 85(PB).
    186. Chen, Zhonglu & Ye, Yong & Li, Xiafei, 2022. "Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic," Resources Policy, Elsevier, vol. 75(C).
    187. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    188. Deistler, Manfred & Koelbl, Lukas & Anderson, Brian D.O., 2017. "Non-identifiability of VMA and VARMA systems in the mixed frequency case," Econometrics and Statistics, Elsevier, vol. 4(C), pages 31-38.
    189. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    190. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    191. Chaudhuri, Malika & Calantone, Roger J. & Voorhees, Clay M. & Cockrell, Seth, 2018. "Disentangling the effects of promotion mix on new product sales: An examination of disaggregated drivers and the moderating effect of product class," Journal of Business Research, Elsevier, vol. 90(C), pages 286-294.
    192. Jiang, Cuixia & Ding, Xiaoyi & Xu, Qifa & Tong, Yongbo, 2020. "A TVM-Copula-MIDAS-GARCH model with applications to VaR-based portfolio selection," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    193. Ken Miyajima & Jorge A. Chan-Lau & Weimin Miao & Jongsoon Shin, 2017. "Assessing Corporate Vulnerabilities in Indonesia: A Bottom-Up Default Analysis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(4), pages 269-289, December.
    194. Michael Funke & Hao Yu & Aaron Mehrota, 2011. "Tracking Chinese CPI inflation in real time," Quantitative Macroeconomics Working Papers 21112, Hamburg University, Department of Economics.
    195. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    196. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2022. "Policy Uncertainty and Stock Market Volatility Revisited: The Predictive Role of Signal Quality," Working Papers 202232, University of Pretoria, Department of Economics.
    197. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    198. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    199. Pan, Beier, 2023. "The asymmetric dynamics of stock–bond liquidity correlation in China: The role of macro-financial determinants," Economic Modelling, Elsevier, vol. 124(C).
    200. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    201. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
    202. Su, Zhi & Fang, Tong & Yin, Libo, 2019. "Understanding stock market volatility: What is the role of U.S. uncertainty?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 582-590.
    203. Vincenzo Candila, 2021. "Multivariate Analysis of Cryptocurrencies," Econometrics, MDPI, vol. 9(3), pages 1-17, July.
    204. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    205. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    206. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    207. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    208. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
    209. Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.
    210. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    211. Londono Yarce, J.M., 2011. "Essays on asset pricing," Other publications TiSEM 744a2ac5-7ada-4fa8-a7aa-e, Tilburg University, School of Economics and Management.
    212. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    213. Qian, Hang, 2010. "Vector autoregression with varied frequency data," MPRA Paper 34682, University Library of Munich, Germany.
    214. Qian, Hang, 2016. "A computationally efficient method for vector autoregression with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 433-437.
    215. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    216. Michelle T. Armesto & Rub…N Hern¡Ndez-Murillo & Michael T. Owyang & Jeremy Piger, 2009. "Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(1), pages 35-55, February.
    217. Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    218. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Post-Print halshs-04344131, HAL.
    219. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    220. Xuan Lv & Menggang Li & Yingjie Zhang, 2022. "Financial Stability and Economic Activity in China: Based on Mixed-Frequency Spillover Method," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    221. Wink Junior, Marcos Vinício & Pereira, Pedro Luiz Valls, 2011. "Modeling and Forecasting of Realized Volatility: Evidence from Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(2), December.
    222. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    223. Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023. "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 266-278.
    224. Chunpeng Yang & Rengui Zhang, 2014. "Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 966-972, March.
    225. Su, Yuandong & Liang, Chao & Zhang, Li & Zeng, Qing, 2022. "Uncover the response of the U.S grain commodity market on El Niño–Southern Oscillation," International Review of Economics & Finance, Elsevier, vol. 81(C), pages 98-112.
    226. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    227. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    228. Zhang, Jin & Li, Pujiang & Zhao, Guochang, 2018. "Is power generation really the gold measure of the Chinese economy? A conceptual and empirical assessment," Energy Policy, Elsevier, vol. 121(C), pages 211-216.
    229. Frömmel, Michael & Midiliç, Murat, 2021. "Daily currency interventions in an emerging market: Incorporating reserve accumulation to the reaction function," Economic Modelling, Elsevier, vol. 97(C), pages 461-476.
    230. Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.
    231. Jens Hogrefe, 2008. "Forecasting data revisions of GDP: a mixed frequency approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(3), pages 271-296, August.
    232. Zhang, Yuan-Yuan & Zhang, Yue-Jun, 2022. "The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models," International Review of Financial Analysis, Elsevier, vol. 84(C).
    233. Afees A. Salisu & Rangan Gupta, 2019. "How do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Working Papers 201946, University of Pretoria, Department of Economics.
    234. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    235. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
    236. Georgiana-Denisa Banulescu & Bertrand Candelon & Christophe Hurlin & Sébastien Laurent, 2014. "Do We Need Ultra-High Frequency Data to Forecast Variances?," Working Papers halshs-01078158, HAL.
    237. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    238. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    239. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    240. Yimin Yang & Fei Jia & Haoran Li, 2023. "Estimation of Panel Data Models with Mixed Sampling Frequencies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 514-544, June.
    241. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    242. Joe Hirschberg & Jenny Lye, 2021. "Estimating risk premiums for regulated firms when accounting for reference-day variation and high-order moments of return volatility," Environment Systems and Decisions, Springer, vol. 41(3), pages 455-467, September.
    243. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    244. Yu, Xiaoling & Huang, Yirong, 2021. "The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    245. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    246. Naif Alsagr & Stefan F. Van Hemmen Almazor, 2020. "Oil Rent, Geopolitical Risk and Banking Sector Performance," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 305-314.
    247. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    248. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    249. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    250. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals: Google Trends Meets Mixed Frequency Data," MPRA Paper 90205, University Library of Munich, Germany.
    251. Yaojie Zhang & Yudong Wang & Feng Ma & Yu Wei, 2022. "To jump or not to jump: momentum of jumps in crude oil price volatility prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-31, December.
    252. Conrad, Christian & Schienle, Melanie, 2019. "Testing for an omitted multiplicative long-term component in GARCH models," Working Paper Series in Economics 121, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    253. Michael P. Clements & Ana Beatriz Galvão, 2014. "Measuring Macroeconomic Uncertainty: US Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-04, Henley Business School, University of Reading.
    254. Özgür Ömer Ersin & Melike Bildirici, 2023. "Financial Volatility Modeling with the GARCH-MIDAS-LSTM Approach: The Effects of Economic Expectations, Geopolitical Risks and Industrial Production during COVID-19," Mathematics, MDPI, vol. 11(8), pages 1-26, April.
    255. Buse, Rebekka & Schienle, Melanie & Urban, Jörg, 2019. "Effectiveness of policy and regulation in European sovereign credit risk markets: a network analysis," ESRB Working Paper Series 90, European Systemic Risk Board.
    256. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    257. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    258. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    259. Turhan, Ibrahim M. & Sensoy, Ahmet & Hacihasanoglu, Erk, 2015. "Shaping the manufacturing industry performance: MIDAS approach," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 286-290.
    260. Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers 2007.12838, arXiv.org.
    261. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    262. Huang, Xiaozhou & Wang, Yubao & Song, Juan, 2023. "The Chinese oil futures volatility: Evidence from high-low estimator information," Finance Research Letters, Elsevier, vol. 56(C).
    263. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    264. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    265. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    266. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    267. Cleiton Guollo Taufemback, 2023. "Asymptotic Behavior of Temporal Aggregation in Mixed‐Frequency Datasets," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(4), pages 894-909, August.
    268. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    269. Storti, Giuseppe & Wang, Chao, 2022. "Nonparametric expected shortfall forecasting incorporating weighted quantiles," International Journal of Forecasting, Elsevier, vol. 38(1), pages 224-239.
    270. Mei, Dexiang & Xie, Yutang, 2022. "U.S. grain commodity futures price volatility: Does trade policy uncertainty matter?," Finance Research Letters, Elsevier, vol. 48(C).
    271. Bhanu Pratap & Nalin Priyaranjan, 2023. "Macroeconomic effects of uncertainty: a Google trends-based analysis for India," Empirical Economics, Springer, vol. 65(4), pages 1599-1625, October.
    272. Allan, Grant & Koop, Gary & McIntyre, Stuart & Smith, Paul, 2014. "Nowcasting Scottish GDP Growth," SIRE Discussion Papers 2015-08, Scottish Institute for Research in Economics (SIRE).
    273. Alexopoulos, Angelos & Varthalitis, Petros, 2023. "A machine learning approach to construct quarterly data on intangible investment for Eurozone," Economics Letters, Elsevier, vol. 231(C).
    274. Zhang, Hongwei & Hong, Huojun & Ding, Shijie, 2023. "The role of climate policy uncertainty on the long-term correlation between crude oil and clean energy," Energy, Elsevier, vol. 284(C).
    275. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    276. Khoo, Joye & Cheung, Adrian (Wai Kong), 2021. "Does geopolitical uncertainty affect corporate financing? Evidence from MIDAS regression," Global Finance Journal, Elsevier, vol. 47(C).
    277. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    278. Jiang, Cuixia & Li, Yuqian & Xu, Qifa & Liu, Yezheng, 2021. "Measuring risk spillovers from multiple developed stock markets to China: A vine-copula-GARCH-MIDAS model," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 386-398.
    279. Boriss Siliverstovs, 2015. "Dissecting the purchasing managers' index," KOF Working papers 15-376, KOF Swiss Economic Institute, ETH Zurich.
    280. Lindblad, Annika, 2017. "Sentiment indicators and macroeconomic data as drivers for low-frequency stock market volatility," MPRA Paper 80266, University Library of Munich, Germany.
    281. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    282. Mila Andreani & Vincenzo Candila & Giacomo Morelli & Lea Petrella, 2021. "Multivariate Analysis of Energy Commodities during the COVID-19 Pandemic: Evidence from a Mixed-Frequency Approach," Risks, MDPI, vol. 9(8), pages 1-20, August.
    283. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    284. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    285. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, vol. 94(2), pages 233-263, November.
    286. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 30-51.
    287. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    288. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
    289. Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
    290. Hammoudeh, Shawkat & Uddin, Gazi Salah & Sousa, Ricardo M. & Wadström, Christoffer & Sharmi, Rubaiya Zaman, 2022. "Do pandemic, trade policy and world uncertainties affect oil price returns?," Resources Policy, Elsevier, vol. 77(C).
    291. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    292. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    293. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    294. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    295. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    296. Keiichi Goshima & Hiroshi Ishijima & Mototsugu Shintani & Hiroki Yamamoto, 2019. "Forecasting Japanese inflation with a news-based leading indicator of economic activities," CARF F-Series CARF-F-458, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    297. Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
    298. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    299. Virmantas Kvedaras & Alfredas Račkauskas, 2010. "Regression Models with Variables of Different Frequencies: The Case of a Fixed Frequency Ratio," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(5), pages 600-620, October.
    300. Tseng Tseng-Chan & Chung Huimin & Huang Chin-Sheng, 2009. "Modeling Jump and Continuous Components in the Volatility of Oil Futures," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(3), pages 1-30, May.
    301. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    302. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    303. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    304. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    305. Chen, Zhonglu & Zhang, Li & Weng, Chen, 2023. "Does climate policy uncertainty affect Chinese stock market volatility?," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 369-381.
    306. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    307. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    308. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
    309. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    310. Geeta Duppati & Frank Scrimgeour & Anoop S. Kumar, 2019. "Country-level Governance and Capital Markets in Asia-Pacific Region," Indian Journal of Corporate Governance, , vol. 12(2), pages 187-212, December.
    311. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    312. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    313. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    314. Ba Chu & Shafiullah Qureshi, 2023. "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1567-1609, December.
    315. Tang, Wenjin & Ding, Saijie & Chen, Hao, 2021. "Economic uncertainty and its spillover networks: Evidence from the Asia-Pacific countries," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    316. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    317. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    318. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
    319. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
    320. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    321. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    322. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    323. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    324. Riza Demirer & Rangan Gupta & He Li & Yu You, 2021. "Financial Vulnerability and Volatility in Emerging Stock Markets: Evidence from GARCH-MIDAS Models," Working Papers 202112, University of Pretoria, Department of Economics.
    325. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    326. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    327. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    328. Emmanuel Apergis & Nicholas Apergis, 2021. "Can the COVID-19 Pandemic and Oil Prices Drive the US Partisan Conflict Index," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 1(1), pages 1-4.
    329. Clark Lundberg, 2019. "Identifying horizon-based heterogeneity in the cross section of portfolio returns," Economics Bulletin, AccessEcon, vol. 39(2), pages 1163-1175.
    330. Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    331. Khalifa, Ahmed & Caporin, Massimiliano & Di Fonzo, Tommaso, 2019. "Scenario-based forecast for the electricity demand in Qatar and the role of energy efficiency improvements," Energy Policy, Elsevier, vol. 127(C), pages 155-164.
    332. Bruno Deschamps & Tianlun Fei & Ying Jiang & Xiaoquan Liu, 2022. "Procyclical volatility in Chinese stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1117-1144, April.
    333. Crimmel, Jeremy & Elyasiani, Elyas, 2021. "The association between financial market volatility and banking market structure," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 335-349.
    334. Julián Alonso Cárdenas-Cárdenas & Edgar Caicedo-García & Eliana R. González Molano, 2020. "Estimación de la variación del precio de los alimentos con modelos de frecuencias mixtas," Borradores de Economia 1109, Banco de la Republica de Colombia.
    335. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    336. Christian Brownlees & Benjamin Chabot & Eric Ghysels & Christopher J. Kurz, 2015. "Backtesting Systemic Risk Measures During Historical Bank Runs," Working Paper Series WP-2015-9, Federal Reserve Bank of Chicago.
    337. Gong, Yuting & Chen, Qiang & Liang, Jufang, 2018. "A mixed data sampling copula model for the return-liquidity dependence in stock index futures markets," Economic Modelling, Elsevier, vol. 68(C), pages 586-598.
    338. Anindya Biswas, 2014. "The output gap and expected security returns," Review of Financial Economics, John Wiley & Sons, vol. 23(3), pages 131-140, September.
    339. Christian Dreger & Konstantin Arkadievich Kholodilin, 2013. "Forecasting Private Consumption by Consumer Surveys," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 10-18, January.
    340. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    341. Zhang Wu & Terence Tai-Leung Chong, 2021. "Does the macroeconomy matter to market volatility? Evidence from US industries," Empirical Economics, Springer, vol. 61(6), pages 2931-2962, December.
    342. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.
    343. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers 198, Society for Economic Dynamics.
    344. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    345. Tumala, Mohammed M. & Salisu, Afees A. & Atoi, Ngozi V., 2022. "Oil-growth nexus in Nigeria: An ADL-MIDAS approach," Resources Policy, Elsevier, vol. 77(C).
    346. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    347. Peng-Fei Dai & Xiong Xiong & Toan Luu Duc Huynh & Jiqiang Wang, 2020. "The impact of economic policy uncertainties on the volatility of European carbon market," Papers 2007.10564, arXiv.org, revised Aug 2021.
    348. Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2022. "Belief Distortions and Macroeconomic Fluctuations," American Economic Review, American Economic Association, vol. 112(7), pages 2269-2315, July.
    349. Denisa BANULESCU-RADU & Laurent FERRARA & Clément MARSILLI, 2019. "Prévoir la volatilité d’un actif financier à l’aide d’un modèle à mélange de fréquences," LEO Working Papers / DR LEO 2710, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    350. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    351. Lukas Koelbl & Manfred Deistler, 2020. "A new approach for estimating VAR systems in the mixed-frequency case," Statistical Papers, Springer, vol. 61(3), pages 1203-1212, June.
    352. Yu, Honghai & Fang, Libing & Du, Donglei & Yan, Panpan, 2017. "How EPU drives long-term industry beta," Finance Research Letters, Elsevier, vol. 22(C), pages 249-258.
    353. Wang, Ruina & Li, Jinfang, 2021. "The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    354. Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
    355. Berger, Philip G., 2011. "Challenges and opportunities in disclosure research—A discussion of ‘the financial reporting environment: Review of the recent literature’," Journal of Accounting and Economics, Elsevier, vol. 51(1), pages 204-218.
    356. Laine, Olli-Matti & Lindblad, Annika, 2020. "Nowcasting Finnish GDP growth using financial variables: a MIDAS approach," BoF Economics Review 4/2020, Bank of Finland.
    357. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    358. Yao, Can-Zhong & Li, Min-Jian, 2023. "GARCH-MIDAS-GAS-copula model for CoVaR and risk spillover in stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 66(C).
    359. Cattivelli, Luca & Pirino, Davide, 2019. "A SHARP model of bid–ask spread forecasts," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1211-1225.
    360. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    361. Liu, Yang & Han, Liyan & Yin, Libo, 2019. "News implied volatility and long-term foreign exchange market volatility," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 126-142.
    362. Pan, Zhiyuan & Huang, Xiao & Liu, Li & Huang, Juan, 2023. "Geopolitical uncertainty and crude oil volatility: Evidence from oil-importing and oil-exporting countries," Finance Research Letters, Elsevier, vol. 52(C).
    363. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.
    364. Guo, Kun & Liu, Fengqi & Sun, Xiaolei & Zhang, Dayong & Ji, Qiang, 2023. "Predicting natural gas futures’ volatility using climate risks," Finance Research Letters, Elsevier, vol. 55(PA).
    365. LUPU, Radu & CALIN, Adrian Cantemir, 2014. "A Mixed Frequency Analysis Of Connections Between Macroeconomic Variables And Stock Markets In Central And Eastern Europe," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 18(2), pages 69-79.
    366. Vasyl Golosnoy & Yarema Okhrin, 2015. "Using information quality for volatility model combinations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1055-1073, June.
    367. Zhao, Ling, 2023. "Global economic policy uncertainty and oil futures volatility prediction," Finance Research Letters, Elsevier, vol. 54(C).
    368. Angelos Kanas & Panagiotis D. Zervopoulos, 2020. "Systemic risk-shifting in U.S. commercial banking," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 517-539, February.
    369. Thomas Walther & Lanouar Charfeddine & Tony Klein, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," Working Papers on Finance 1816, University of St. Gallen, School of Finance.
    370. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    371. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    372. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    373. Shuting Liu & Qifa Xu & Cuixia Jiang, 2021. "Systemic risk of China’s commercial banks during financial turmoils in 2010-2020: A MIDAS-QR based CoVaR approach," Applied Economics Letters, Taylor & Francis Journals, vol. 28(18), pages 1600-1609, October.
    374. Franses, Ph.H.B.F., 2016. "Yet another look at MIDAS regression," Econometric Institute Research Papers EI2016-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    375. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    376. Hanoma, Ahmed & Nautz, Dieter, 2018. "The information content of inflation swap rates for the long-term inflation expectations of professionals: Evidence from a MIDAS analysis," Discussion Papers 2018/16, Free University Berlin, School of Business & Economics.
    377. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
    378. Marc Francke & Alex Van De Minne, 2022. "Daily appraisal of commercial real estate a new mixed frequency approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(5), pages 1257-1281, September.
    379. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    380. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    381. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.
    382. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.
    383. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.
    384. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
    385. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.
    386. Holmberg, Johan, 2021. "Earnings and Employment Dynamics: Capturing Cyclicality using Mixed Frequency Data," Umeå Economic Studies 991, Umeå University, Department of Economics.

  5. Hong, Harrison & Torous, Walter & Valkanov, Rossen, 2007. "Do industries lead stock markets?," Journal of Financial Economics, Elsevier, vol. 83(2), pages 367-396, February.

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    2. Huszár, Zsuzsa R. & Tan, Ruth S.K. & Zhang, Weina, 2017. "Do short sellers exploit industry information?," Journal of Empirical Finance, Elsevier, vol. 41(C), pages 118-139.
    3. Azi Ben‐Rephael & Bruce I. Carlin & Zhi Da & Ryan D. Israelsen, 2021. "Information Consumption and Asset Pricing," Journal of Finance, American Finance Association, vol. 76(1), pages 357-394, February.
    4. Li, Xiyang & Chen, Xiaoyue & Li, Bin & Singh, Tarlok & Shi, Kan, 2022. "Predictability of stock market returns: New evidence from developed and developing countries," Global Finance Journal, Elsevier, vol. 54(C).
    5. Xue, Wen-Jun & Zhang, Li-Wen, 2017. "Stock return autocorrelations and predictability in the Chinese stock market—Evidence from threshold quantile autoregressive models," Economic Modelling, Elsevier, vol. 60(C), pages 391-401.
    6. Li, Wei & Luo, Yulei & Nie, Jun, 2017. "Elastic attention, risk sharing, and international comovements," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 1-20.
    7. Robin Greenwood & Andrei Shleifer & Yang You, 2017. "Bubbles for Fama," NBER Working Papers 23191, National Bureau of Economic Research, Inc.
    8. Paresh K. Narayan & Rangan Gupta, 2014. "Has Oil Pirce Predicted Stock Returns for Over a Century?," Working Papers 201446, University of Pretoria, Department of Economics.
    9. Thierry Foucault & Laurent Fresard, 2014. "Learning from peers' stock prices and corporate investment," Post-Print hal-00977071, HAL.
    10. Gounopoulos, Dimitrios & Mazouz, Khelifa & Wood, Geoffrey, 2021. "The consequences of political donations for IPO premium and performance," Journal of Corporate Finance, Elsevier, vol. 67(C).
    11. Esther Eiling & Raymond Kan & Ali Sharifkhani, 2018. "Sectoral Labor Reallocation and Return Predictability," Working Papers 2018-006, Human Capital and Economic Opportunity Working Group.
    12. Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020. "Distillation of News Flow into Analysis of Stock Reactions," Papers 2009.10392, arXiv.org.
    13. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," SAFE Working Paper Series 256, Leibniz Institute for Financial Research SAFE.
    14. Turhan, M. Ibrahim & Sensoy, Ahmet & Ozturk, Kevser & Hacihasanoglu, Erk, 2014. "A view to the long-run dynamic relationship between crude oil and the major asset classes," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 286-299.
    15. van Zundert, Jeroen & Driessen, Joost, 2022. "Stocks versus corporate bonds: A cross-sectional puzzle," Journal of Banking & Finance, Elsevier, vol. 137(C).
    16. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02312186, HAL.
    17. Amado Peiró, 2016. "Cross-autocorrelations in European stock returns," Economics and Business Letters, Oviedo University Press, vol. 5(1), pages 30-37.
    18. Wang, Wenzhao & Su, Chen & Duxbury, Darren, 2021. "Investor sentiment and stock returns: Global evidence," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 365-391.
    19. Albuquerque, Rui & Ramadorai, Tarun & Watugala, Sumudu W., 2015. "Trade credit and cross-country predictable firm returns," Journal of Financial Economics, Elsevier, vol. 115(3), pages 592-613.
    20. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    21. Thomas Gilbert & Shimon Kogan & Lars Lochstoer & Ataman Ozyildirim, 2007. "Investor Inattention and the Market Impact of Summary Statistics," GSIA Working Papers 2006-E24, Carnegie Mellon University, Tepper School of Business.
    22. Felix Haase & Matthias Neuenkirch, 2020. "Predictability of Bull and Bear Markets: A New Look at Forecasting Stock Market Regimes (and Returns) in the US," Research Papers in Economics 2020-01, University of Trier, Department of Economics.
    23. Goto, Shingo & Xiao, Gang & Xu, Yan, 2015. "As told by the supplier: Trade credit and the cross section of stock returns," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 296-309.
    24. Sun, Kaisi & Wang, Hui & Zhu, Yifeng, 2022. "How is the change in left-tail risk priced in China?," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
    25. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    26. Yu, Miao & Hu, Xiaolu & Zhong, Angel, 2023. "Trade links and return predictability: The Australian evidence," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    27. Rizvi, Syed Aun R. & Arshad, Shaista, 2018. "Understanding time-varying systematic risks in Islamic and conventional sectoral indices," Economic Modelling, Elsevier, vol. 70(C), pages 561-570.
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    29. Lee, Charles M. C. Lee & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2017. "Technological Links and Predictable Returns," Research Papers repec:ecl:stabus:3605, Stanford University, Graduate School of Business.
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    41. Huang, Jiekun, 2018. "The customer knows best: The investment value of consumer opinions," Journal of Financial Economics, Elsevier, vol. 128(1), pages 164-182.
    42. Gilbert, Thomas, 2011. "Information aggregation around macroeconomic announcements: Revisions matter," Journal of Financial Economics, Elsevier, vol. 101(1), pages 114-131, July.
    43. Kim, Min-Su & Kim, Woojin & Lee, Dong Wook, 2015. "Stock return commonality within business groups: Fundamentals or sentiment?," Pacific-Basin Finance Journal, Elsevier, vol. 35(PA), pages 198-224.
    44. Dogah, Kingsley E. & Premaratne, Gamini, 2018. "Sectoral exposure of financial markets to oil risk factors in BRICS countries," Energy Economics, Elsevier, vol. 76(C), pages 228-256.
    45. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    46. Warren Thomson, 2016. "Influence of market states on industry returns," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 119-134, March.
    47. Leng, Tiecheng & Liu, Ying & Xiao, Yi & Hou, Chunxiao, 2023. "Does firm financialization affect optimal real investment decisions? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    48. Chen, Huaizhi & Cohen, Lauren & Gurun, Umit & Lou, Dong & Malloy, Christopher, 2020. "IQ from IP: Simplifying search in portfolio choice," Journal of Financial Economics, Elsevier, vol. 138(1), pages 118-137.
    49. Cohen, Lauren & Lou, Dong, 2011. "Complicated firms," LSE Research Online Documents on Economics 119066, London School of Economics and Political Science, LSE Library.
    50. Nicholas Apergis & Vasilios Plakandaras & Ioannis Pragidis, 2022. "Industry momentum and reversals in stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3093-3138, July.
    51. Fan Zhang & Paresh Kumar Narayan & Neluka Devpura, 2021. "Has COVID-19 changed the stock return-oil price predictability pattern?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-10, December.
    52. Ciner, Cetin, 2019. "Do industry returns predict the stock market? A reprise using the random forest," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 152-158.
    53. Narongdech Thakerngkiat & Hung T. Nguyen & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2021. "Do accounting information and market environment matter for cross‐asset predictability?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4389-4434, September.
    54. Ratti, Ronald A. & Hasan, M. Zahid, 2013. "Oil Price Shocks and Volatility in Australian Stock Returns ‎," MPRA Paper 49043, University Library of Munich, Germany.
    55. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    56. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2015. "International Stock Return Predictability: Is the Role of U.S. Time-Varying?," Working Papers 201524, University of Pretoria, Department of Economics.
    57. Zhu, Hui, 2014. "Implications of limited investor attention to customer–supplier information transfers," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(3), pages 405-416.
    58. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    59. Haesen, Daniel & Houweling, Patrick & van Zundert, Jeroen, 2017. "Momentum spillover from stocks to corporate bonds," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 28-41.
    60. Zareei, Abalfazl, 2021. "Cross-momentum: Tracking idiosyncratic shocks," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 177-199.
    61. Wen-Jun Xue & Li-Wen Zhang, 2016. "Stock Return Autocorrelations and Predictability in the Chinese Stock Market: Evidence from Threshold Quantile Autoregressive Models," Working Papers 1605, Florida International University, Department of Economics.
    62. Laborda, Ricardo & Olmo, Jose, 2021. "Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic," Research in International Business and Finance, Elsevier, vol. 57(C).
    63. Hsiu-Lang Chen, 2018. "Information diffusion of upstream and downstream industry-wide earnings surprises and its implications," Review of Quantitative Finance and Accounting, Springer, vol. 51(3), pages 751-784, October.
    64. Chou, Pin-Huang & Ho, Po-Hsin & Ko, Kuan-Cheng, 2012. "Do industries matter in explaining stock returns and asset-pricing anomalies?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 355-370.
    65. Smith, L. Vanessa & Yamagata, Takashi, 2011. "Firm level return–volatility analysis using dynamic panels," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 847-867.
    66. Alizadeh, Amir H. & Muradoglu, Gulnur, 2014. "Stock market efficiency and international shipping-market information," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 445-461.
    67. Mengxi He & Yudong Wang & Yaojie Zhang, 2023. "The predictability of iron ore futures prices: A product‐material lead–lag effect," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(9), pages 1289-1304, September.
    68. Chung, Chune Young & Liu, Chang & Wang, Kainan, 2021. "The big picture: The industry effect of short interest," International Review of Financial Analysis, Elsevier, vol. 76(C).
    69. Hafner, Christian & Wang, Linqi, 2020. "Dynamic portfolio selection with sector-specific regularization," LIDAM Discussion Papers ISBA 2020032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    70. José Afonso Faias & Juan Arismendi Zambrano, 2022. "Equity Risk Premium Predictability from Cross-Sectoral Downturns [International asset allocation with regime shifts]," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 12(3), pages 808-842.
    71. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    72. Chi Dong & Hooi Hooi Lean & Zamri Ahmad & Wing-Keung Wong, 2019. "The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    73. Luo, Yulei & Nie, Jun & Wang, Gaowang & Young, Eric R., 2017. "Rational inattention and the dynamics of consumption and wealth in general equilibrium," Journal of Economic Theory, Elsevier, vol. 172(C), pages 55-87.
    74. Alessandro Carretta & Vincenzo Farina & Elvira Anna Graziano & Marco Reale, 2013. "Does Investor Attention Influence Stock Market Activity? The Case of Spin-Off Deals," Palgrave Macmillan Studies in Banking and Financial Institutions, in: Alessandro Carretta & Gianluca Mattarocci (ed.), Asset Pricing, Real Estate and Public Finance over the Crisis, chapter 1, pages 7-24, Palgrave Macmillan.
    75. Jangkoo Kang & Kyung Yoon Kwon, 2020. "Can commodity futures risk factors predict economic growth?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(12), pages 1825-1860, December.
    76. Marco Di Maggio & Marco Pagano, 2012. "Financial Disclosure and Market Transparency with Costly Information Processing," EIEF Working Papers Series 1212, Einaudi Institute for Economics and Finance (EIEF), revised May 2014.
    77. Gao, George P. & Moulton, Pamela C. & Ng, David T., 2017. "Institutional ownership and return predictability across economically unrelated stocks," Journal of Financial Intermediation, Elsevier, vol. 31(C), pages 45-63.
    78. Alldredge, Dallin M. & Cicero, David C., 2015. "Attentive insider trading," Journal of Financial Economics, Elsevier, vol. 115(1), pages 84-101.
    79. Hirshleifer, David & Kewei Hou & Teoh, Siew Hong & Yinglei Zhang, 2004. "Do investors overvalue firms with bloated balance sheets?," Journal of Accounting and Economics, Elsevier, vol. 38(1), pages 297-331, December.
    80. Da, Zhi & Warachka, Mitch, 2011. "The disparity between long-term and short-term forecasted earnings growth," Journal of Financial Economics, Elsevier, vol. 100(2), pages 424-442, May.
    81. Harrison Hong & Frank Weikai Li & Jiangmin Xu, 2016. "Climate Risks and Market Efficiency," NBER Working Papers 22890, National Bureau of Economic Research, Inc.
    82. Teresa Czerwinska, 2012. "The effectiveness of Social Responsible Investment on the stock market (Efektywnosc inwestycji spolecznie odpowiedzialnych na rynku akcji)," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 10(39), pages 129-140.
    83. Chenchen Li & Rui Li & Xundi Diao & Chongfeng Wu, 2020. "Market segmentation and supply‐chain predictability: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1531-1562, June.
    84. Francesca Carrieri & Vihang Errunza & Sergei Sarkissian, 2012. "The Dynamics of Geographic versus Sectoral Diversification: Is There a Link to the Real Economy?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(04), pages 1-41.
    85. Singh, Vikkram & Roca, Eduardo & Li, Bin, 2021. "Effectiveness of policy interventions during financial crises in China and Russia: Lessons for the COVID-19 pandemic," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 253-277.
    86. Gultekin Isiklar, 2005. "Structural VAR identification in asset markets using short-run market inefficiencies," Econometrics 0501001, University Library of Munich, Germany, revised 02 Jan 2005.
    87. Tse, Yiuman, 2015. "Do industries lead stock markets? A reexamination," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 195-203.
    88. Andrey Kudryavtsev, 2021. "The Correlation Between Stock Returns Before And After Analyst Recommendation Revisions," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 66(228), pages 69-100, January –.
    89. Boulatov, Alex & Hatch, Brian C. & Johnson, Shane A. & Lei, Adam Y.C., 2009. "Dealer attention, the speed of quote adjustment to information, and net dealer revenue," Journal of Banking & Finance, Elsevier, vol. 33(8), pages 1531-1542, August.
    90. Hyoung-Goo Kang & Kyounghun Bae & Jung Ah Shin & Seongmin Jeon, 2021. "Will data on internet queries predict the performance in the marketplace: an empirical study on online searches and IPO stock returns," Electronic Commerce Research, Springer, vol. 21(1), pages 101-124, March.
    91. Hirshleifer, David & Teoh, Siew Hong, 2005. "Limited Investor Attention and Stock Market Misreactions to Accounting Information," Working Paper Series 2005-24, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    92. Ferrer Fernández, María & Henry, Ólan & Pybis, Sam & Stamatogiannis, Michalis P., 2023. "Can we forecast better in periods of low uncertainty? The role of technical indicators," Journal of Empirical Finance, Elsevier, vol. 71(C), pages 1-12.
    93. Sharifkhani, Ali & Simutin, Mikhail, 2021. "Feedback loops in industry trade networks and the term structure of momentum profits," Journal of Financial Economics, Elsevier, vol. 141(3), pages 1171-1187.
    94. Javadi, Siamak & Masum, Abdullah-Al, 2021. "The impact of climate change on the cost of bank loans," Journal of Corporate Finance, Elsevier, vol. 69(C).
    95. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.
    96. Wang, Yudong & Pan, Zhiyuan & Wu, Chongfeng & Wu, Wenfeng, 2020. "Industry equi-correlation: A powerful predictor of stock returns," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 1-24.
    97. Matteo Bonato & Riza Demirer & Rangan Gupta, 2016. "The Predictive Power of Industrial Electricity Usage Revisited: Evidence from Nonparametric Causality Tests," Working Papers 201679, University of Pretoria, Department of Economics.
    98. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    99. Obinna Franklin Ezeibekwe, 2020. "Financial Development And Economic Growth In The Presence Of Simultaneity Bias: Panel Data Evidence," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 25, pages 47-67, June.
    100. Loh, Roger, 2008. "Investor Attention and the Underreaction to Stock Recommendations," Working Paper Series 2008-2, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    101. George M. Korniotis & Alok Kumar & Jeremy K. Page, 2020. "Investor sophistication and asset prices," Review of Financial Economics, John Wiley & Sons, vol. 38(4), pages 557-579, October.
    102. Guillaume Coqueret, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02000726, HAL.
    103. Yun‐Huan Lee & Tzu‐Hsiang Liao & Hsiu‐Chuan Lee, 2022. "Overnight returns of industry exchange‐traded funds, investor sentiment, and futures market returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1114-1134, June.
    104. Abhiroop Mukherjee & George Panayotov & Janghoon Shon, 2019. "Eye in the sky: private satellites and government macro data," HKUST IEMS Working Paper Series 2019-68, HKUST Institute for Emerging Market Studies, revised Oct 2019.
    105. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    106. Lauren Cohen & Dong Lou, 2011. "Complicated Firms," FMG Discussion Papers dp683, Financial Markets Group.
    107. Milot Hasaj & Bernd Scherer, 2021. "Covid-19 and smart beta," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 515-532, December.
    108. Bams, Dennis & Blanchard, Gildas & Honarvar, Iman & Lehnert, Thorsten, 2017. "Does oil and gold price uncertainty matter for the stock market?," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 270-285.
    109. Chen, Huaizhi & Cohen, Lauren & Gurun, Umit & Lou, Dong & Malloy, Christopher, 2020. "IQ from IP: simplifying search in portfolio choice," LSE Research Online Documents on Economics 101133, London School of Economics and Political Science, LSE Library.
    110. David Hirshleifer & Sonya Seongyeon Lim & Siew Hong Teoh, 2009. "Driven to Distraction: Extraneous Events and Underreaction to Earnings News," Journal of Finance, American Finance Association, vol. 64(5), pages 2289-2325, October.
    111. Elbannan, Mohamed A. & Elbannan, Mona A., 2015. "Information content of SFAS 157 fair value reporting," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 25(C), pages 31-45.
    112. Xiao, Jihong & Hu, Chunyan & Ouyang, Guangda & Wen, Fenghua, 2019. "Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 80(C), pages 297-309.
    113. Ben Jacobsen & Ben R. Marshall & Nuttawat Visaltanachoti, 2019. "Stock Market Predictability and Industrial Metal Returns," Management Science, INFORMS, vol. 65(7), pages 3026-3042, July.
    114. Ikhlaas Gurrib & Firuz Kamalov & Elgilani E. Alshareif, 2022. "High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 441-456, September.
    115. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    116. Xiao, Jihong & Wen, Fenghua & Zhao, Yupei & Wang, Xiong, 2021. "The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 311-333.
    117. Killins, Robert N. & Egly, Peter V. & Batabyal, Sourav, 2021. "The impact of the yield curve on bank equity returns: Evidence from Canada," The Quarterly Review of Economics and Finance, Elsevier, vol. 81(C), pages 319-329.
    118. Thomas Boulton & Bill B. Francis & Thomas Shohfi & Daqi Xin, 2021. "Investor awareness or information asymmetry? Wikipedia and IPO underpricing," The Financial Review, Eastern Finance Association, vol. 56(3), pages 535-561, August.
    119. Lou, Dong, 2013. "Attracting investor attention through advertising," LSE Research Online Documents on Economics 54382, London School of Economics and Political Science, LSE Library.
    120. L. D. Zubkova & S. M. D’yachkov, 2018. "Industry Investment Analysis of Activities of Russian Telecommunications Companies," Studies on Russian Economic Development, Springer, vol. 29(2), pages 182-190, March.
    121. Marie Briere & Ariane Szafarz, 2021. "When it Rains, it Pours: Multifactor Asset Management in Good and Bad Times," Working Papers CEB 21-002, ULB -- Universite Libre de Bruxelles.
    122. Victor Troster & José Penalva & Abderrahim Taamouti & Dominik Wied, 2021. "Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1291-1309, November.
    123. Xin Wang & Haofei Zhang, 2023. "The cross‐predictability of industry returns in international financial markets," International Review of Finance, International Review of Finance Ltd., vol. 23(4), pages 859-885, December.
    124. Gaye GENCER & Sercan DEMIRALAY, 2013. "The impact of oil prices on sectoral returns: an empirical analysis from Borsa Istanbul," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(12(589)), pages 7-24, December.
    125. Nina Boyarchenko, 2012. "Information acquisition and financial intermediation," Staff Reports 571, Federal Reserve Bank of New York.
    126. Boyao Wu & Difang Huang & Muzi Chen, 2023. "Estimating contagion mechanism in global equity market with time‐zone effect," Financial Management, Financial Management Association International, vol. 52(3), pages 543-572, September.
    127. Coqueret, Guillaume, 2017. "Empirical properties of a heterogeneous agent model in large dimensions," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 180-201.
    128. Robert N. Killins & Haiwei Chen, 2022. "The impact of the yield curve on the equity returns of insurance companies," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1134-1153, January.
    129. Gelman, Sergey & Burhop, Carsten, 2008. "Taxation, regulation and the information efficiency of the Berlin stock exchange, 1892–1913," European Review of Economic History, Cambridge University Press, vol. 12(1), pages 39-66, April.
    130. Hirshleifer, David & Hsu, Po-Hsuan & Li, Dongmei, 2013. "Innovative efficiency and stock returns," Journal of Financial Economics, Elsevier, vol. 107(3), pages 632-654.
    131. Chava, Sudheer & Hsu, Alex & Zeng, Linghang, 2020. "Does history repeat itself? Business cycle and industry returns," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 201-218.
    132. Nuno Silva, 2015. "Industry based equity premium forecasts," GEMF Working Papers 2015-19, GEMF, Faculty of Economics, University of Coimbra.
    133. Roger K. Loh, 2010. "Investor Inattention and the Underreaction to Stock Recommendations," Financial Management, Financial Management Association International, vol. 39(3), pages 1223-1252, September.
    134. Conlon, Thomas & Cotter, John & Gençay, Ramazan, 2018. "Long-run wavelet-based correlation for financial time series," European Journal of Operational Research, Elsevier, vol. 271(2), pages 676-696.
    135. Tolikas, Konstantinos & Topaloglou, Nikolas, 2017. "Is default risk priced equally fast in the credit default swap and the stock markets? AN empirical investigation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 39-57.
    136. Yanying Zhang & Yiuman Tse & Gaiyan Zhang, 2022. "Return predictability between industries and the stock market in China," Pacific Economic Review, Wiley Blackwell, vol. 27(2), pages 194-220, May.
    137. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Oil price and stock returns of consumers and producers of crude oil," Working Papers fe_2015_12, Deakin University, Department of Economics.
    138. Lambertides, Neophytos & Savva, Christos S. & Tsouknidis, Dimitris A., 2017. "The effects of oil price shocks on U.S. stock order flow imbalances and stock returns," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 137-146.
    139. Bruzda, Joanna, 2019. "Complex analytic wavelets in the measurement of macroeconomic risks," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    140. Chen, Huaizhi & Cohen, Lauren & Lou, Dong, 2013. "Industry window dressing," LSE Research Online Documents on Economics 119035, London School of Economics and Political Science, LSE Library.
    141. Gregor Dorfleitner & Felix Rößle, 2018. "The financial performance of the health care industry: a global, regional and industry specific empirical investigation," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(4), pages 585-594, May.
    142. Kim, Woo Chang & Kim, Jang Ho & Mulvey, John M. & Fabozzi, Frank J., 2015. "Focusing on the worst state for robust investing," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 19-31.
    143. Bülent Köksal & Ahmet Çalışkan, 2012. "Political Business Cycles and Partisan Politics: Evidence from a Developing Economy," Economics and Politics, Wiley Blackwell, vol. 24(2), pages 182-199, July.
    144. Ben R. Marshall & Nuttawat Visaltanachoti & Genevieve Cooper, 2014. "Sell the rumour, buy the fact?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 54(1), pages 237-249, March.
    145. Fan, Qinbin & Jahan-Parvar, Mohammad R., 2012. "U.S. industry-level returns and oil prices," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 112-128.
    146. Yuan, Yu, 2015. "Market-wide attention, trading, and stock returns," Journal of Financial Economics, Elsevier, vol. 116(3), pages 548-564.
    147. Yabei Zhu & Xingguo Luo & Qi Xu, 2023. "Industry variance risk premium, cross‐industry correlation, and expected returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(1), pages 3-32, January.
    148. Lauren Cohen & Umit G. Gurun & Christopher J. Malloy, 2012. "Resident Networks and Firm Trade," NBER Working Papers 18312, National Bureau of Economic Research, Inc.
    149. Sirio Aramonte & Mohammad Jahan-Parvar & Justin Shugarman, 2015. "Institutions and return predictability in oil-exporting countries," Finance and Economics Discussion Series 2015-14, Board of Governors of the Federal Reserve System (U.S.).
    150. Narayan, Paresh Kumar & Ali Ahmed, Huson & Sharma, Susan Sunila & Prabheesh, K. P., 2014. "How profitable is the Indian stock market?," Working Papers fe_2014_14, Deakin University, Department of Economics.
    151. Sharma, Susan Sunila & Narayan, Paresh Kumar, 2014. "New evidence on turn-of-the-month effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 29(C), pages 92-108.
    152. He, Mengxi & Zhang, Yaojie, 2022. "Climate policy uncertainty and the stock return predictability of the oil industry," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    153. Lee, Chien-Chiang & Chen, Mei-Ping & Chang, Chi-Hung, 2013. "Dynamic relationships between industry returns and stock market returns," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 119-144.
    154. Laopodis, Nikiforos T., 2016. "Industry returns, market returns and economic fundamentals: Evidence for the United States," Economic Modelling, Elsevier, vol. 53(C), pages 89-106.
    155. Naidu, Dharmendra & Ranjeeni, Kumari, 2021. "Effect of coronavirus fear on the performance of Australian stock returns: Evidence from an event study," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    156. Nicholas Apergis & James E. Payne, 2013. "New Evidence on the Information and Predictive Content of the Baltic Dry Index," IJFS, MDPI, vol. 1(3), pages 1-19, July.
    157. Wu, Yuliang & Mazouz, Khelifa, 2016. "Long-term industry reversals," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 236-250.
    158. Yajie Chen & Qinlin Zhong & Fuxiu Jiang, 2020. "The capital market spillover effect of product market advertising: Evidence from stock price synchronicity," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-21, December.
    159. Dawar, Ishaan & Dutta, Anupam & Bouri, Elie & Saeed, Tareq, 2021. "Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression," Renewable Energy, Elsevier, vol. 163(C), pages 288-299.
    160. Chen, Tzu-Ying & Tsai, An-Mei & Tzeng, Larry Y., 2022. "Revisiting almost marginal conditional stochastic dominance," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 260-269.
    161. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    162. Rizova, Savina, 2013. "Trade momentum," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 258-293.
    163. Makarov, Igor & Papanikolaou, D., 2008. "Sources of systematic risk," LSE Research Online Documents on Economics 53906, London School of Economics and Political Science, LSE Library.
    164. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
    165. Axel Per Hedström & Gazi Salah Uddin & Md Lutfur Rahman & Bo Sjö, 2024. "Systemic risk in the Scandinavian banking sector," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 581-608, January.
    166. Ling-Ni Boon & Florian Ielpo, 2016. "An anatomy of global risk premiums," Journal of Asset Management, Palgrave Macmillan, vol. 17(4), pages 229-243, July.
    167. Marshall, Ben R. & Visaltanachoti, Nuttawat, 2010. "The Other January Effect: Evidence against market efficiency?," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2413-2424, October.
    168. Sarwar, Ghulam, 2014. "U.S. stock market uncertainty and cross-market European stock returns," Journal of Multinational Financial Management, Elsevier, vol. 28(C), pages 1-14.
    169. Driesprong, Gerben & Jacobsen, Ben & Maat, Benjamin, 2008. "Striking oil: Another puzzle?," Journal of Financial Economics, Elsevier, vol. 89(2), pages 307-327, August.
    170. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    171. Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
    172. Yu-Hau Hu & Shun-Jen Hsueh, 2013. "A Study of yhe Nonlinear Relationships among the U.S. and Asian Stock Markets during Financial Crises," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 134-147, December.
    173. Paulo Silva, 2015. "The information content of the open interest of credit default swaps," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 381-427, November.
    174. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2014. "Firm return volatility and economic gains: The role of oil prices," Economic Modelling, Elsevier, vol. 38(C), pages 142-151.
    175. Tatyana Marchuk & Christian Schlag & Mariano Croce, 2017. "The Leading Premium," 2017 Meeting Papers 1251, Society for Economic Dynamics.
    176. Michaely, Roni & Rubin, Amir & Vedrashko, Alexander, 2016. "Are Friday announcements special? Overcoming selection bias," Journal of Financial Economics, Elsevier, vol. 122(1), pages 65-85.
    177. Ivan Contreras & J. Ignacio Hidalgo & Laura Nuñez, 2018. "Exploring the influence of industries and randomness in stock prices," Empirical Economics, Springer, vol. 55(2), pages 713-729, September.
    178. Sheng Fang & Paul Egan, 2021. "Tail dependence between oil prices and China's A‐shares: Evidence from firm‐level data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1469-1487, January.
    179. Oh, Jong-Min, 2017. "Absorptive capacity, technology spillovers, and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 85(C), pages 146-164.
    180. Wang, Hanjie & Feil, Jan-Henning & Yu, Xiaohua, 2021. "Disagreement on sunspots and soybeans futures price," Economic Modelling, Elsevier, vol. 95(C), pages 385-393.
    181. Killins, Robert N., 2020. "The impact of oil on equity returns of Canadian and U.S. Railways and airlines," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    182. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    183. van Zundert, Jeroen, 2018. "Empirical studies on the cross-section of corporate bond and stock markets," Other publications TiSEM 338205fc-a031-4e06-a636-9, Tilburg University, School of Economics and Management.
    184. Yaojie Zhang & Yudong Wang & Feng Ma, 2021. "Forecasting US stock market volatility: How to use international volatility information," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 733-768, August.
    185. Ellington, Michael & Stamatogiannis, Michalis P. & Zheng, Yawen, 2022. "A study of cross-industry return predictability in the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 83(C).
    186. Jasleen Kaur & Khushdeep Dharni, 2022. "Assessing efficacy of association rules for predicting global stock indices," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 329-339, September.
    187. Tse, Yiuman, 2015. "Momentum strategies with stock index exchange-traded funds," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 134-148.
    188. Jacobs, Heiko & Weber, Martin, 2015. "On the determinants of pairs trading profitability," Journal of Financial Markets, Elsevier, vol. 23(C), pages 75-97.
    189. Lin Peng & Wei Xiong & Tim Bollerslev, 2007. "Investor Attention and Time‐varying Comovements," European Financial Management, European Financial Management Association, vol. 13(3), pages 394-422, June.
    190. Xuexin WANG, 2021. "Generalized Spectral Tests for High Dimensional Multivariate Martingale Difference Hypotheses," Working Papers 2021-11-06, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    191. Yiuman Tse, 2018. "Return predictability and contrarian profits of international index futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(7), pages 788-803, July.
    192. Michael T. Chng, 2010. "Comparing Different Economic Linkages Among Commodity Futures," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(9‐10), pages 1348-1389, November.
    193. Zareei, Abalfazl, 2019. "Network origins of portfolio risk," Journal of Banking & Finance, Elsevier, vol. 109(C).
    194. Tao Huang & Xueyong Zhang, 2022. "Media coverage of industry and the cross‐section of stock returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(S1), pages 1107-1141, April.
    195. Tung-Yueh Pai & Yen-Hsien Lee, 2018. "Industry Herding, Spillover Index and Investment Strategy," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 8(6), pages 1-6.
    196. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    197. Liu, Bin & Xiao, Wen & Zhu, Xingting, 2023. "How does inter-industry spillover improve the performance of volatility forecasting?," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    198. Dai, Rui & Ng, Lilian & Zaiats, Nataliya, 2022. "Short seller attention," Journal of Corporate Finance, Elsevier, vol. 72(C).
    199. Chi Dong & Hooi Hooi Lean & Zamri Ahmad, 2017. "Intra-industry information diffusion in China's stock market," Economics Bulletin, AccessEcon, vol. 37(1), pages 1-11.
    200. Serkan Karadas & Minh Tam Tammy Schlosky & Joshua Hall, 2021. "Did Politicians Use Non-Public Macroeconomic Information in Their Stock Trades? Evidence from the STOCK Act of 2012," JRFM, MDPI, vol. 14(6), pages 1-18, June.
    201. Ronald A. Ratti & M. Zahid Hasan, 2013. "Oil Price Shocks and Volatility in Australian Stock Returns," The Economic Record, The Economic Society of Australia, vol. 89, pages 67-83, June.
    202. Rababa’a, Abdel Razzaq Al & Alomari, Mohammad & Rehman, Mobeen Ur & McMillan, David & Hendawi, Raed, 2022. "Multiscale relationship between economic policy uncertainty and sectoral returns: Implications for portfolio management," Research in International Business and Finance, Elsevier, vol. 61(C).
    203. Tolikas, Konstantinos, 2016. "The relative informational efficiency of corporate retail bonds: Evidence from the London Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 191-201.
    204. Junni L. Zhang & Wolfgang K. Härdle & Cathy Y. Chen & Elisabeth Bommes, 2015. "Distillation of News Flow into Analysis of Stock Reactions," SFB 649 Discussion Papers SFB649DP2015-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    205. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Tran, Vuong Thao, 2018. "Can economic policy uncertainty predict stock returns? Global evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 134-150.
    206. L. Vanessa Smith & Takashi Yamagata, 2008. "Firm Level Volatility-Return Analysis using Dynamic Panels," Discussion Papers 08/09, Department of Economics, University of York.
    207. Maik Schmeling & Andreas Schrimpf, 2008. "Expected Inflation, Expected Stock Returns, and Money Illusion: What can we learn from Survey Expectations?," SFB 649 Discussion Papers SFB649DP2008-036, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    208. M. Max Croce & Tatyana Marchuk & Christian Schlag, 2019. "The Leading Premium," NBER Working Papers 25633, National Bureau of Economic Research, Inc.
    209. Rossen Valkanov & Andra Ghent, 2014. "Complexity in Structured Finance: Financial Wizardry or Smoke and Mirrors," 2014 Meeting Papers 104, Society for Economic Dynamics.
    210. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    211. Kuo-Hao Lee & Ahmed Elkassabgi & Wei-Jen Hsieh, 2014. "Volatility of the Utilities Industry: Its Causal Relationship to Other Nine Industries," Review of Economics & Finance, Better Advances Press, Canada, vol. 4, pages 15-22, May.
    212. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.
    213. Thomas Trier Bjerring & Kourosh Marjani Rasmussen & Alex Weissensteiner, 2018. "Portfolio selection under supply chain predictability," Computational Management Science, Springer, vol. 15(2), pages 139-159, June.
    214. Marshall, Ben R. & Nguyen, Hung T. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2021. "Country governance and international equity returns," Journal of Banking & Finance, Elsevier, vol. 122(C).
    215. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    216. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.
    217. Bodnaruk, Andriy & O'Brien, William & Simonov, Andrei, 2016. "Captive finance and firm's competitiveness," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 210-228.
    218. Cohen, Lauren & Lou, Dong, 2012. "Complicated firms," Journal of Financial Economics, Elsevier, vol. 104(2), pages 383-400.
    219. Hasselgren, Anton & Peltomäki, Jarkko & Graham, Michael, 2020. "Speculator activity and the cross-asset predictability of FX returns," International Review of Financial Analysis, Elsevier, vol. 72(C).
    220. Chia-Wei Chen & Christos Pantzalis & Jung Chul Park, 2013. "Press Coverage And Stock Price Deviation From Fundamental Value," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 36(2), pages 175-214, June.
    221. Ashish Agarwal & Alvin Chung Man Leung & Prabhudev Konana & Alok Kumar, 2017. "Cosearch Attention and Stock Return Predictability in Supply Chains," Information Systems Research, INFORMS, vol. 28(2), pages 265-288, June.
    222. Tan, Siow-Hooi & Lai, Ming-Ming & Tey, Eng-Xin & Chong, Lee-Lee, 2020. "Testing the performance of technical analysis and sentiment-TAR trading rules in the Malaysian stock market," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    223. Lauren LoRe & Mahfuz Raihan, 2016. "Corporate Payouts, Macroeconomic Influences and Industry Effects," Applied Finance and Accounting, Redfame publishing, vol. 2(2), pages 100-112, August.
    224. Swasti Gupta‐Mukherjee & Ankur Pareek, 2020. "Limited attention and portfolio choice: The impact of attention allocation on mutual fund performance," Financial Management, Financial Management Association International, vol. 49(4), pages 1083-1125, December.
    225. Wu, Qiongbing & Shamsuddin, Abul, 2014. "Investor attention, information diffusion and industry returns," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 30-43.
    226. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    227. Chikashi Tsuji, 2012. "Exchange Rate Changes and Stock Returns: The Recent Cases of the Japanese Electric Appliances Industry Firms," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 1(2), pages 71-79, October.
    228. Croce, Mariano & Schlag, Christian & Marchuk, Tatyana, 2018. "The Leading Premium," CEPR Discussion Papers 12631, C.E.P.R. Discussion Papers.
    229. Wen, Yi-Chieh & Lin, Philip T. & Li, Bin & Roca, Eduardo, 2015. "Stock return predictability in South Africa: The role of major developed markets," Finance Research Letters, Elsevier, vol. 15(C), pages 257-265.
    230. Li Guo & Lin Peng & Yubo Tao & Jun Tu, 2017. "Joint News, Attention Spillover,and Market Returns," Papers 1703.02715, arXiv.org, revised Nov 2022.
    231. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    232. Loughran, Tim, 2007. "Geographic dissemination of information," Journal of Corporate Finance, Elsevier, vol. 13(5), pages 675-694, December.
    233. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Bannigidadmath, Deepa, 2017. "Is the profitability of Indian stocks compensation for risks?," Emerging Markets Review, Elsevier, vol. 31(C), pages 47-64.
    234. Alsalman, Zeina, 2016. "Oil price uncertainty and the U.S. stock market analysis based on a GARCH-in-mean VAR model," Energy Economics, Elsevier, vol. 59(C), pages 251-260.
    235. Chao Liang & Yu Wei & Likun Lei & Feng Ma, 2022. "Global equity market volatility forecasting: New evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 594-609, January.
    236. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
    237. Nonejad, Nima, 2020. "Crude oil price changes and the United Kingdom real gross domestic product growth rate: An out-of-sample investigation," The Journal of Economic Asymmetries, Elsevier, vol. 21(C).
    238. Onishchenko, Olena & Zhao, Jing & Kuruppuarachchi, Duminda & Roberts, Helen, 2021. "Intraday time-series momentum and investor trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    239. Moosa, Imad A. & Al-Deehani, Talla M., 2009. "The Myth of International Diversification," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 62(3), pages 383-406.
    240. Ana Monteiro & Nuno Silva & Helder Sebastião, 2023. "Industry return lead-lag relationships between the US and other major countries," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-48, December.
    241. Valcarcel, Victor J. & Vivian, Andrew J. & Wohar, Mark E., 2017. "Predictability and underreaction in industry-level returns: Evidence from commodity markets," Journal of Commodity Markets, Elsevier, vol. 6(C), pages 1-15.
    242. Smith, Simon C. & Timmermann, Allan, 2022. "Have risk premia vanished?," Journal of Financial Economics, Elsevier, vol. 145(2), pages 553-576.
    243. Chiang, I-Hsuan Ethan & Hughen, W. Keener, 2017. "Do oil futures prices predict stock returns?," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 129-141.
    244. Mukherjee, Abhiroop & Panayotov, George & Shon, Janghoon, 2021. "Eye in the sky: Private satellites and government macro data," Journal of Financial Economics, Elsevier, vol. 141(1), pages 234-254.
    245. Irena Vodenska & Hideaki Aoyama & Yoshi Fujiwara & Hiroshi Iyetomi & Yuta Arai, 2016. "Interdependencies and Causalities in Coupled Financial Networks," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-32, March.
    246. Zeng, Kailin & Tang, Ting & Liu, Fangbiao & Atta Mills, Ebenezer Fiifi Emire, 2022. "Innovation links, information diffusion, and return predictability: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    247. Nonejad, Nima, 2020. "A comprehensive empirical analysis of the predictive impact of the price of crude oil on aggregate equity return volatility," Journal of Commodity Markets, Elsevier, vol. 20(C).
    248. Seungho Baek & Kwan Yong Lee & Merih Uctum & Seok Hee Oh, 2020. "Robo-Advisors: Machine Learning in Trend-Following ETF Investments," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    249. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    250. Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Forecasting Realized Stock-Market Volatility: Do Industry Returns have Predictive Value?," Working Papers 2020107, University of Pretoria, Department of Economics.
    251. Xie Haibin & Zhou Mo & Yu Mei & Hu Yi, 2014. "Forecasting the Crude Oil Price with Extreme Values," Journal of Systems Science and Information, De Gruyter, vol. 2(3), pages 193-205, June.
    252. Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
    253. Schlag, Christian & Zeng, Kailin, 2019. "Horizontal industry relationships and return predictability," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 310-330.
    254. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    255. Zhu, Xiaoneng, 2015. "Out-of-sample bond risk premium predictions: A global common factor," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 155-173.
    256. Huang, Yisu & Ma, Feng & Bouri, Elie & Huang, Dengshi, 2023. "A comprehensive investigation on the predictive power of economic policy uncertainty from non-U.S. countries for U.S. stock market returns," International Review of Financial Analysis, Elsevier, vol. 87(C).
    257. Kailin Zeng & Ebenezer Fiifi Emire Atta Mills, 2023. "Can economic links explain lead–lag relations across firms?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1338-1363, April.
    258. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    259. Kenneth Högholm & Johan Knif & Gregory Koutmos & Seppo Pynnönen, 2021. "Financial crises and the asymmetric relation between returns on banks, risk factors, and other industry portfolio returns," The Financial Review, Eastern Finance Association, vol. 56(1), pages 179-198, February.
    260. Wang, Yudong & Wei, Yu & Wu, Chongfeng & Yin, Libo, 2018. "Oil and the short-term predictability of stock return volatility," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 90-104.
    261. Muhammad Saqib Bashir Butt & Hasniza Mohd Taib, 2019. "Economic Forces and Firm Stock Returns Volatility: Role of Firm Features," Pakistan Journal of Humanities and Social Sciences, International Research Alliance for Sustainable Development (iRASD), vol. 7(3), pages :281-302, September.
    262. Chng, Michael T., 2009. "Economic linkages across commodity futures: Hedging and trading implications," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 958-970, May.
    263. Lu, Helen & Jacobsen, Ben, 2016. "Cross-asset return predictability: Carry trades, stocks and commodities," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 62-87.
    264. Hong, Harrison & Li, Frank Weikai & Xu, Jiangmin, 2019. "Climate risks and market efficiency," Journal of Econometrics, Elsevier, vol. 208(1), pages 265-281.

  6. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2007. "Valuation in US Commercial Real Estate," European Financial Management, European Financial Management Association, vol. 13(3), pages 472-497, June.

    Cited by:

    1. David Ling & Gianluca Marcato & Pat McAllister, 2009. "Dynamics of Asset Prices and Transaction Activity in Illiquid Markets: the Case of Private Commercial Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 39(3), pages 359-383, October.
    2. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    3. Frank Fabozzi & Robert Shiller & Radu Tunaru, 2009. "Property Derivatives for Managing European Real-Estate Risk," Yale School of Management Working Papers amz2652, Yale School of Management, revised 01 Sep 2009.
    4. Füss, Roland & Ruf, Daniel, 2021. "Bank systemic risk exposure and office market interconnectedness," Journal of Banking & Finance, Elsevier, vol. 133(C).
    5. Christian Rehring, 2012. "Real Estate in a Mixed‐Asset Portfolio: The Role of the Investment Horizon," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 40(1), pages 65-95, March.
    6. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    7. Daniele Bianchi & Massimo Guidolin, 2014. "Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 49(1), pages 116-164, July.
    8. David C. Ling & Andy Naranjo, 2015. "Returns and Information Transmission Dynamics in Public and Private Real Estate Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(1), pages 163-208, March.
    9. Prashant Das & Patrick Smith & Paul Gallimore, 2018. "Pricing Extreme Attributes in Commercial Real Estate: the Case of Hotel Transactions," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 264-296, August.
    10. Jack Corgel & Crocker Liu & Robert White, 2015. "Determinants of Hotel Property Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 51(3), pages 415-439, October.
    11. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    12. Frank J. Fabozzi & Robert J. Shiller & Radu S. Tunaru, 2012. "A Pricing Framework for Real Estate Derivatives," European Financial Management, European Financial Management Association, vol. 18(5), pages 762-789, November.
    13. Bart Hobijn & John Krainer & David Lang, 2011. "Cap rates and commercial property prices," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue sep19.
    14. Jeffrey Fisher & David C. Ling & Andy Naranjo, 2009. "Institutional Capital Flows and Return Dynamics in Private Commercial Real Estate Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(1), pages 85-116, March.
    15. Frank J. Fabozzi & Robert J. Shiller & Radu S. Tunaru, 2010. "Property Derivatives for Managing European Real†Estate Risk," European Financial Management, European Financial Management Association, vol. 16(1), pages 8-26, January.
    16. David C. Ling & Andy Naranjo & Benjamin Scheick, 2014. "Investor Sentiment, Limits to Arbitrage and Private Market Returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(3), pages 531-577, September.
    17. Ferentinos, Konstantinos & Gibberd, Alex & Guin, Benjamin, 2023. "Stranded houses? The price effect of a minimum energy efficiency standard," Energy Economics, Elsevier, vol. 120(C).

  7. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    See citations under working paper version above.
  8. Valkanov, Rossen, 2005. "Functional Central Limit Theorem approximations and the distribution of the Dickey-Fuller test with strongly heteroskedastic data," Economics Letters, Elsevier, vol. 86(3), pages 427-433, March.

    Cited by:

    1. Su, Jen-Je & Cheung, Adrian (Wai-Kong) & Roca, Eduardo, 2014. "Does Purchasing Power Parity hold? New evidence from wild-bootstrapped nonlinear unit root tests in the presence of heteroskedasticity," Economic Modelling, Elsevier, vol. 36(C), pages 161-171.
    2. Nikolay Gospodinov & Ye Tao, 2011. "Bootstrap Unit Root Tests in Models with GARCH(1,1) Errors," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 379-405, August.
    3. Jürgen Wolters & Uwe Hassler, 2006. "Unit root testing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 43-58, March.

  9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    See citations under working paper version above.
  10. Walter Torous & Rossen Valkanov & Shu Yan, 2004. "On Predicting Stock Returns with Nearly Integrated Explanatory Variables," The Journal of Business, University of Chicago Press, vol. 77(4), pages 937-966, October.

    Cited by:

    1. Martin Lettau & Stijn Van Nieuwerburgh, 2006. "Reconciling the Return Predictability Evidence," 2006 Meeting Papers 29, Society for Economic Dynamics.
    2. Cai, Zongwu & Li, Qi & Park, Joon Y., 2009. "Functional-coefficient models for nonstationary time series data," Journal of Econometrics, Elsevier, vol. 148(2), pages 101-113, February.
    3. Hjalmarsson, Erik, 2008. "Interpreting long-horizon estimates in predictive regressions," Finance Research Letters, Elsevier, vol. 5(2), pages 104-117, June.
    4. Zongwu Cai & Haiqiang Chen & Xiaosai Liao, 2020. "A New Robust Inference for Predictive Quantile Regression," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202002, University of Kansas, Department of Economics, revised Feb 2020.
    5. Avdis, Efstathios & Wachter, Jessica A., 2017. "Maximum likelihood estimation of the equity premium," Journal of Financial Economics, Elsevier, vol. 125(3), pages 589-609.
    6. Firouz Fallahi, 2019. "Persistence and stationarity of sectoral energy consumption in the US: A confidence interval approach," Energy & Environment, , vol. 30(5), pages 882-897, August.
    7. Sousa, João & Sousa, Ricardo M., 2013. "Asset returns under model uncertainty: evidence from the euro area, the U.S. and the U.K," Working Paper Series 1575, European Central Bank.
    8. Jacob Boudoukh & Ronen Israel & Matthew P. Richardson, 2020. "Biases in Long-Horizon Predictive Regressions," NBER Working Papers 27410, National Bureau of Economic Research, Inc.
    9. Dunbar, Kwamie, 2021. "Pricing the hedging factor in the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    10. Chiquoine, Benjamin & Hjalmarsson, Erik, 2009. "Jackknifing stock return predictions," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 793-803, December.
    11. Narayan, Paresh Kumar & Sharma, Susan & Poon, Wai Ching & Westerlund, Joakim, 2014. "Do oil prices predict economic growth? New global evidence," Energy Economics, Elsevier, vol. 41(C), pages 137-146.
    12. Xiaoquan Jiang & Bong-Soo Lee, 2013. "Equity issues and aggregate market returns under information asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 13(2), pages 281-300, January.
    13. Kohei Aono & Tokuo Iwaisako, 2010. "On the Predictability of Japanese Stock Returns Using Dividend Yield," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(2), pages 141-149, June.
    14. Chang-Jin Kim & Cheolbeom Park, 2012. "Disappearing Dividends: Implications for the Dividend-Price Ratio and Return Predictability," Discussion Paper Series 1205, Institute of Economic Research, Korea University.
    15. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 331-353, June.
    16. Markus K. Brunnermeier & Christian Julliard, 2006. "Money Illusion and Housing Frenzies," NBER Working Papers 12810, National Bureau of Economic Research, Inc.
    17. Campbell, John & Vuolteenaho, Tuomo, 2004. "Bad Beta, Good Beta," Scholarly Articles 3122489, Harvard University Department of Economics.
    18. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    19. Alex Maynard, 2006. "The forward premium anomaly: statistical artefact or economic puzzle? New evidence from robust tests," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 39(4), pages 1244-1281, November.
    20. Charlotte S. Hansen & Bjorn E. Tuypens, 2004. "Long-Run Regressions: Theory and Application to US Asset Markets," Finance 0410018, University Library of Munich, Germany.
    21. Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
    22. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, New Economic School (NES).
    23. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    24. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, May.
    25. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    26. João Sousa & Ricardo M. Sousa, 2011. "Asset Returns Under Model Uncertainty: Eveidence from the euro area, the U.K and the U.S," NIPE Working Papers 21/2011, NIPE - Universidade do Minho.
    27. David De Villiers & Natalya Apopo & Andrew Phiri, 2018. "Unobserved structural shifts and asymmetries in the random walk model for stock returns in African frontier markets," Working Papers 1826, Department of Economics, Nelson Mandela University.
    28. Ilaria Piatti & Fabio Trojani, 2020. "Dividend Growth Predictability and the Price–Dividend Ratio," Management Science, INFORMS, vol. 66(1), pages 130-158, January.
    29. Campbell, John & Thompson, Samuel P., 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Scholarly Articles 2622619, Harvard University Department of Economics.
    30. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    31. Miyanishi, Masako, 2012. "Testing the single-factor model in the presence of persistent regressors," Economics Letters, Elsevier, vol. 116(3), pages 634-636.
    32. Lorenzo Camponovo & Olivier Scaillet & Fabio Trojani, 2016. "Predictability Hidden by Anomalous Observations," Papers 1612.05072, arXiv.org.
    33. Eiji Kurozumi & Kohei Aono, 2011. "Estimation and Inference in Predictive Regressions," Global COE Hi-Stat Discussion Paper Series gd11-192, Institute of Economic Research, Hitotsubashi University.
    34. Koijen, R.S.J., 2008. "Essays on asset pricing," Other publications TiSEM 75662994-29dc-4a83-a3ff-9, Tilburg University, School of Economics and Management.
    35. Robert J. Shiller, 2014. "Speculative Asset Prices (Nobel Prize Lecture)," Cowles Foundation Discussion Papers 1936, Cowles Foundation for Research in Economics, Yale University.
    36. Erik Hillebrand & Tae-Hwy Lee & Marcelo Cunha Medeiros, 2012. "Let´s do it again: bagging equity premium predictors," Textos para discussão 604, Department of Economics PUC-Rio (Brazil).
    37. John B. Donaldson & Rajnish Mehra, 2021. "Average crossing time: An alternative characterization of mean aversion and reversion," Quantitative Economics, Econometric Society, vol. 12(3), pages 903-944, July.
    38. Davide Pettenuzzo & Allan G. Timmermann & Rossen I. Valkanov, 2008. "Return Predictability under Equilibrium Constraints on the Equity Premium," Working Papers 37, Brandeis University, Department of Economics and International Business School.
    39. Christophe Boucher & Bertrand Maillet, 2012. "Prévoir sans persistance," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00662771, HAL.
    40. Borja Larrain & Motohiro Yogo, 2007. "Does Firm Value Move Too Much to be Justified by Subsequent Changes in Cash Flow?," NBER Working Papers 12847, National Bureau of Economic Research, Inc.
    41. Fallahi, Firouz, 2012. "The stationarity of consumption–income ratios: Evidence from bootstrapping confidence intervals," Economics Letters, Elsevier, vol. 115(1), pages 137-140.
    42. Michael Johannes & Arthur Korteweg & Nicholas Polson, 2014. "Sequential Learning, Predictability, and Optimal Portfolio Returns," Journal of Finance, American Finance Association, vol. 69(2), pages 611-644, April.
    43. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    44. Giuseppe Alesii, 2006. "Fundamentals Efficiency of the Italian Stock Market: Some Long Run Evidence," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 5(3), pages 245-264, December.
    45. Jessica A. Wachter & Missaka Warusawitharana, 2006. "Predictable returns and asset allocation: Should a skeptical investor time the market?," 2006 Meeting Papers 22, Society for Economic Dynamics.
    46. Nikitas Pittis & Christina Christou & Sarantis Kalyvitis & Christis Hassapis, 2009. "Long‐Run PPP under the Presence of Near‐to‐Unit Roots: The Case of the British Pound–US Dollar Rate," Review of International Economics, Wiley Blackwell, vol. 17(1), pages 144-155, February.
    47. GIOT, Pierre & PETITJEAN, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," LIDAM Reprints CORE 1982, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    48. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    49. Shiller, Robert J., 2013. "Speculative Asset Prices," Nobel Prize in Economics documents 2013-6, Nobel Prize Committee.
    50. Jiang, Xiaoquan & Lee, Bong-Soo, 2007. "Stock returns, dividend yield, and book-to-market ratio," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 455-475, February.
    51. Huang, Darien & Kilic, Mete, 2019. "Gold, platinum, and expected stock returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 50-75.
    52. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    53. Jiang, Xiaoquan & Zaman, Mir A., 2010. "Aggregate insider trading: Contrarian beliefs or superior information?," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1225-1236, June.
    54. Kilian, Lutz & Park, Cheolbeom, 2007. "The Impact of Oil Price Shocks on the U.S. Stock Market," CEPR Discussion Papers 6166, C.E.P.R. Discussion Papers.
    55. David Rey, 2005. "Market Timing And Model Uncertainty: An Exploratory Study For The Swiss Stock Market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(3), pages 239-260, October.
    56. Christophe Boucher & Bertrand Maillet, 2012. "Prévoir sans persistance," Documents de travail du Centre d'Economie de la Sorbonne 12001, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    57. Kuang-Liang Chang & Nan-Kuang Chen & Charles Ka Yui Leung, 2016. "Losing Track of the Asset Markets: the Case of Housing and Stock," International Real Estate Review, Global Social Science Institute, vol. 19(4), pages 435-492.
    58. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    59. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    60. Sajjadur Rahman, 2021. "Oil price volatility and the US stock market," Empirical Economics, Springer, vol. 61(3), pages 1461-1489, September.
    61. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
    62. Brennan, Michael J. & Xia, Yihong, 2005. "tay's as good as cay," Finance Research Letters, Elsevier, vol. 2(1), pages 1-14, March.
    63. JULES H. Van BINSBERGEN & MICHAEL W. BRANDT & RALPH S. J. KOIJEN, 2008. "Optimal Decentralized Investment Management," Journal of Finance, American Finance Association, vol. 63(4), pages 1849-1895, August.
    64. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    65. Kothari, Pratik & O’Doherty, Michael S., 2023. "Job postings and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 64(C).
    66. John Y. Campbell & Christopher Polk & Tuomo Vuolteenaho, 2010. "Growth or Glamour? Fundamentals and Systematic Risk in Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 305-344, January.
    67. Jennie Bai, 2010. "Equity premium predictions with adaptive macro indexes," Staff Reports 475, Federal Reserve Bank of New York.
    68. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    69. Efstathios Avdis & Jessica A. Wachter, 2013. "Maximum likelihood estimation of the equity premium," NBER Working Papers 19684, National Bureau of Economic Research, Inc.
    70. Michael Scholz & Jens Perch Nielsen & Stefan Sperlich, 2012. "Nonparametric prediction of stock returns guided by prior knowledge," Graz Economics Papers 2012-02, University of Graz, Department of Economics.
    71. Qiu, Mei & Pinfold, John F. & Rose, Lawrence C., 2011. "Predicting foreign exchange movements using historic deviations from PPP," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 485-497, October.
    72. Campbell, John, 2008. "Estimating the Equity Premium," Scholarly Articles 3196339, Harvard University Department of Economics.
    73. Narayan, Seema & Smyth, Russell, 2015. "The financial econometrics of price discovery and predictability," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 380-393.
    74. Nikolaos Mitianoudis & Theologos Dergiades, 2016. "Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain," Discussion Paper Series 2016_04, Department of Economics, University of Macedonia, revised Dec 2016.
    75. John Y. Campbell & Samuel B. Thompson, 2005. "Predicting the Equity Premium Out of Sample: Can Anything Beat the Historical Average?," Harvard Institute of Economic Research Working Papers 2084, Harvard - Institute of Economic Research.
    76. Nonejad, Nima, 2021. "Predicting the return on the spot price of crude oil out-of-sample by conditioning on news-based uncertainty measures: Some new empirical results," Energy Economics, Elsevier, vol. 104(C).
    77. Katsumi Shimotsu & Alex Maynard, 2004. "Covariance-based orthogonality tests for regressors with unknown persistence," Econometric Society 2004 North American Summer Meetings 536, Econometric Society.
    78. Fallahi, Firouz & Voia, Marcel-Cristian, 2015. "Convergence and persistence in per capita energy use among OECD countries: Revisited using confidence intervals," Energy Economics, Elsevier, vol. 52(PA), pages 246-253.
    79. Andreou, Elena & Kasparis, Ioannis & Phillips, Peter C. B., 2013. "Nonparametric Predictive Regression," CEPR Discussion Papers 9570, C.E.P.R. Discussion Papers.
    80. Hualde, Javier, 2014. "Estimation of long-run parameters in unbalanced cointegration," Journal of Econometrics, Elsevier, vol. 178(2), pages 761-778.
    81. Miguel A. Ferreira & Pedro Santa-Clara, 2008. "Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole," NBER Working Papers 14571, National Bureau of Economic Research, Inc.
    82. Simionescu, Mihaela, 2022. "Stochastic convergence in per capita energy use in the EU-15 countries. The role of economic growth," Applied Energy, Elsevier, vol. 322(C).
    83. Fallahi, Firouz & Karimi, Mohammad & Voia, Marcel-Cristian, 2016. "Persistence in world energy consumption: Evidence from subsampling confidence intervals," Energy Economics, Elsevier, vol. 57(C), pages 175-183.
    84. Fallahi, Firouz, 2017. "Stochastic convergence in per capita energy use in world," Energy Economics, Elsevier, vol. 65(C), pages 228-239.
    85. Laimutė Urbšienė & Andrius Bugajevas & Marekas Pipiras, 2016. "The Impact Of Investment Horizon On The Return And Risk Of Investments In Securities In Lithuania," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 7(2).
    86. Aroh Nkechi Nympha. Ph.D & Egolum, Priscilla Uchenna. Ph.D & Chukwuani Victoria Nnenna. Ph.D, 2021. "Dividend Policy Determinants of Firm Value: Empirical Evidence from Listed Non-Financial Companies in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(07), pages 612-634, July.
    87. Gungor, Sermin & Luger, Richard, 2020. "Small-sample tests for stock return predictability with possibly non-stationary regressors and GARCH-type effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 750-770.
    88. Liyu Dou & Ulrich K. Müller, 2021. "Generalized Local‐to‐Unity Models," Econometrica, Econometric Society, vol. 89(4), pages 1825-1854, July.
    89. Audrino, Francesco & Camponovo, Lorenzo & Roth, Constantin, 2015. "Testing the lag structure of assets’ realized volatility dynamics," Economics Working Paper Series 1501, University of St. Gallen, School of Economics and Political Science.
    90. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    91. Dunbar, Kwamie & Jiang, Jing, 2020. "What do movements in financial traders’ net long positions reveal about aggregate stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    92. Catherine Georgiou, 2020. "The British Stock Market under the Structure of Market Capitalization Value: New Evidence on its Predictive Content," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 13(3), pages 56-69, December.
    93. Christophe Boucher & Bertrand Maillet, 2011. "Detrending Persistent Predictors," Post-Print halshs-00587775, HAL.
    94. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
    95. Hong, Harrison & Torous, Walter & Valkanov, Rossen, 2007. "Do industries lead stock markets?," Journal of Financial Economics, Elsevier, vol. 83(2), pages 367-396, February.
    96. Erik Hjalmarsson, 2006. "Inference in Long-Horizon Regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.).
    97. Nonejad, Nima, 2023. "Conditional out-of-sample predictability of aggregate equity returns and aggregate equity return volatility using economic variables," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 91-122.
    98. Roskelley, Kenneth D., 2008. "Cromwell's Rule and the Role of the Prior in the Economic Metric: An Application to the Portfolio Allocation Problem," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 227-236, April.
    99. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    100. M. Max Croce & Tatyana Marchuk & Christian Schlag, 2019. "The Leading Premium," NBER Working Papers 25633, National Bureau of Economic Research, Inc.
    101. Efthymios Pavlidis & Ivan Paya & David Peel, 2012. "A New Test for Rational Speculative Bubbles using Forward Exchange Rates: The Case of the Interwar German Hyperinflation," Working Papers 18599597, Lancaster University Management School, Economics Department.
    102. Hjalmarsson, Erik, 2005. "On the Predictability of Global Stock Returns," Working Papers in Economics 161, University of Gothenburg, Department of Economics.
    103. Sha Zhu & Fujun Lai & Jie Deng & Qian Wang, 2021. "Do Mutual Funds’ Exposure to Financial Stress Predict Their Future Returns? Evidence From China," SAGE Open, , vol. 11(4), pages 21582440211, October.
    104. Bessler, Wolfgang & Drobetz, Wolfgang & Zimmermann, Heinz, 2007. "Conditional Performance Evaluation for German Mutual Equity Funds," Working papers 2007/22, Faculty of Business and Economics - University of Basel.
    105. Croce, Mariano M. & Marchuk, Tatyana & Schlag, Christian, 2022. "The leading premium," SAFE Working Paper Series 371, Leibniz Institute for Financial Research SAFE.
    106. Nonejad, Nima, 2020. "Crude oil price volatility and equity return predictability: A comparative out-of-sample study," International Review of Financial Analysis, Elsevier, vol. 71(C).
    107. Yoon, Sun-Joong, 2017. "Time-varying risk aversion and return predictability," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 327-339.
    108. Kang, Jangkoo & Kim, Tong Suk & Lee, Changjun & Min, Byoung-Kyu, 2011. "Macroeconomic risk and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3158-3173.
    109. John Powell & Jing Shi & Tom Smith & Robert Whaley, 2009. "Common Divisors, Payout Persistence, and Return Predictability," International Review of Finance, International Review of Finance Ltd., vol. 9(4), pages 335-357, December.
    110. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    111. Jacob Boudoukh & Matthew Richardson & Robert F. Whitelaw, 2008. "The Myth of Long-Horizon Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1577-1605, July.
    112. Choi, Yongok & Jacewitz, Stefan & Park, Joon Y., 2016. "A reexamination of stock return predictability," Journal of Econometrics, Elsevier, vol. 192(1), pages 168-189.
    113. Kaihua Deng & Chang-Jin Kim, 2015. "Predicting Stock Returns — The Information Content Of Predictors Across Horizons," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 1-27, December.
    114. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers wp677, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    115. Jules H. van Binsbergen & Michael W. Brandt, 2007. "Optimal Asset Allocation in Asset Liability Management," NBER Working Papers 12970, National Bureau of Economic Research, Inc.
    116. Aroh Nkechi Nympha. Ph.D & Egolum, Priscilla Uchenna. Ph.D & Chukwuani Victoria Nnenna. Ph.D, 2021. "Dividend Policy Determinants of Firm Value: Empirical Evidence from Listed Non-Financial Companies in Nigeria," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(7), pages 612-634, July.
    117. Croce, Mariano & Schlag, Christian & Marchuk, Tatyana, 2018. "The Leading Premium," CEPR Discussion Papers 12631, C.E.P.R. Discussion Papers.
    118. Collard, Fabrice & Feve, Patrick & Ghattassi, Imen, 2006. "Predictability and habit persistence," Journal of Economic Dynamics and Control, Elsevier, vol. 30(11), pages 2217-2260, November.
    119. Aaron Smallwood; Alex Maynard; Mark Wohar, 2005. "The Long and the Short of It: Long Memory Regressors and Predictive Regressions," Computing in Economics and Finance 2005 384, Society for Computational Economics.
    120. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    121. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predicting inflation expectations: A habit-based explanation under hedging," International Review of Financial Analysis, Elsevier, vol. 89(C).
    122. Nima Nonejad, 2020. "A detailed look at crude oil price volatility prediction using macroeconomic variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1119-1141, November.
    123. Nonejad, Nima, 2020. "A comprehensive empirical analysis of the predictive impact of the price of crude oil on aggregate equity return volatility," Journal of Commodity Markets, Elsevier, vol. 20(C).
    124. Ventosa-Santaulària, Daniel & Noriega, Antonio E., 2015. "Long-run monetary neutrality under stochastic and deterministic trends," Economic Modelling, Elsevier, vol. 47(C), pages 372-382.
    125. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    126. Cai, Zongwu & Chen, Haiqiang & Liao, Xiaosai, 2023. "A new robust inference for predictive quantile regression," Journal of Econometrics, Elsevier, vol. 234(1), pages 227-250.
    127. Shin, Dong Wan & Joon Kim, Han & Jhee, Won-Chul, 2007. "Asymptotic efficiency of the ordinary least-squares estimator for sur models with integrated regressors," Statistics & Probability Letters, Elsevier, vol. 77(1), pages 75-82, January.
    128. Firouz Fallahi & Mohammad Karimi & Marcel-Cristian Voia, 2014. "Are Shocks to Energy Consumption Persistent? Evidence from Subsampling Confidence Intervals," Carleton Economic Papers 14-02, Carleton University, Department of Economics.
    129. Christophe Boucher & Bertrand Maillet, 2012. "Prévoir sans persistance," Post-Print halshs-00662771, HAL.

  11. Pedro Santa‐Clara & Rossen Valkanov, 2003. "The Presidential Puzzle: Political Cycles and the Stock Market," Journal of Finance, American Finance Association, vol. 58(5), pages 1841-1872, October.

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    2. David Le Bris, 2012. "Stock Returns, Governments and Market Foresight in France, 1871-2008," Working Papers CEB 12-007, ULB -- Universite Libre de Bruxelles.
    3. Hyeongwoo Kim & Madeline Kim, 2021. "U.S. Presidential Election Polls and the Economic Prospects of China and Mexico," Auburn Economics Working Paper Series auwp2021-02, Department of Economics, Auburn University.
    4. Yi-Hsien Wang & Jui-Cheng Hung & Yen-Hsien Lee & Chung-Chu Chuang, 2012. "Computing regression quantiles to analysis the relationship between market behavior and political risk," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(4), pages 1047-1055, June.
    5. Beyer, Deborah B. & Fan, Zaifeng S., 2023. "The calming effects of conflict: The impact of partisan conflict on market volatility," International Review of Financial Analysis, Elsevier, vol. 85(C).
    6. Laurent E. Calvet & Adlai J. Fisher, 2005. "Multifrequency News and Stock Returns," NBER Working Papers 11441, National Bureau of Economic Research, Inc.
    7. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    8. He, Yan & Lin, Hai & Wu, Chunchi & Dufrene, Uric B., 2009. "The 2000 presidential election and the information cost of sensitive versus non-sensitive S&P 500 stocks," Journal of Financial Markets, Elsevier, vol. 12(1), pages 54-86, February.
    9. Niklas Potrafke, 2018. "Government ideology and economic policy-making in the United States—a survey," Public Choice, Springer, vol. 174(1), pages 145-207, January.
    10. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    11. Sutsarun Lumiajiak & Sirimon Treepongkaruna & Marvin Wee & Robert Brooks, 2014. "Thai Financial Markets and Political Change," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 1, pages 5-26, July.
    12. Yong-Huang Lin & Yun-Wu Wu & Jer-Shiou Chiou, 2008. "The impacts of sociopolitical instability on construction dimension," Applied Economics Letters, Taylor & Francis Journals, vol. 15(15), pages 1207-1211.
    13. Kofi A. Amoateng, 2019. "Did Tom Brady Save the US stock market? Market Anomaly or Market Efficiency?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(5), pages 128-128, May.
    14. Augusto Carvalho & Bernardo Guimaraes, 2016. "State-controlled companies and political risk: Evidence from the 2014 Brazilian election," Discussion Papers 1702, Centre for Macroeconomics (CFM).
    15. Chkir, Imed & Gallali, Mohamed Imen & Toukabri, Manara, 2020. "Political connections and corporate debt: Evidence from two U.S. election campaigns," The Quarterly Review of Economics and Finance, Elsevier, vol. 75(C), pages 229-239.
    16. Bretscher, Lorenzo & Julliard, Christian & Rosa, Carlo, 2016. "Human capital and international portfolio diversification: a reappraisal," LSE Research Online Documents on Economics 64835, London School of Economics and Political Science, LSE Library.
    17. Bohl, Martin T. & Gottschalk, Katrin, 2006. "International evidence on the Democrat premium and the presidential cycle effect," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 107-120, August.
    18. Eichler, Stefan & Plaga, Timo, 2020. "The economic record of the government and sovereign bond and stock returns around national elections," Journal of Banking & Finance, Elsevier, vol. 118(C).
    19. Rangan Gupta & Yuvana Jaichand & Christian Pierdzioch & Reneé van Eyden, 2023. "Realized Stock-Market Volatility of the United States and the Presidential Approval Rating," Mathematics, MDPI, vol. 11(13), pages 1-27, July.
    20. Federica Liberini & Michela Redoano & Antonio Russo & Ángel Cuevas & Rubén Cuevas, 2020. "Politics in the Facebook Era - Evidence from the 2016 US Presidential Elections," CESifo Working Paper Series 8235, CESifo.
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    22. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2012. "Prediction Markets for Economic Forecasting," CESifo Working Paper Series 3884, CESifo.
    23. Daniele Girardi, 2018. "Political shocks and financial markets : regression-discontinuity evidence from national elections," UMASS Amherst Economics Working Papers 2018-08, University of Massachusetts Amherst, Department of Economics.
    24. Brent C Smith & Kenneth N. Daniels, 2018. "Unintended Consequences of Risk Based Pricing: Racial Differences in Mortgage Costs," Journal of Financial Services Research, Springer;Western Finance Association, vol. 54(3), pages 323-343, December.
    25. Jamal Bouoiyour & Refk Selmi & Shawkat Hammoudeh & Mark E Wohar, 2019. "What are the categories of geopolitical risks that could drive oil prices higher? Acts or threats?," Post-Print hal-02409062, HAL.
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    27. Snowberg, Erik & Wolfers, Justin & Zitzewitz, Eric, 2006. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," IZA Discussion Papers 1996, Institute of Labor Economics (IZA).
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    30. Al-Thaqeb, Saud Asaad & Algharabali, Barrak Ghanim, 2019. "Economic policy uncertainty: A literature review," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    31. Tomasz Wisniewski & Geoffrey Lightfoot & Simon Lilley, 2012. "Speculating on presidential success: exploring the link between the price–earnings ratio and approval ratings," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 36(1), pages 106-122, January.
    32. Saibal Ghosh, 2023. "Political connections and bank behaviour," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 52(1), February.
    33. Sy, Oumar & Zaman, Ashraf Al, 2020. "Is the presidential premium spurious?," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 94-104.
    34. Alan S. Blinder & Mark W. Watson, 2016. "Presidents and the US Economy: An Econometric Exploration," American Economic Review, American Economic Association, vol. 106(4), pages 1015-1045, April.
    35. Faridah Najuna Misman & Shashazrina Roslan & Muhammad Izzat Mat Aladin, 2020. "General Election and Stock Market Performance: A Malaysian Case," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(3), pages 139-145, June.
    36. Clemens Sialm, 2005. "Tax Changes and Asset Pricing: Time-Series Evidence," NBER Working Papers 11756, National Bureau of Economic Research, Inc.
    37. Chan, Kam Fong & Gray, Philip & Gray, Stephen & Zhong, Angel, 2020. "Political uncertainty, market anomalies and Presidential honeymoons," Journal of Banking & Finance, Elsevier, vol. 113(C).
    38. Dao, Thong M. & McGroarty, Frank & Urquhart, Andrew, 2019. "The Brexit vote and currency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 59(C), pages 153-164.
    39. Wolfers, Justin & Zitzewitz, Eric & Snowberg, Erik, 2011. "How Prediction Markets Can Save Event Studies," CEPR Discussion Papers 8351, C.E.P.R. Discussion Papers.
    40. Ray C. Fair, 2021. "Retrospective Voting Versus Risk-Aversion Voting: A Comment on Pástor and Veronesi (2020)," Cowles Foundation Discussion Papers 2279, Cowles Foundation for Research in Economics, Yale University, revised Jul 2021.
    41. Leilane de Freitas Rocha Cambara & Roberto Meurer, 2023. "News sentiment and foreign portfolio investment in Brazil," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3332-3348, July.
    42. Sean D. Campbell & Canlin Li, 2004. "Alternative estimates of the presidential premium," Finance and Economics Discussion Series 2004-69, Board of Governors of the Federal Reserve System (U.S.).
    43. Hudepohl, Tom & van Lamoen, Ryan & de Vette, Nander, 2021. "Quantitative easing and exuberance in stock markets: Evidence from the euro area," Journal of International Money and Finance, Elsevier, vol. 118(C).
    44. Gil-Alana, Luis A. & Mudida, Robert & Yaya, OlaOluwa S & Osuolale, Kazeem & Ogbonna, Ephraim A, 2019. "Influence of US Presidential Terms on S&P500 Index Using a Time Series Analysis Approach," MPRA Paper 93941, University Library of Munich, Germany.
    45. Shen, Chung-Hua & Bui, Dien Giau & Lin, Chih-Yung, 2017. "Do political factors affect stock returns during presidential elections?," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 180-198.
    46. Alexander Opitz, 2018. "“Comrades, Let's March!”.† The Revolution of 1905 and its impact on financial markets," European Review of Economic History, European Historical Economics Society, vol. 22(1), pages 28-52.
    47. Ray C. Fair, 2021. "Retrospective Voting Versus Risk-Aversion Voting," Cowles Foundation Discussion Papers 2279, Cowles Foundation for Research in Economics, Yale University.
    48. Treepongkaruna, Sirimon & Chan, Kam Fong & Malik, Ihtisham, 2023. "Climate policy uncertainty and the cross-section of stock returns," Finance Research Letters, Elsevier, vol. 55(PA).
    49. Geller, Gabriel & Guedes, Maria João Coelho, 2017. "Was the collapse of the communist bloc a game changer in the stock markets? Left-wing vs. right-wing political preferences and stock market development," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 423-432.
    50. Caporale, Barbara & Caporale, Tony, 2008. "Political risk and the expectations hypothesis," Economics Letters, Elsevier, vol. 100(2), pages 178-180, August.
    51. Pham, Huy Nguyen Anh & Ramiah, Vikash & Moosa, Nisreen & Huynh, Tam & Pham, Nhi, 2018. "The financial effects of Trumpism," Economic Modelling, Elsevier, vol. 74(C), pages 264-274.
    52. Gupta, Rangan & Pierdzioch, Christian & Selmi, Refk & Wohar, Mark E., 2018. "Does partisan conflict predict a reduction in US stock market (realized) volatility? Evidence from a quantile-on-quantile regression model☆," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 87-96.
    53. Youngsoo Kim & Jung Chul Park, 2022. "Presidential power and stock returns," Financial Management, Financial Management Association International, vol. 51(2), pages 455-499, June.
    54. Powell, John G. & Shi, Jing & Smith, Tom & Whaley, Robert E., 2009. "Political regimes, business cycles, seasonalities, and returns," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1112-1128, June.
    55. Mark Borgschulte & Heepyung Cho & Darren Lubotsky, 2019. "Partisanship and Survey Refusal," NBER Working Papers 26433, National Bureau of Economic Research, Inc.
    56. Samar Ashour & David Rakowski & Salil K. Sarkar, 2021. "Currency risk exposure and the presidential effect in stock returns," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(3), pages 469-485, July.
    57. Wisniewski, Tomasz Piotr & Lambe, Brendan John, 2015. "Does economic policy uncertainty drive CDS spreads?," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 447-458.
    58. Taylor, Mark & Filippou, Ilias & Gozluklu, Arie & Nguyen, My, 2020. "U.S. Populist Rhetoric and Currency Returns," CEPR Discussion Papers 15054, C.E.P.R. Discussion Papers.
    59. Wong, Wing-Keung & McAleer, Michael, 2009. "Mapping the Presidential Election Cycle in US stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(11), pages 3267-3277.
    60. Veronesi, Pietro & Pástor, Luboš, 2010. "Uncertainty about Government Policy and Stock Prices," CEPR Discussion Papers 7897, C.E.P.R. Discussion Papers.
    61. Anderson, Hamish D. & Malone, Christopher B. & Marshall, Ben R., 2008. "Investment returns under right- and left-wing governments in Australasia," Pacific-Basin Finance Journal, Elsevier, vol. 16(3), pages 252-267, June.
    62. Urquhart, Andrew & McGroarty, Frank, 2014. "Calendar effects, market conditions and the Adaptive Market Hypothesis: Evidence from long-run U.S. data," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 154-166.
    63. Kim, Chansog (Francis) & Pantzalis, Christos & Chul Park, Jung, 2012. "Political geography and stock returns: The value and risk implications of proximity to political power," Journal of Financial Economics, Elsevier, vol. 106(1), pages 196-228.
    64. Federico M. Bandi & Bernard Perron & Andrea Tamoni & Claudio Tebaldi, 2014. "The scale of predictability," Working Papers 509, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    65. Bialkowski, Jedrzej & Gottschalk, Katrin & Wisniewski, Tomasz, 2006. "Stock market volatiltity around national elections," MPRA Paper 302, University Library of Munich, Germany, revised Nov 2006.
    66. Andrieş, Alin Marius & Plopeanu, Aurelian-Petruş & Sprincean, Nicu, 2023. "Institutional determinants of households’ financial investment behaviour across European countries," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 300-325.
    67. Boutchkov, Maria & Doshi, Hitesh & Durnev, Art & Molchanov, Alexander, 2008. "Politics and Volatility," CEI Working Paper Series 2008-10, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    68. Steven A. Block & Burkhard N. Schrage & Paul M. Vaaler, 2003. "DEMOCRACY???S SPREAD: Elections and Sovereign Debt in Developing Countries," William Davidson Institute Working Papers Series 2003-575, William Davidson Institute at the University of Michigan.
    69. Fernandez-Perez, Adrian & Gilbert, Aaron & Indriawan, Ivan & Nguyen, Nhut H., 2021. "COVID-19 pandemic and stock market response: A culture effect," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    70. Yi-Hsien Wang & Chung-Chu Chuang, 2009. "Selecting the portfolio investment strategy under political structure change in United States," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(5), pages 845-854, September.
    71. Bryan Kelly & Lubos Pastor & Pietro Veronesi, 2014. "The Price of Political Uncertainty: Theory and Evidence from the Option Market," NBER Working Papers 19812, National Bureau of Economic Research, Inc.
    72. P. Manasse & G. Moramarco & G. Trigilia, 2020. "Exchange Rates and Political Uncertainty: The Brexit Case," Working Papers wp1141, Dipartimento Scienze Economiche, Universita' di Bologna.
    73. Francis, Bill B. & Hasan, Iftekhar & Zhu, Yun, 2021. "The impact of political uncertainty on institutional ownership," Journal of Financial Stability, Elsevier, vol. 57(C).
    74. Yi-Hsien Wang & Chin-Tsai Lin, 2008. "Empirical analysis of political uncertainty on TAIEX stock market," Applied Economics Letters, Taylor & Francis Journals, vol. 15(7), pages 545-550.
    75. Wagner, Alexander F. & Zeckhauser, Richard J. & Ziegler, Alexandre, 2017. "Paths to Convergence: Stock Price Behavior after Donald Trump's Election," Working Paper Series rwp17-039, Harvard University, John F. Kennedy School of Government.
    76. Marek Szymański & Grzegorz Wojtalik, 2022. "Wpływ wyborów politycznych na ceny akcji na Giełdzie Papierów Wartościowych w Warszawie," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 3, pages 290-306.
    77. Waisman, Maya & Ye, Pengfei & Zhu, Yun, 2015. "The effect of political uncertainty on the cost of corporate debt," Journal of Financial Stability, Elsevier, vol. 16(C), pages 106-117.
    78. Ángel Pardo Tornero & María Dolores Furió Ortega, 2010. "Politics and elections at the Spanish stock exchange," Working Papers. Serie EC 2010-11, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    79. Mattozzi, Andrea, 2004. "Can we insure against political uncertainty? Evidence from the U.S. Stock Market," Working Papers 1207, California Institute of Technology, Division of the Humanities and Social Sciences.
    80. Montone, Maurizio, 2022. "Does the U.S. president affect the stock market?," Journal of Financial Markets, Elsevier, vol. 61(C).
    81. Chung-Chu Chuang & Yi-Hsien Wang, 2009. "Developed stock market reaction to political change: a panel data analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(6), pages 941-949, November.
    82. Das, Kuntal K. & Yaghoubi, Mona, 2023. "Stock liquidity and firm-level political risk," Finance Research Letters, Elsevier, vol. 51(C).
    83. Pierdzioch, Christian & Döpke, Jörg, 2004. "Politics and the Stock Market: Evidence from Germany," Kiel Working Papers 1203, Kiel Institute for the World Economy (IfW Kiel).
    84. Jonathan Brogaard & Andrew Detzel, 2015. "The Asset-Pricing Implications of Government Economic Policy Uncertainty," Management Science, INFORMS, vol. 61(1), pages 3-18, January.
    85. Acker, Daniella & Duck, Nigel W., 2015. "Political risk, investor attention and the Scottish Independence referendum," Finance Research Letters, Elsevier, vol. 13(C), pages 163-171.
    86. Nicholas Apergis & Ioannis Chatziantoniou, 2022. "US partisan conflict shocks and international stock market returns," Empirical Economics, Springer, vol. 63(6), pages 2817-2854, December.
    87. Wang, Miao & Wong, M. C. Sunny, 2015. "Rational speculative bubbles in the US stock market and political cycles," Finance Research Letters, Elsevier, vol. 13(C), pages 1-9.
    88. Qadan, Mahmoud & Idilbi, Yasmeen, 2022. "Presidential honeymoons, political cycles and the commodity market," Resources Policy, Elsevier, vol. 77(C).
    89. Steven Block & Burkhard N. Schrage & Paul M. Vaaler, 2003. "Democratization???s Risk Premium: Partisan and Opportunistic Political Business Cycle Effects on Sovereign Ratings in Developing Countries," William Davidson Institute Working Papers Series 546, William Davidson Institute at the University of Michigan.
    90. Dorine Boumans & Klaus Gründler & Niklas Potrafke & Fabian Ruthardt, 2021. "The Global Economic Impact of Politicians: Evidence from an International Survey RCT," CESifo Working Paper Series 8833, CESifo.
    91. Bialkowski, Jedrzej & Gottschalk, Katrin & Wisniewski, Tomasz Piotr, 2006. "Political Orientation of Government and Stock Market Returns," Working Paper Series 2006,9, European University Viadrina Frankfurt (Oder), The Postgraduate Research Programme Capital Markets and Finance in the Enlarged Europe.
    92. Kräussl, Roman & Lucas, André & Rijsbergen, David R. & van der Sluis, Pieter Jelle & Vrugt, Evert B., 2014. "Washington meets Wall Street: A closer examination of the presidential cycle puzzle," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 50-69.
    93. Jawad M. Addoum & Alok Kumar, 2016. "Political Sentiment and Predictable Returns," The Review of Financial Studies, Society for Financial Studies, vol. 29(12), pages 3471-3518.
    94. Bülent Köksal & Ahmet Çalışkan, 2012. "Political Business Cycles and Partisan Politics: Evidence from a Developing Economy," Economics and Politics, Wiley Blackwell, vol. 24(2), pages 182-199, July.
    95. Chen, Zilin & Da, Zhi & Huang, Dashan & Wang, Liyao, 2023. "Presidential economic approval rating and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 147(1), pages 106-131.
    96. Chin-Tsai Lin & Yi-Hsien Wang, 2007. "The impact of party alternative on the stock market: the case of Japan," Applied Economics, Taylor & Francis Journals, vol. 39(1), pages 79-85.
    97. Wisniewski, Tomasz Piotr, 2016. "Is there a link between politics and stock returns? A literature survey," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 15-23.
    98. Sojli, Elvira & Tham, Wing Wah, 2015. "Divided governments and futures prices," Journal of Econometrics, Elsevier, vol. 187(2), pages 622-633.
    99. Aliyu, Shehu Usman Rano, 2020. "What have we learnt from modelling stock returns in Nigeria: Higgledy-piggledy?," MPRA Paper 110382, University Library of Munich, Germany, revised 06 Jun 2021.
    100. John Goodell & Richard Bodey, 2012. "Price-earnings changes during US presidential election cycles: voter uncertainty and other determinants," Public Choice, Springer, vol. 150(3), pages 633-650, March.
    101. Yan He & Hai Lin & Chunchi Wu & Uric B. Dufrene, 2013. "The 2000 presidential election and the information cost of sensitive versus," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    102. Roland Füss & Michael Bechtel, 2008. "Partisan politics and stock market performance: The effect of expected government partisanship on stock returns in the 2002 German federal election," Public Choice, Springer, vol. 135(3), pages 131-150, June.
    103. Li, Qingyuan & Li, Si & Xu, Li, 2018. "National elections and tail risk: International evidence," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 113-128.
    104. Chrétien, Stéphane & Coggins, Frank, 2009. "Election outcomes and financial market returns in Canada," The North American Journal of Economics and Finance, Elsevier, vol. 20(1), pages 1-23, March.
    105. Demirer, Riza & Gupta, Rangan, 2018. "Presidential cycles and time-varying bond–stock market correlations: Evidence from more than two centuries of data," Economics Letters, Elsevier, vol. 167(C), pages 36-39.
    106. Hacioglu Hoke, Sinem, 2019. "Macroeconomic effects of political risk shocks," Bank of England working papers 841, Bank of England.
    107. Marshall, Ben R. & Nguyen, Hung T. & Nguyen, Nhut H. & Visaltanachoti, Nuttawat, 2018. "Politics and liquidity," Journal of Financial Markets, Elsevier, vol. 38(C), pages 1-13.
    108. Liston, Daniel Perez, 2016. "Sin stock returns and investor sentiment," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 63-70.
    109. Chin-Tsai Lin & Yi-Hsien Wang, 2005. "An Analysis of Political Changes on Nikkei 225 Stock Returns and Volatilities," Annals of Economics and Finance, Society for AEF, vol. 6(1), pages 169-183, May.
    110. Apergis, Nicholas & Polemis, Michael, 2018. "Electricity supply shocks and economic growth across the US states: evidence from a time-varying Bayesian panel VAR model, aggregate and disaggregate energy sources," MPRA Paper 84954, University Library of Munich, Germany.
    111. John G Powell & Meifen Qian & Jing Shi & Qiaoqiao Zhu, 2015. "Should stock market return forecasts be conditioned on politics?," Australian Journal of Management, Australian School of Business, vol. 40(4), pages 672-700, November.
    112. Alexander F. Wagner & Richard J. Zeckhauser & Alexandre Ziegler, 2017. "Company Stock Reactions to the 2016 Election Shock: Trump, Taxes and Trade," Swiss Finance Institute Research Paper Series 17-06, Swiss Finance Institute.
    113. Chan, Kam Fong & Powell, John G. & Treepongkaruna, Sirimon, 2014. "Currency jumps and crises: Do developed and emerging market currencies jump together?," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 132-157.
    114. Killins, Robert N. & Ngo, Thanh & Wang, Hongxia, 2022. "Politics and equity markets: Evidence from Canada," Journal of Multinational Financial Management, Elsevier, vol. 63(C).
    115. Roman Kraussl & Andre Lucas & David R. Rijsbergen & Pieter Jelle van der Sluis & Evert B. Vrugt, 2013. "Washington Meets Wall Street: A Closer Examination of the Presidential Cylce Puzzle," DEM Discussion Paper Series 13-4, Department of Economics at the University of Luxembourg.
    116. Tiniç, Murat & Savaser, Tanseli, 2020. "Political turmoil and the impact of foreign orders on equity prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    117. IRSHAD Hira, 2017. "Relationship Among Political Instability, Stock Market Returns And Stock Market Volatility," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 12(2), pages 70-99, August.
    118. Gala, Vito D. & Pagliardi, Giovanni & Zenios, Stavros A., 2023. "Global political risk and international stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 78-102.
    119. Camyar, Isa & Ulupinar, Bahar, 2013. "The partisan policy cycle and firm valuation," European Journal of Political Economy, Elsevier, vol. 30(C), pages 92-111.
    120. Ray Sturm, 2013. "Economic policy and the presidential election cycle in stock returns," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 37(2), pages 200-215, April.
    121. Duyvesteyn, Johan & Martens, Martin & Verwijmeren, Patrick, 2016. "Political risk and expected government bond returns," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 498-512.
    122. Yaya, OlaOluwa S & Adekoya, Oluwasegun B. & Adesiyan, Femi, 2020. "The Persistence of Stock Market Returns during the Presidential elections in Nigeria," MPRA Paper 99390, University Library of Munich, Germany.
    123. Chrétien, Stéphane & Fu, Hsuan, 2023. "Presidential cycles in international equity flows and returns," Finance Research Letters, Elsevier, vol. 53(C).
    124. Liu, Yang & Shaliastovich, Ivan, 2022. "Government policy approval and exchange rates," Journal of Financial Economics, Elsevier, vol. 143(1), pages 303-331.
    125. Mnasri, Ayman & Essaddam, Naceur, 2021. "Impact of U.S. presidential elections on stock markets’ volatility: Does incumbent president's party matter?," Finance Research Letters, Elsevier, vol. 39(C).
    126. James Cooley, 2009. "Stock Market Returns and Partisan Political Business Cycles," Departmental Working Papers 0902, Southern Methodist University, Department of Economics.
    127. Goodell, John W. & Vähämaa, Sami, 2013. "US presidential elections and implied volatility: The role of political uncertainty," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1108-1117.
    128. Yaser Abolghasemi & Stanko Dimitrov, 2021. "Determining the causality between U.S. presidential prediction markets and global financial markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4534-4556, July.
    129. Bumba Mukherjee & David Leblang, 2007. "Partisan Politics, Interest Rates And The Stock Market: Evidence From American And British Returns In The Twentieth Century," Economics and Politics, Wiley Blackwell, vol. 19(2), pages 135-167, July.
    130. Gu, Xian & Hasan, Iftekhar & Zhu, Yun, 2019. "Political influence and financial flexibility: Evidence from China," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 142-156.
    131. Grossman, Richard S. & Imai, Masami, 2009. "Japan's return to gold: Turning points in the value of the yen during the 1920s," Explorations in Economic History, Elsevier, vol. 46(3), pages 314-323, July.
    132. Anderson, Warwick & Białkowski, Jędrzej & Wagner, Moritz, 2023. "Midterm elections and stock returns," Finance Research Letters, Elsevier, vol. 55(PA).
    133. Ľuboš Pástor & Pietro Veronesi, 2020. "Political Cycles and Stock Returns," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4011-4045.
    134. Fan Wang, 2018. "Elections, Political Control and Duration of Stock Market Cycles," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201810, University of Kansas, Department of Economics, revised Oct 2018.
    135. Tielmann, Artur & Schiereck, Dirk, 2017. "Arising borders and the value of logistic companies: Evidence from the Brexit referendum in Great Britain," Finance Research Letters, Elsevier, vol. 20(C), pages 22-28.
    136. Cheng, Mengyao, 2022. "Legislative gridlock and stock return dispersion around roll-call votes," Journal of Banking & Finance, Elsevier, vol. 138(C).
    137. Civilize, Sireethorn & Wongchoti, Udomsak & Young, Martin, 2015. "Military regimes and stock market performance," Emerging Markets Review, Elsevier, vol. 22(C), pages 76-95.
    138. Bill Francis & Eric Ofori, 2015. "Political regimes and stock market development," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 5(1), pages 111-137, June.
    139. Bismark Aha & David.M Higgins & Timothy Lee, 2018. "UK Political Cycle and the Effect on National House Prices: An Exploratory Study," ERES eres2018_60, European Real Estate Society (ERES).
    140. Ming-Chi Chen & Chi-Lu Peng & So-De Shyu & Jhih-Hong Zeng, 2012. "Market States and the Effect on Equity REIT Returns due to Changes in Monetary Policy Stance," The Journal of Real Estate Finance and Economics, Springer, vol. 45(2), pages 364-382, August.
    141. Yi-Hsien Wang & Chin-Tsai Lin, 2009. "The political uncertainty and stock market behavior in emerging democracy: the case of Taiwan," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(2), pages 237-248, March.
    142. Luis A. Gil‐Alana & Robert Mudida & OlaOluwa S. Yaya & Kazeem A. Osuolale & Ahamuefula E. Ogbonna, 2021. "Mapping US presidential terms with S&P500 index: Time series analysis approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1938-1954, April.
    143. Yun Zhu, 2015. "Political uncertainty and non-pricing terms of financial contract," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 5(1), pages 77-109, June.
    144. Azmi, Wajahat & Mohamad, Shamsher & Shah, Mohamed Eskandar, 2020. "Ethical investments and financial performance: An international evidence," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    145. Wisniewski, Tomasz P. & Pathan, Saima K., 2014. "Political environment and foreign direct investment: Evidence from OECD countries," European Journal of Political Economy, Elsevier, vol. 36(C), pages 13-23.
    146. Gerasimos G. Rompotis, 2018. "Political Uncertainty and the Greek Stock Market over the Period 2011-2015," Capital Markets Review, Malaysian Finance Association, vol. 26(1), pages 1-18.
    147. Niklas Potrafke, 2017. "Government Ideology and Economic Policy-Making in the United States," CESifo Working Paper Series 6444, CESifo.
    148. Warwick Anderson & Jędrzej Białkowski & Moritz Wagner, 2023. "The midterm election effect on US stock returns: Some practical considerations for investors," Working Papers in Economics 23/05, University of Canterbury, Department of Economics and Finance.
    149. Samar Ashour & David A. Rakowski & Salil K. Sarkar, 2019. "U.S. presidential cycles and the foreign exchange market," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 523-540, October.
    150. Wagner, Alexander F. & Zeckhauser, Richard J. & Ziegler, Alexandre, 2018. "Company stock price reactions to the 2016 election shock: Trump, taxes, and trade," Journal of Financial Economics, Elsevier, vol. 130(2), pages 428-451.
    151. John J. Shon, 2010. "Do Stock Returns Vary With Campaign Contributions? Bush Vs. Gore: The Florida Recount," Economics and Politics, Wiley Blackwell, vol. 22(3), pages 257-281, November.
    152. K. Arin & Alexander Molchanov & Otto Reich, 2013. "Politics, stock markets, and model uncertainty," Empirical Economics, Springer, vol. 45(1), pages 23-38, August.
    153. Killins, Robert N. & Ngo, Thanh & Wang, Hongxia, 2022. "Financial institution IPOs and regulatory environments," Finance Research Letters, Elsevier, vol. 46(PB).
    154. Deari Fitim & Koku Paul Sergius, 2024. "Do Local Political Elections Affect Daily Stock Returns? Evidence from the Republic of North Macedonia's MBI10 Index," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 34(1), pages 98-116, March.
    155. Cooper, Michael J. & McConnell, John J. & Ovtchinnikov, Alexei V., 2006. "The other January effect," Journal of Financial Economics, Elsevier, vol. 82(2), pages 315-341, November.
    156. Po‐Hsuan Hsu & Kai Li & Chi‐Yang Tsou, 2023. "The Pollution Premium," Journal of Finance, American Finance Association, vol. 78(3), pages 1343-1392, June.
    157. Travis L Johnson, 2019. "A Fresh Look at Return Predictability Using a More Efficient Estimator," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 9(1), pages 1-46.
    158. Rizzo, Emanuele, 2018. "Essays on corporate governance and the impact of regulation on financial markets," Other publications TiSEM b5158260-ea13-4763-b992-6, Tilburg University, School of Economics and Management.
    159. Lehrer, Nimrod David, 2018. "The value of political connections in a multiparty parliamentary democracy: Evidence from the 2015 elections in Israel," European Journal of Political Economy, Elsevier, vol. 53(C), pages 13-58.
    160. Cheng, Xu & Kong, Dongmin & Wang, Junbo, 2021. "Political uncertainty and A-H share premium," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    161. Jürgen Huber & Michael Kirchler, 2013. "Corporate campaign contributions and abnormal stock returns after presidential elections," Public Choice, Springer, vol. 156(1), pages 285-307, July.
    162. Anne Duquerroy, 2019. "The Real Effects of Checks and Balances: Policy Uncertainty and Corporate Investment," Working papers 735, Banque de France.
    163. Azimli, Asil, 2022. "The impact of policy, political and economic uncertainty on corporate capital investment in the emerging markets of Eastern Europe and Turkey," Economic Systems, Elsevier, vol. 46(2).
    164. Dirk Swagerman & Ivan Novakovic, 2010. "Multi-National Evidence On Calendar Patterns In Stock Returns: An Empirical Case Study On Investment Strategy And The Halloween Effect," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 4(4), pages 23-42.
    165. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach, 2017. "Momentum strategies for Islamic stocks," Pacific-Basin Finance Journal, Elsevier, vol. 42(C), pages 96-112.
    166. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Bannigidadmath, Deepa, 2017. "Is the profitability of Indian stocks compensation for risks?," Emerging Markets Review, Elsevier, vol. 31(C), pages 47-64.
    167. Gropper, Daniel M. & Jahera, John S. & Park, Jung Chul, 2013. "Does it help to have friends in high places? Bank stock performance and congressional committee chairmanships," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 1986-1999.
    168. Lin, Boqiang & Zhao, Hengsong, 2023. "Tracking policy uncertainty under climate change," Resources Policy, Elsevier, vol. 83(C).
    169. Angelini, Eliana & Foglia, Matteo & Ortolano, Alessandra & Leone, Maria, 2018. "The “Donald” and the market: Is there a cointegration?," Research in International Business and Finance, Elsevier, vol. 45(C), pages 30-37.
    170. Rangan Gupta & Christian Pierdzioch & Refk Selmi & Mark E. Wohar, 2017. "Does Partisan Conflict Predict a Reduction in US Stock Market (Realized) Volatility? Evidence from a Quantile-on-Quantile Regression Model," Working Papers 201744, University of Pretoria, Department of Economics.
    171. Rangan Gupta & Mark E. Wohar, 2019. "Presidential Cycles In The Usa And The Dollar-Pound Exchange Rate: Evidence From Over Two Centuries," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 151-163, June.
    172. Narayan, Paresh Kumar & Narayan, Seema, 2021. "Do opinion polls on government preference influence stock returns?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    173. Daniel Borup & Jorge Wolfgang Hansen & Benjamin Dybro Liengaard & Erik Christian Montes Schütte, 2023. "Quantifying investor narratives and their role during COVID‐19," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 512-532, June.
    174. Rohan Chinchwadkar, 2020. "Political Business Cycles, Elections and Entrepreneurial Finance: A Framework," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(4), pages 941-954, December.
    175. Dai, Lili & Ngo, Phong T. H., 2013. "Political Uncertainty and Accounting Conservatism: Evidence from the U.S. Presidential Election Cycle," MPRA Paper 43606, University Library of Munich, Germany.
    176. Novy-Marx, Robert, 2014. "Predicting anomaly performance with politics, the weather, global warming, sunspots, and the stars," Journal of Financial Economics, Elsevier, vol. 112(2), pages 137-146.
    177. Chong-Chuo Chang & Kuen-Shiou Yang, 2021. "Loose monetary policy and firm uncertainty," SN Business & Economics, Springer, vol. 1(3), pages 1-27, March.
    178. Chien-Liang Chiu & Chun-Da Chen & Wan-Wei Tang, 2005. "Political elections and foreign investor trading in South Korea's financial markets," Applied Economics Letters, Taylor & Francis Journals, vol. 12(11), pages 673-677.
    179. Apergis, Nicholas, 2015. "Policy risks, technological risks and stock returns: New evidence from the US stock market," Economic Modelling, Elsevier, vol. 51(C), pages 359-365.
    180. Rangan Gupta & Mark E. Wohar, 2018. "Presidential Cycles in the United States and the Dollar-Pound Exchange Rate: Evidence from over Two Centuries of Data," Working Papers 201874, University of Pretoria, Department of Economics.
    181. Shanaev, Savva & Ghimire, Binam, 2019. "Is all politics local? Regional political risk in Russia and the panel of stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 21(C), pages 70-82.
    182. Andrew C. Worthington, 2009. "Political Cycles in the Australian Stock Market since Federation," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 42(4), pages 397-409, December.
    183. William T. Chittenden, 2020. "Political Parties In Power And U.S. Economic Performance," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 14(2), pages 21-36.
    184. Paritosh Chandra Sinha, 2021. "Attention to the Election-Economics-Politics (EEP) Nexus in the Indian Stock Markets," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 13(1), pages 7-32, June.

  12. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.

    Cited by:

    1. Martin Lettau & Stijn Van Nieuwerburgh, 2006. "Reconciling the Return Predictability Evidence," 2006 Meeting Papers 29, Society for Economic Dynamics.
    2. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    3. Federico M Bandi & Valentina Corradi & Daniel Wilhelm, 2016. "Possibly Nonstationary Cross-Validation," CeMMAP working papers CWP11/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. David McMillan & Alan Speight, 2006. "Non-linear long horizon returns predictability: evidence from six south-east Asian markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 13(2), pages 95-111, June.
    5. Theologos Dergiades & Panos K. Pouliasis, 2021. "Should Stock Returns Predictability be hooked on Long Horizon Regressions?," Discussion Paper Series 2021_03, Department of Economics, University of Macedonia, revised Feb 2021.
    6. Chang, C-L. & Ilomäki, J. & Laurila, H. & McAleer, M.J., 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Econometric Institute Research Papers EI2018-39, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Lettau, Martin & Ludvigson, Sydney, 2002. "Expected Returns and Expected Dividend Growth," CEPR Discussion Papers 3507, C.E.P.R. Discussion Papers.
    8. Gourieroux, Christian & Jasiak, Joann, 2010. "Inference for Noisy Long Run Component Process," MPRA Paper 98987, University Library of Munich, Germany.
    9. Hjalmarsson, Erik, 2008. "Interpreting long-horizon estimates in predictive regressions," Finance Research Letters, Elsevier, vol. 5(2), pages 104-117, June.
    10. Francesco Ravazzolo & Tommy Sveen & Sepideh K. Zahiri, 2016. "Commodity Futures and Forecasting Commodity Currencies," Working Papers No 7/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Andersen, Torben G. & Varneskov, Rasmus T., 2022. "Testing for parameter instability and structural change in persistent predictive regressions," Journal of Econometrics, Elsevier, vol. 231(2), pages 361-386.
    12. Tim Bollerslev & Hao Zhou, 2006. "Expected stock returns and variance risk premia," Finance and Economics Discussion Series 2007-11, Board of Governors of the Federal Reserve System (U.S.).
    13. Møller, Stig V. & Rangvid, Jesper, 2015. "End-of-the-year economic growth and time-varying expected returns," Journal of Financial Economics, Elsevier, vol. 115(1), pages 136-154.
    14. Scholz, Michael & Nielsen, Jens Perch & Sperlich, Stefan, 2015. "Nonparametric prediction of stock returns based on yearly data: The long-term view," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 143-155.
    15. Ke-Li Xu & Junjie Guo, 2021. "A New Test for Multiple Predictive Regression," CAEPR Working Papers 2022-001 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    16. Jacob Boudoukh & Ronen Israel & Matthew P. Richardson, 2020. "Biases in Long-Horizon Predictive Regressions," NBER Working Papers 27410, National Bureau of Economic Research, Inc.
    17. Favero, Carlo A. & Gozluklu, Arie E. & Tamoni, Andrea, 2011. "Demographic Trends, the Dividend-Price Ratio, and the Predictability of Long-Run Stock Market Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 46(5), pages 1493-1520, October.
    18. Chiquoine, Benjamin & Hjalmarsson, Erik, 2009. "Jackknifing stock return predictions," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 793-803, December.
    19. Nguyen, Hoa & Faff, Robert, 2006. "Foreign debt and financial hedging: Evidence from Australia," International Review of Economics & Finance, Elsevier, vol. 15(2), pages 184-201.
    20. Tom Engsted & Thomas Q. Pedersen, 2009. "The dividend-price ratio does predict dividend growth: International evidence," CREATES Research Papers 2009-36, Department of Economics and Business Economics, Aarhus University.
    21. Federico M Bandi & Valentina Corradi & Daniel Wilhelm, 2016. "Possibly Nonstationary Cross-Validation," CeMMAP working papers 11/16, Institute for Fiscal Studies.
    22. Valkanov, Rossen, 2005. "Functional Central Limit Theorem approximations and the distribution of the Dickey-Fuller test with strongly heteroskedastic data," Economics Letters, Elsevier, vol. 86(3), pages 427-433, March.
    23. Conlon, Thomas & Cotter, John & Eyiah-Donkor, Emmanuel, 2022. "The illusion of oil return predictability: The choice of data matters!," Journal of Banking & Finance, Elsevier, vol. 134(C).
    24. Ferson, Wayne E. & Sarkissian, Sergei & Simin, Timothy, 2008. "Asset Pricing Models with Conditional Betas and Alphas: The Effects of Data Snooping and Spurious Regression," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(2), pages 331-353, June.
    25. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    26. Alex Maynard, 2006. "The forward premium anomaly: statistical artefact or economic puzzle? New evidence from robust tests," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 39(4), pages 1244-1281, November.
    27. Maio, Paulo, 2016. "Cross-sectional return dispersion and the equity premium," Journal of Financial Markets, Elsevier, vol. 29(C), pages 87-109.
    28. Andreas Fuster & Benjamin Hebert & David Laibson, 2012. "Natural Expectations, Macroeconomic Dynamics, and Asset Pricing," NBER Macroeconomics Annual, University of Chicago Press, vol. 26(1), pages 1-48.
    29. Scholz, Michael & Sperlich, Stefan & Nielsen, Jens Perch, 2016. "Nonparametric long term prediction of stock returns with generated bond yields," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 82-96.
    30. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    31. Jean-Yves Pitarakis, 2017. "A Simple Approach for Diagnosing Instabilities in Predictive Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(5), pages 851-874, October.
    32. J. Annaert & W. Van Hyfte, 2006. "Long-Horizon Mean Reversion for the Brussels Stock Exchange: Evidence for the 19th Century," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/376, Ghent University, Faculty of Economics and Business Administration.
    33. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2010. "Regime Specific Predictability in Predictive Regressions," MPRA Paper 29190, University Library of Munich, Germany.
    34. Lars Lochstoer & Harjoat S. Bhamra, 2009. "Return Predictability and Labor Market Frictions in a Real Business Cycle Model," 2009 Meeting Papers 1257, Society for Economic Dynamics.
    35. Henkel, Sam James & Martin, J. Spencer & Nardari, Federico, 2011. "Time-varying short-horizon predictability," Journal of Financial Economics, Elsevier, vol. 99(3), pages 560-580, March.
    36. Meng-Chen Hsieh & Clifford Hurvich & Philippe Soulier, 2022. "Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes," Papers 2202.00793, arXiv.org.
    37. Xyngis, Georgios, 2017. "Business-cycle variation in macroeconomic uncertainty and the cross-section of expected returns: Evidence for scale-dependent risks," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 43-65.
    38. Koijen, R.S.J., 2008. "Essays on asset pricing," Other publications TiSEM 75662994-29dc-4a83-a3ff-9, Tilburg University, School of Economics and Management.
    39. Andrea Buraschi & Andrea Carnelli, 2013. "The economic value of predictability in portfolio management," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 1, pages 5-22, January.
    40. Sarmidi, Tamat, 2008. "Exchange Rates Predictability in Developing Countries," MPRA Paper 16580, University Library of Munich, Germany.
    41. Kaniel, Ron & Yan, Hong & Carlson, Murray & Chapman, David A., 2015. "Asset Return Predictability in a Heterogeneous Agent Equilibrium Model," CEPR Discussion Papers 10328, C.E.P.R. Discussion Papers.
    42. Menkhoff, Lukas & Rebitzky, Rafael R., 2008. "Investor sentiment in the US-dollar: Longer-term, non-linear orientation on PPP," Journal of Empirical Finance, Elsevier, vol. 15(3), pages 455-467, June.
    43. Yu-chin Chen & Kwok Ping Tsang, 2013. "What Does the Yield Curve Tell Us about Exchange Rate Predictability?," The Review of Economics and Statistics, MIT Press, vol. 95(1), pages 185-205, March.
    44. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2018. "Aggregate Expected Investment Growth and Stock Market Returns," ADBI Working Papers 808, Asian Development Bank Institute.
    45. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    46. Fong, Wai Mun, 2012. "Do expected business conditions explain the value premium?," Journal of Financial Markets, Elsevier, vol. 15(2), pages 181-206.
    47. Michael W. Brandt & Qiang Kang, 2002. "On the Relationship Between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach," NBER Working Papers 9056, National Bureau of Economic Research, Inc.
    48. Borja Larrain & Motohiro Yogo, 2007. "Does Firm Value Move Too Much to be Justified by Subsequent Changes in Cash Flow?," NBER Working Papers 12847, National Bureau of Economic Research, Inc.
    49. Frank Schorfheide & Dongho Song & Amir Yaron, 2014. "Identifying Long-Run Risks: A Bayesian Mixed-Frequency Approach," NBER Working Papers 20303, National Bureau of Economic Research, Inc.
    50. Ang, Andrew & Liu, Jun, 2007. "Risk, return, and dividends," Journal of Financial Economics, Elsevier, vol. 85(1), pages 1-38, July.
    51. Raj Aggarwal & Brian M. Lucey & Fergal A. O'Connor, 2014. "Rationality in Precious Metals Forward Markets: Evidence of Behavioural Deviations in the Gold Markets," The Institute for International Integration Studies Discussion Paper Series iiisdp462, IIIS.
    52. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
    53. Giuseppe Alesii, 2006. "Fundamentals Efficiency of the Italian Stock Market: Some Long Run Evidence," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 5(3), pages 245-264, December.
    54. Torben G. Andersen & Rasmus T. Varneskov, 2021. "Consistent Inference for Predictive Regressions in Persistent Economic Systems," NBER Working Papers 28568, National Bureau of Economic Research, Inc.
    55. Pierlauro Lopez, 2017. "Online Appendix to "A New Keynesian Q Theory and the Link Between Inflation and the Stock Market"," Online Appendices 16-134, Review of Economic Dynamics.
    56. Harrison Hong & Frank Weikai Li & Jiangmin Xu, 2016. "Climate Risks and Market Efficiency," NBER Working Papers 22890, National Bureau of Economic Research, Inc.
    57. GIOT, Pierre & PETITJEAN, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," LIDAM Reprints CORE 1982, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    58. McCown, James Ross & Shaw, Ron, 2017. "Investment potential and risk hedging characteristics of platinum group metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 328-337.
    59. Sanders, Dwight R. & Irwin, Scott H., 2014. "Energy futures prices and commodity index investment: New evidence from firm-level position data," Energy Economics, Elsevier, vol. 46(S1), pages 57-68.
    60. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    61. Federico M. Bandi & Bernard Perron & Andrea Tamoni & Claudio Tebaldi, 2014. "The scale of predictability," Working Papers 509, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    62. Schmeling, Maik, 2006. "Institutional and Individual Sentiment: Smart Money and Noise Trader Risk," Hannover Economic Papers (HEP) dp-337, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    63. Huffman, Stephen P. & Makar, Stephen D. & Beyer, Scott B., 2010. "A three-factor model investigation of foreign exchange-rate exposure," Global Finance Journal, Elsevier, vol. 21(1), pages 1-12.
    64. Bosch, David & Pradkhan, Elina, 2015. "The impact of speculation on precious metals futures markets," Resources Policy, Elsevier, vol. 44(C), pages 118-134.
    65. Antonio Montanés & Marcos Sanso-Navarro, "undated". "Another look at long-horizon uncovered interest parity," Studies on the Spanish Economy 221, FEDEA.
    66. Maynard, Alex & Ren, Dongmeng, 2019. "The finite sample power of long-horizon predictive tests in models with financial bubbles," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 418-430.
    67. Paulo M.M. Rodrigues & Antonio Rubia, 2011. "A Class of Robust Tests in Augmented Predictive Regressions," Working Papers w201126, Banco de Portugal, Economics and Research Department.
    68. Noriega Antonio E. & Ventosa-Santaulària Daniel, 2010. "Spurious Long-Horizon Regression in Econometrics," Working Papers 2010-06, Banco de México.
    69. Viale, Ariel M. & Kolari, James W. & Fraser, Donald R., 2009. "Common risk factors in bank stocks," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 464-472, March.
    70. Stephan Jank, 2015. "Changes in the Composition of Publicly Traded Firms: Implications for the Dividend-Price Ratio and Return Predictability," Management Science, INFORMS, vol. 61(6), pages 1362-1377, June.
    71. Patrick J. Coe & James M. Nason, 2004. "Long-run monetary neutrality and long-horizon regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(3), pages 355-373.
    72. Chen, Long, 2009. "On the reversal of return and dividend growth predictability: A tale of two periods," Journal of Financial Economics, Elsevier, vol. 92(1), pages 128-151, April.
    73. Georg Kaltenbrunner & Lars Lochstoer, 2007. "Long-Run Risk through Consumption Smoothing," 2007 Meeting Papers 25, Society for Economic Dynamics.
    74. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    75. David Rey, 2005. "Market Timing And Model Uncertainty: An Exploratory Study For The Swiss Stock Market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(3), pages 239-260, October.
    76. Lioui, Abraham & Poncet, Patrice, 2019. "Long horizon predictability: An asset allocation perspective," European Journal of Operational Research, Elsevier, vol. 278(3), pages 961-975.
    77. Rangvid, Jesper, 2006. "Output and expected returns," Journal of Financial Economics, Elsevier, vol. 81(3), pages 595-624, September.
    78. Nelson C. Mark & Donggyu Sul, 2004. "The Use of Predictive Regressions at Alternative Horizons in Finance and Economics," NBER Technical Working Papers 0298, National Bureau of Economic Research, Inc.
    79. Martin, Anna D. & Mauer, Laurence J., 2005. "A note on common methods used to estimate foreign exchange exposure," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(2), pages 125-140, April.
    80. Campbell, John Y. & Yogo, Motohiro, 2006. "Efficient tests of stock return predictability," Journal of Financial Economics, Elsevier, vol. 81(1), pages 27-60, July.
    81. Bakshi, Gurdip & Panayotov, George & Skoulakis, Georgios, 2011. "Improving the predictability of real economic activity and asset returns with forward variances inferred from option portfolios," Journal of Financial Economics, Elsevier, vol. 100(3), pages 475-495, June.
    82. Ms. Hali J Edison & Mr. Francis E. Warnock, 2003. "Cross-Border Listings, Capital Controls, and U.S. Equity Flows to Emerging Markets," IMF Working Papers 2003/236, International Monetary Fund.
    83. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    84. Esben Hedegaard & Robert J. Hodrick, 2014. "Estimating the Risk-Return Trade-off with Overlapping Data Inference," NBER Working Papers 19969, National Bureau of Economic Research, Inc.
    85. Kraus, Alan & Sagi, Jacob S., 2006. "Asset pricing with unforeseen contingencies," Journal of Financial Economics, Elsevier, vol. 82(2), pages 417-453, November.
    86. Aslanidis, Nektarios & Christiansen, Charlotte, 2014. "Quantiles of the realized stock–bond correlation and links to the macroeconomy," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 321-331.
    87. Bedri Kamil Onur Tas, 2011. "Private information of the Fed and predictability of stock returns," Applied Economics, Taylor & Francis Journals, vol. 43(19), pages 2381-2398.
    88. Hokuto Ishii, 2018. "Modeling and Predictability of Exchange Rate Changes by the Extended Relative Nelson–Siegel Class of Models," IJFS, MDPI, vol. 6(3), pages 1-15, August.
    89. Bonomo, Marco & Garcia, René & Meddahi, Nour & Tédongap, Roméo, 2010. "Generalized Disappointment Aversion, Long Run Volatility Risk and Asset Prices," TSE Working Papers 10-187, Toulouse School of Economics (TSE).
    90. Viceira, Luis M., 2012. "Bond risk, bond return volatility, and the term structure of interest rates," International Journal of Forecasting, Elsevier, vol. 28(1), pages 97-117.
    91. Zhang, Yuzhao, 2014. "Contrarian flows, consumption and expected stock returns," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 101-111.
    92. Sarmidi, Tamat, 2010. "Ringgit Malaysia Predictability: Do Currencies and Prediction Horizon Matters?," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 44, pages 51-60.
    93. Jennie Bai, 2010. "Equity premium predictions with adaptive macro indexes," Staff Reports 475, Federal Reserve Bank of New York.
    94. Maio, Paulo & Santa-Clara, Pedro, 2012. "Multifactor models and their consistency with the ICAPM," Journal of Financial Economics, Elsevier, vol. 106(3), pages 586-613.
    95. Michael Scholz & Jens Perch Nielsen & Stefan Sperlich, 2012. "Nonparametric prediction of stock returns guided by prior knowledge," Graz Economics Papers 2012-02, University of Graz, Department of Economics.
    96. Ludvigson, Sydney C., 2013. "Advances in Consumption-Based Asset Pricing: Empirical Tests," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 799-906, Elsevier.
    97. Qiu, Mei & Pinfold, John F. & Rose, Lawrence C., 2011. "Predicting foreign exchange movements using historic deviations from PPP," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 485-497, October.
    98. Hjalmarsson, Erik, 2012. "Some curious power properties of long-horizon tests," Finance Research Letters, Elsevier, vol. 9(2), pages 81-91.
    99. Joakim Westerlund, 2008. "Panel cointegration tests of the Fisher effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 193-233.
    100. Nikolaos Mitianoudis & Theologos Dergiades, 2016. "Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain," Discussion Paper Series 2016_04, Department of Economics, University of Macedonia, revised Dec 2016.
    101. Sanjay Sehgal & Tarunika Jain Agrawal, 2017. "Bank Risk Factors and Changing Risk Exposures in the Pre- and Post-financial Crisis Periods: An Empirical Study for India," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 42(4), pages 356-378, November.
    102. Katsumi Shimotsu & Alex Maynard, 2004. "Covariance-based orthogonality tests for regressors with unknown persistence," Econometric Society 2004 North American Summer Meetings 536, Econometric Society.
    103. Satadru Hore, 2015. "Equilibrium Predictability, Term Structure of Equity Premia, and Other Return Characteristics," Review of Finance, European Finance Association, vol. 19(1), pages 423-466.
    104. Cedric Okou & Eric Jacquier, 2014. "Horizon Effect in the Term Structure of Long-Run Risk-Return Trade-Offs," CIRANO Working Papers 2014s-36, CIRANO.
    105. Jank, Stephan, 2012. "Changes in the composition of publicly traded firms: Implications for the dividend-price ratio and return predictability," CFR Working Papers 12-08, University of Cologne, Centre for Financial Research (CFR).
    106. Lloyd, Simon & Marin, Emile, 2020. "Exchange rate risk and business cycles," Bank of England working papers 872, Bank of England.
    107. Richard K. Crump & Stefano Eusepi & Emanuel Moench, 2016. "The term structure of expectations and bond yields," Staff Reports 775, Federal Reserve Bank of New York.
    108. Miguel A. Ferreira & Pedro Santa-Clara, 2008. "Forecasting Stock Market Returns: The Sum of the Parts is More than the Whole," NBER Working Papers 14571, National Bureau of Economic Research, Inc.
    109. Lawrenz, Jochen & Zorn, Josef, 2018. "Decomposing the predictive power of local and global financial valuation ratios," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 137-149.
    110. Ralph S.J. Koijen & Stijn Van Nieuwerburgh, 2011. "Predictability of Returns and Cash Flows," Annual Review of Financial Economics, Annual Reviews, vol. 3(1), pages 467-491, December.
    111. Ioannis Kyriakou & Parastoo Mousavi & Jens Perch Nielsen & Michael Scholz, 2021. "Forecasting benchmarks of long-term stock returns via machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 221-240, February.
    112. Laimutė Urbšienė & Andrius Bugajevas & Marekas Pipiras, 2016. "The Impact Of Investment Horizon On The Return And Risk Of Investments In Securities In Lithuania," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 7(2).
    113. Andrew Ang & Geert Bekaert, 2001. "Stock Return Predictability: Is it There?," NBER Working Papers 8207, National Bureau of Economic Research, Inc.
    114. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    115. Barbara Rossi, 2007. "Expectations hypotheses tests at Long Horizons," Econometrics Journal, Royal Economic Society, vol. 10(3), pages 554-579, November.
    116. Becker, Sascha O. & Hoffmann, Mathias, 2006. "Intra- and international risk-sharing in the short run and the long run," European Economic Review, Elsevier, vol. 50(3), pages 777-806, April.
    117. Krivenko, Pavel, 2023. "Asset prices in a labor search model with confidence shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    118. Maio, Paulo & Xu, Danielle, 2020. "Cash-flow or return predictability at long horizons? The case of earnings yield," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 172-192.
    119. Eric Jacquier & Cedric Okou, 2013. "Disentangling Continuous Volatility from Jumps in Long-Run Risk-Return Relationships," CIRANO Working Papers 2013s-14, CIRANO.
    120. Bandi, Federico M. & Perron, Benoît, 2008. "Long-run risk-return trade-offs," Journal of Econometrics, Elsevier, vol. 143(2), pages 349-374, April.
    121. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    122. Jiang, Xiaoquan & Lee, Bong-Soo, 2014. "The intertemporal risk-return relation: A bivariate model approach," Journal of Financial Markets, Elsevier, vol. 18(C), pages 158-181.
    123. Amit Goyal & Ivo Welch, 2004. "A Comprehensive Look at the Empirical Performance of Equity Premium Prediction," Yale School of Management Working Papers amz2412, Yale School of Management, revised 01 Jan 2006.
    124. Eric Ghysels & Alberto Plazzi & Rossen Valkanov, 2007. "Valuation in US Commercial Real Estate," European Financial Management, European Financial Management Association, vol. 13(3), pages 472-497, June.
    125. Okou, Cédric & Jacquier, Éric, 2016. "Horizon effect in the term structure of long-run risk-return trade-offs," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 445-466.
    126. Ghattassi, Imen, 2008. "On the predictive power of the surplus consumption ratio," Finance Research Letters, Elsevier, vol. 5(1), pages 21-31, March.
    127. Müller, Ulrich K. & Watson, Mark W., 2013. "Low-frequency robust cointegration testing," Journal of Econometrics, Elsevier, vol. 174(2), pages 66-81.
    128. Tae-Hwy Lee & Eric Hillebrand & Marcelo Medeiros, 2014. "Bagging Constrained Equity Premium Predictors," Working Papers 201421, University of California at Riverside, Department of Economics, revised Feb 2013.
    129. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    130. Hong, Harrison & Torous, Walter & Valkanov, Rossen, 2007. "Do industries lead stock markets?," Journal of Financial Economics, Elsevier, vol. 83(2), pages 367-396, February.
    131. Hualde Javier & Iacone Fabrizio, 2012. "First Stage Estimation of Fractional Cointegration," Journal of Time Series Econometrics, De Gruyter, vol. 4(1), pages 1-32, May.
    132. Erik Hjalmarsson, 2006. "Inference in Long-Horizon Regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.).
    133. Horia – Dumitru CRISTEA & Cecilia – Nicoleta ANIS, 2012. "Sectoral Study of the Correlation Risk – Return for Romanian Companies," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 289-292.
    134. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544, World Scientific Publishing Co. Pte. Ltd..
    135. Liu, Guannan & Yao, Shuang, 2020. "A robust test for predictability with unknown persistence," Economics Letters, Elsevier, vol. 189(C).
    136. Jaime Casassus & Freddy Higuera, 2011. "Stock Return Predictability and Oil Prices," Documentos de Trabajo 406, Instituto de Economia. Pontificia Universidad Católica de Chile..
    137. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    138. Park, Cheolbeom, 2010. "When does the dividend-price ratio predict stock returns?," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 81-101, January.
    139. Maik Schmeling & Andreas Schrimpf, 2008. "Expected Inflation, Expected Stock Returns, and Money Illusion: What can we learn from Survey Expectations?," SFB 649 Discussion Papers SFB649DP2008-036, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    140. M. Max Croce & Tatyana Marchuk & Christian Schlag, 2019. "The Leading Premium," NBER Working Papers 25633, National Bureau of Economic Research, Inc.
    141. Hjalmarsson, Erik, 2005. "On the Predictability of Global Stock Returns," Working Papers in Economics 161, University of Gothenburg, Department of Economics.
    142. Hunter, Delroy M., 2006. "The evolution of stock market integration in the post-liberalization period - A look at Latin America," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 795-826, August.
    143. Lawrenz, Jochen & Zorn, Josef, 2017. "Predicting international stock returns with conditional price-to-fundamental ratios," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 159-184.
    144. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    145. D. Ventosa-Santaulària, 2009. "Spurious Regression," Journal of Probability and Statistics, Hindawi, vol. 2009, pages 1-27, August.
    146. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    147. Ghattassi, I., 2013. "Surplus Consumption Ratio and Expected Stock Returns," Working papers 417, Banque de France.
    148. Galsband, Victoria, 2012. "Downside risk of international stock returns," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2379-2388.
    149. Wang, Cindy Shin-Huei & Hafner, Christian, 2018. "A simple solution of the spurious regression problem," LIDAM Reprints ISBA 2018044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    150. Jacob Boudoukh & Matthew Richardson & Robert F. Whitelaw, 2008. "The Myth of Long-Horizon Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1577-1605, July.
    151. Choi, Yongok & Jacewitz, Stefan & Park, Joon Y., 2016. "A reexamination of stock return predictability," Journal of Econometrics, Elsevier, vol. 192(1), pages 168-189.
    152. Lei Tan & Bo Zheng & Jun-Jie Chen & Xiong-Fei Jiang, 2015. "How Volatilities Nonlocal in Time Affect the Price Dynamics in Complex Financial Systems," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-16, February.
    153. Oglend, Atle, 2022. "The commodities/equities beta term-structure," Journal of Commodity Markets, Elsevier, vol. 28(C).
    154. Michael Scholz & Stefan Sperlich & Jens Perch Nielsen, 2012. "Nonparametric prediction of stock returns with generated bond yields," Graz Economics Papers 2012-10, University of Graz, Department of Economics.
    155. Wright, Jonathan H. & Zhou, Hao, 2009. "Bond risk premia and realized jump risk," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2333-2345, December.
    156. Zhu, Xiaoneng & Zhu, Jie, 2013. "Predicting stock returns: A regime-switching combination approach and economic links," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4120-4133.
    157. Phillips, Peter C.B. & Lee, Ji Hyung, 2013. "Predictive regression under various degrees of persistence and robust long-horizon regression," Journal of Econometrics, Elsevier, vol. 177(2), pages 250-264.
    158. Sizova, Natalia, 2014. "A frequency-domain alternative to long-horizon regressions with application to return predictability," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 261-272.
    159. Yang Bai, 2022. "150 Years of Return Predictability Around the World: A Holistic View," Papers 2209.00121, arXiv.org.
    160. Croce, Mariano & Schlag, Christian & Marchuk, Tatyana, 2018. "The Leading Premium," CEPR Discussion Papers 12631, C.E.P.R. Discussion Papers.
    161. Francesca Carrieri & Vihang Errunza & Sergei Sarkissian, 2004. "Industry Risk and Market Integration," Management Science, INFORMS, vol. 50(2), pages 207-221, February.
    162. Huang, Dashan & Li, Jiangyuan & Wang, Liyao & Zhou, Guofu, 2020. "Time series momentum: Is it there?," Journal of Financial Economics, Elsevier, vol. 135(3), pages 774-794.
    163. Møller, Stig V. & Sander, Magnus, 2017. "Dividends, earnings, and predictability," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 153-163.
    164. Deng, Kaihua, 2016. "A refined asymptotic framework for dividend yield in predictive regressions," Economics Letters, Elsevier, vol. 138(C), pages 60-63.
    165. Collard, Fabrice & Feve, Patrick & Ghattassi, Imen, 2006. "Predictability and habit persistence," Journal of Economic Dynamics and Control, Elsevier, vol. 30(11), pages 2217-2260, November.
    166. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    167. Mototsugu Shintani & Tomoyoshi Yabu & Daisuke Nagakura, 2008. "Spurious Regressions in Technical Trading: Momentum or Contrarian?," IMES Discussion Paper Series 08-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    168. Adrian Austin & Swarna Dutt, 2015. "Exchange Rates and Fundamentals: A New Look at the Evidence on Long-Horizon Predictability," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 43(1), pages 147-159, March.
    169. Shintani, Mototsugu & Yabu, Tomoyoshi & Nagakura, Daisuke, 2012. "Spurious regressions in technical trading," Journal of Econometrics, Elsevier, vol. 169(2), pages 301-309.
    170. John Hatgioannides & Spiros Mesomeris, 2005. "Mean Reversion in Equity Prices: the G-7 Evidence," Money Macro and Finance (MMF) Research Group Conference 2005 64, Money Macro and Finance Research Group.
    171. Kim Kaivanto & Peng Zhang, 2019. "Investor Sentiment as a Predictor of Market Returns," Working Papers 268005798, Lancaster University Management School, Economics Department.
    172. Liu, Naiping & Zhang, Lu, 2008. "Is the value spread a useful predictor of returns?," Journal of Financial Markets, Elsevier, vol. 11(3), pages 199-227, August.
    173. Ventosa-Santaulària, Daniel & Noriega, Antonio E., 2015. "Long-run monetary neutrality under stochastic and deterministic trends," Economic Modelling, Elsevier, vol. 47(C), pages 372-382.
    174. Stig V. Møller & Jesper Rangvid, 2012. "End-of-the-year economic growth and time-varying expected returns," CREATES Research Papers 2012-42, Department of Economics and Business Economics, Aarhus University.
    175. Ming, Lei & Song, Wuqi & Dong, Minyi, 2023. "Revisiting time series momentum in China's commodity futures market: Evidence on sources of momentum profits," Economic Modelling, Elsevier, vol. 128(C).
    176. Irwin, Scott H. & Sanders, Dwight R., 2012. "Testing the Masters Hypothesis in commodity futures markets," Energy Economics, Elsevier, vol. 34(1), pages 256-269.
    177. Paye, Bradley S. & Timmermann, Allan, 2006. "Instability of return prediction models," Journal of Empirical Finance, Elsevier, vol. 13(3), pages 274-315, June.
    178. Hicham Benjelloun, 2011. "About stock markets predictability," Journal of Economics and Behavioral Studies, AMH International, vol. 1(1), pages 26-31.
    179. Engsted, Tom & Hyde, Stuart & Møller, Stig V., 2010. "Habit formation, surplus consumption and return predictability: International evidence," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1237-1255, November.
    180. Chen, Chaoyi & Gospodinov, Nikolay & Maynard, Alex & Pesavento, Elena, 2022. "Long-horizon stock valuation and return forecasts based on demographic projections," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 190-215.
    181. Jin Lee, 2005. "Long horizon regressions with moderate deviations from a unit root," Economics Bulletin, AccessEcon, vol. 3(52), pages 1-11.

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