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Citations for "Predicting volatility: getting the most out of return data sampled at different frequencies"

by Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen

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  1. 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.
  2. 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".
  3. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
  4. Gregory H. Bauer & Keith Vorkink, 2007. "Multivariate Realized Stock Market Volatility," Working Papers 07-20, Bank of Canada.
  5. 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.
  6. Pedregal, Diego J. & Pérez, Javier J., 2008. "Should quarterly government finance statistics be used for fiscal surveillane in Europe?," Working Paper Series 0937, European Central Bank.
  7. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Publishing House "SINERGIA PRESS", Publishing House "SINERGIA PRESS", vol. 15(3), pages 94-138.
  8. Chaboud, Alain P. & Chiquoine, Benjamin & Hjalmarsson, Erik & Loretan, Mico, 2010. "Frequency of observation and the estimation of integrated volatility in deep and liquid financial markets," Journal of Empirical Finance, Elsevier, Elsevier, vol. 17(2), pages 212-240, March.
  9. 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.
  10. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Bandi, Federico M. & Russell, Jeffrey R. & Yang, Chen, 2008. "Realized volatility forecasting and option pricing," Journal of Econometrics, Elsevier, Elsevier, vol. 147(1), pages 34-46, November.
  16. Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
  17. Christoffersen, Peter & Mazzotta, Stefano, 2004. "The informational content of over-the-counter currency options," Working Paper Series 0366, European Central Bank.
  18. Andreou, Elena & Ghysels, Eric, 2006. "Monitoring disruptions in financial markets," Journal of Econometrics, Elsevier, Elsevier, vol. 135(1-2), pages 77-124.
  19. Ghysels, Eric & Sohn, Bumjean, 2009. "Which power variation predicts volatility well?," Journal of Empirical Finance, Elsevier, Elsevier, vol. 16(4), pages 686-700, September.
  20. Falk Brauning & Siem Jan Koopman, 2012. "Forecasting Macroeconomic Variables using Collapsed Dynamic Factor Analysis," Tinbergen Institute Discussion Papers 12-042/4, Tinbergen Institute.
  21. Qian, Hang, 2010. "Linear regression using both temporally aggregated and temporally disaggregated data: Revisited," MPRA Paper 32686, University Library of Munich, Germany.
  22. Audrino, Francesco, 2011. "Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks," Economics Working Paper Series 1112, University of St. Gallen, School of Economics and Political Science.
  23. John M. Maheu & Thomas H. McCurdy, 2009. "Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?," Working Paper Series, The Rimini Centre for Economic Analysis 19_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  24. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
  25. Nave, Juan & Rubio Irigoyen, Gonzalo & León, Angel, 2005. "The Relationship between Risk and Expected Return in Europe," DFAEII Working Papers 2005-08, University of the Basque Country - Department of Foundations of Economic Analysis II.
  26. Ghysels, Eric & Sinko, Arthur, 2011. "Volatility forecasting and microstructure noise," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 257-271, January.
  27. Guérin, Pierre & Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
  28. 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, School of Economics and Management, University of Aarhus.
  29. Torben G. Andersen & Tim Bollerslev & 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," PIER Working Paper Archive 03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003.
  30. Çelik, Sibel & Ergin, Hüseyin, 2014. "Volatility forecasting using high frequency data: Evidence from stock markets," Economic Modelling, Elsevier, vol. 36(C), pages 176-190.
  31. Michael McAleer & Marcelo Medeiros, 2008. "Realized Volatility: A Review," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 27(1-3), pages 10-45.
  32. 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.
  33. Hautsch, Nikolaus & Kyj, Lada M. & Malec, Peter, 2011. "The merit of high-frequency data in portfolio allocation," CFS Working Paper Series 2011/24, Center for Financial Studies (CFS).
  34. Ralf Becker & Adam Clements, 2007. "Forecasting stock market volatility conditional on macroeconomic conditions," NCER Working Paper Series, National Centre for Econometric Research 18, National Centre for Econometric Research.
  35. 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.
  36. Chun Liu & John M. Maheu, 2008. "Are There Structural Breaks in Realized Volatility?," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 6(3), pages 326-360, Summer.
  37. 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.
  38. Visser, Marcel P., 2008. "Garch Parameter Estimation Using High-Frequency Data," MPRA Paper 9076, University Library of Munich, Germany.
  39. Fulvio Corsi & Davide Pirino & Roberto Renò, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Post-Print peer-00741630, HAL.
  40. J. Isaac Miller, 2012. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Working Papers, Department of Economics, University of Missouri 1211, Department of Economics, University of Missouri.
  41. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
  42. Cecilia Frale & Libero Monteforte, 2011. "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area 788, Bank of Italy, Economic Research and International Relations Area.
  43. Baele, Lieven & Londono, Juan M., 2013. "Understanding industry betas," Journal of Empirical Finance, Elsevier, Elsevier, vol. 22(C), pages 30-51.
  44. Adam Clements & Ralf Becker, 2009. "A nonparametric approach to forecasting realized volatility," NCER Working Paper Series, National Centre for Econometric Research 43, National Centre for Econometric Research.
  45. Adam E Clements & Ayesha Scott & Annastiina Silvennoinen, 2012. "Forecasting multivariate volatility in larger dimensions: some practical issues," NCER Working Paper Series, National Centre for Econometric Research 80, National Centre for Econometric Research.
  46. 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.
  47. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
  48. Ole E. Barndorff-Nielsen & Neil Shephard, 2005. "Variation, jumps, market frictions and high frequency data in financial econometrics," OFRC Working Papers Series 2005fe08, Oxford Financial Research Centre.
  49. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Practical Volatility and Correlation Modeling for Financial Market Risk Management," NBER Working Papers 11069, National Bureau of Economic Research, Inc.
  50. 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.
  51. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2003. "There is a Risk-Return Tradeoff After All," CIRANO Working Papers 2003s-26, CIRANO.
  52. 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.
  53. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," CREATES Research Papers 2014-12, School of Economics and Management, University of Aarhus.
  54. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
  55. Wang, Jianxin & Yang, Minxian, 2009. "Asymmetric volatility in the foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, Elsevier, vol. 19(4), pages 597-615, October.
  56. Fabian Krueger & Frieder Mokinski & Winfried Pohlmeier, 2011. "Combining Survey Forecasts and Time Series Models: The Case of the Euribor," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 231(1), pages 63-81, February.
  57. Francisco Blasques & Siem Jan Koopman & and Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.
  58. Anthony S. Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Development Economics Working Papers 22482, East Asian Bureau of Economic Research.
  59. 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.
  60. Hooper, Vincent J. & Ng, Kevin & Reeves, Jonathan J., 2008. "Quarterly beta forecasting: An evaluation," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(3), pages 480-489.
  61. Torben G. Andersen & Viktor Todorov, 2009. "Realized Volatility and Multipower Variation," CREATES Research Papers 2009-49, School of Economics and Management, University of Aarhus.
  62. Cheng, Ai-Ru & Jahan-Parvar, Mohammad R., 2014. "Risk–return trade-off in the pacific basin equity markets," Emerging Markets Review, Elsevier, Elsevier, vol. 18(C), pages 123-140.
  63. 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.
  64. repec:hal:wpaper:hal-00583372 is not listed on IDEAS
  65. 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.
  66. 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.
  67. Henker, Thomas & Husodo, Zaäfri A., 2010. "Noise and efficient variance in the Indonesia Stock Exchange," Pacific-Basin Finance Journal, Elsevier, Elsevier, vol. 18(2), pages 199-216, April.
  68. 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.
  69. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, Elsevier, vol. 26(2), pages 293-311, April.
  70. 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.
  71. Elena Andreou & Eric Ghysels, 2004. "Monitoring for Disruptions in Financial Markets," CIRANO Working Papers 2004s-26, CIRANO.
  72. Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers, Rutgers University, Department of Economics 201315, Rutgers University, Department of Economics.
  73. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena, Department of Economics, University of Siena 534, Department of Economics, University of Siena.
  74. Bauer, Gregory H. & Vorkink, Keith, 2011. "Forecasting multivariate realized stock market volatility," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 93-101, January.
  75. repec:lan:wpaper:3324 is not listed on IDEAS
  76. 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.
  77. 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, School of Economics, University of East Anglia, Norwich, UK. 056, School of Economics, University of East Anglia, Norwich, UK..
  78. Anderson, Evan W. & Ghysels, Eric & Juergens, Jennifer L., 2009. "The impact of risk and uncertainty on expected returns," Journal of Financial Economics, Elsevier, Elsevier, vol. 94(2), pages 233-263, November.
  79. Anders B. Trolle & Eduardo S. Schwartz, 2010. "An Empirical Analysis of the Swaption Cube," NBER Working Papers 16549, National Bureau of Economic Research, Inc.
  80. 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.
  81. 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.
  82. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2008. "Quantile forecasts of daily exchange rate returns from forecasts of realized volatility," Journal of Empirical Finance, Elsevier, Elsevier, vol. 15(4), pages 729-750, September.
  83. Patton, Andrew J & Ramadorai, Tarun, 2011. "On the High-Frequency Dynamics of Hedge Fund Risk Exposures," CEPR Discussion Papers 8479, C.E.P.R. Discussion Papers.
  84. Teresa Leal & Diego Pedregal & Javier Pérez, 2011. "Short-term monitoring of the Spanish government balance," SERIEs, Spanish Economic Association, vol. 2(1), pages 97-119, March.
  85. 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.
  86. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 220-234, January.
  87. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC-IPTS Working Papers JRC84138, Institute for Prospective and Technological Studies, Joint Research Centre.
  88. 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, Elsevier, vol. 25(2), pages 259-281.
  89. repec:lan:wpaper:3046 is not listed on IDEAS
  90. Sévi, Benoît, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Economic Modelling, Elsevier, vol. 31(C), pages 189-197.
  91. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," International Econometric Review (IER), Econometric Research Association, Econometric Research Association, vol. 2(2), pages 73-96, September.
  92. 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.
  93. Girardin, Eric & Joyeux, Roselyne, 2013. "Macro fundamentals as a source of stock market volatility in China: A GARCH-MIDAS approach," Economic Modelling, Elsevier, vol. 34(C), pages 59-68.
  94. 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.
  95. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
  96. 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.
  97. Bandi, Federico M. & Russell, Jeffrey R., 2011. "Market microstructure noise, integrated variance estimators, and the accuracy of asymptotic approximations," Journal of Econometrics, Elsevier, Elsevier, vol. 160(1), pages 145-159, January.
  98. Aue, Alexander & Horváth, Lajos & Hurvich, Clifford & Soulier, Philippe, 2014. "Limit Laws In Transaction-Level Asset Price Models," Econometric Theory, Cambridge University Press, vol. 30(03), pages 536-579, June.
  99. Clements, Michael P & Galvão, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data : Forecasting US output growth and inflation," The Warwick Economics Research Paper Series (TWERPS) 773, University of Warwick, Department of Economics.
  100. 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.
  101. Wong, Wing-Keung & McAleer, Michael, 2009. "Mapping the Presidential Election Cycle in US stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, Elsevier, vol. 79(11), pages 3267-3277.
  102. 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.
  103. Eric Ghysels & Jonathan H. Wright, 2006. "Forecasting professional forecasters," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2006-10, Board of Governors of the Federal Reserve System (U.S.).
  104. 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.
  105. Christian T. Brownlees & Giampiero M. Gallo, 2010. "Comparison of Volatility Measures: a Risk Management Perspective," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 8(1), pages 29-56, Winter.
  106. Nielsen, Morten Ørregaard & Frederiksen, Per, 2008. "Finite sample accuracy and choice of sampling frequency in integrated volatility estimation," Journal of Empirical Finance, Elsevier, Elsevier, vol. 15(2), pages 265-286, March.
  107. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series, Federal Reserve Bank of Chicago WP-08-14, Federal Reserve Bank of Chicago.
  108. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2013. "Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?," SFB 649 Discussion Papers SFB649DP2013-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  109. Neville Francis, 2012. "The Low-Frequency Impact of Daily Monetary Policy Shock," 2012 Meeting Papers, Society for Economic Dynamics 198, Society for Economic Dynamics.
  110. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
  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. 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, Research Centre.
  113. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 521-536.