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Predictive regressions with time-varying coefficients

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Cited by:

  1. Wang, Yudong & Hao, Xianfeng, 2023. "Forecasting the real prices of crude oil: What is the role of parameter instability?," Energy Economics, Elsevier, vol. 117(C).
  2. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
  3. Liya Chu & Xue-Zhong He & Kai Li & Jun Tu, 2022. "Investor Sentiment and Paradigm Shifts in Equity Return Forecasting," Management Science, INFORMS, vol. 68(6), pages 4301-4325, June.
  4. Patrick Hable & Patrick Launhardt, 2020. "Aggregate insider trading and the prediction of corporate credit spread changes," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(1), pages 1-31, March.
  5. Baral, Srijana & Mei, Bin, 2023. "Inflation hedging effectiveness of farmland and timberland assets in the United States," Forest Policy and Economics, Elsevier, vol. 151(C).
  6. Li, Jiahan & Tsiakas, Ilias, 2017. "Equity premium prediction: The role of economic and statistical constraints," Journal of Financial Markets, Elsevier, vol. 36(C), pages 56-75.
  7. Lansing, Kevin J. & LeRoy, Stephen F. & Ma, Jun, 2022. "Examining the sources of excess return predictability: Stochastic volatility or market inefficiency?," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 50-72.
  8. Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
  9. Davide Pettenuzzo & Francesco Ravazzolo, 2016. "Optimal Portfolio Choice Under Decision‐Based Model Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1312-1332, November.
  10. Florian Huber & Gregor Kastner & Martin Feldkircher, 2019. "Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 621-640, August.
  11. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
  12. Odendahl, Florens & Rossi, Barbara & Sekhposyan, Tatevik, 2023. "Evaluating forecast performance with state dependence," Journal of Econometrics, Elsevier, vol. 237(2).
  13. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
  14. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  15. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Van Huy Pham & Jang-Eui Hong & Keun Ho Ryu & Nipon Theera-Umpon, 2022. "Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
  16. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
  17. 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.
  18. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
  19. Potanin, Bogdan & Trifonov, Juri, 2021. "The influence of investors’ expectations on oil prices," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 76-90.
  20. Korobilis, Dimitris & Koop, Gary, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Essex Finance Centre Working Papers 22665, University of Essex, Essex Business School.
  21. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  22. Wen, Chufu & Zhu, Haoyang & Dai, Zhifeng, 2023. "Forecasting commodity prices returns: The role of partial least squares approach," Energy Economics, Elsevier, vol. 125(C).
  23. Gurdip Bakshi & Xiaohui Gao & Alberto G. Rossi, 2019. "Understanding the Sources of Risk Underlying the Cross Section of Commodity Returns," Management Science, INFORMS, vol. 65(2), pages 619-641, February.
  24. Kejin Wu & Sayar Karmakar, 2023. "A model-free approach to do long-term volatility forecasting and its variants," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
  25. Antonio Gargano & Davide Pettenuzzo & Allan Timmermann, 2019. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Management Science, INFORMS, vol. 65(2), pages 508-540, February.
  26. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
  27. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
  28. Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
  29. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
  30. Gupta, Rangan & Hammoudeh, Shawkat & Modise, Mampho P. & Nguyen, Duc Khuong, 2014. "Can economic uncertainty, financial stress and consumer sentiments predict U.S. equity premium?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 367-378.
  31. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
  32. Daniel Mantilla-García & Vijay Vaidyanathan, 2017. "Predicting stock returns in the presence of uncertain structural changes and sample noise," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(3), pages 357-391, August.
  33. Casas Villalba, Maria Isabel & Mao, Xiuping & Lopes Moreira Da Veiga, María Helena, 2020. "Adaptative predictability of stock market returns," DES - Working Papers. Statistics and Econometrics. WS 31648, Universidad Carlos III de Madrid. Departamento de Estadística.
  34. Russell Davidson & Niels S. Grønborg, 2018. "Time-varying parameters: New test tailored to applications in finance and macroeconomics," CREATES Research Papers 2018-22, Department of Economics and Business Economics, Aarhus University.
  35. Artur Doshchyn, 2023. "Sinking Ships: Illiquidity and the Predictability of Returns on Real Assets in Recessions," Economics Series Working Papers 1028, University of Oxford, Department of Economics.
  36. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2020. "Bayesian Modelling of TVP-VARs Using Regression Trees," Working Papers 2308, University of Strathclyde Business School, Department of Economics, revised Aug 2023.
  37. Gary Koop & Lise Tole, 2013. "Forecasting the European carbon market," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, June.
  38. Davide Pettenuzzo & Riccardo Sabbatucci & Allan Timmermann, 2020. "Cash Flow News and Stock Price Dynamics," Journal of Finance, American Finance Association, vol. 75(4), pages 2221-2270, August.
  39. Yin, Anwen, 2020. "Equity premium prediction and optimal portfolio decision with Bagging," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
  40. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2017. "International stock return predictability: Is the role of U.S. time-varying?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 121-146, February.
  41. Nonejad, Nima, 2017. "Forecasting aggregate stock market volatility using financial and macroeconomic predictors: Which models forecast best, when and why?," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 131-154.
  42. 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.
  43. Bitto, Angela & Frühwirth-Schnatter, Sylvia, 2019. "Achieving shrinkage in a time-varying parameter model framework," Journal of Econometrics, Elsevier, vol. 210(1), pages 75-97.
  44. Stefano Grassi & Nima Nonejad & Paolo Santucci De Magistris, 2017. "Forecasting With the Standardized Self‐Perturbed Kalman Filter," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 318-341, March.
  45. João F. Caldeira & Rangan Gupta & Hudson S. Torrent, 2020. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
  46. Chen, Yong & Da, Zhi & Huang, Dayong, 2022. "Short selling efficiency," Journal of Financial Economics, Elsevier, vol. 145(2), pages 387-408.
  47. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
  48. Sander, Magnus, 2018. "Market timing over the business cycle," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 130-145.
  49. Shuo Cao, 2018. "Learning about Term Structure Predictability under Uncertainty," GRU Working Paper Series GRU_2018_006, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
  50. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
  51. Massimo Guidolin & Manuela Pedio, 2020. "Distilling Large Information Sets to Forecast Commodity Returns: Automatic Variable Selection or HiddenMarkov Models?," BAFFI CAREFIN Working Papers 20140, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  52. Amélie Charles & Olivier Darné & Jae H. Kim, 2016. "Stock Return Predictability: Evaluation based on prediction intervals," Working Papers hal-01295037, HAL.
  53. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
  54. José Afonso Faias & Tiago Castel-Branco, 2018. "Out-Of-Sample Stock Return Prediction Using Higher-Order Moments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 1-27, September.
  55. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
  56. 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.
  57. Fong, Tom Pak Wing & Wu, Shui Tang, 2020. "Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
  58. 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.
  59. Boudoukh, Jacob & Israel, Ronen & Richardson, Matthew, 2022. "Biases in long-horizon predictive regressions," Journal of Financial Economics, Elsevier, vol. 145(3), pages 937-969.
  60. Umit Ozmel & Deniz Yavuz & Tim Trombley & Ranjay Gulati, 2020. "Interfirm Ties Between Ventures and Limited Partners of Venture Capital Funds: Performance Effects in Financial Markets," Organization Science, INFORMS, vol. 31(3), pages 698-719, May.
  61. Daniele Bianchi & Kenichiro McAlinn, 2018. "Large-Scale Dynamic Predictive Regressions," Papers 1803.06738, arXiv.org.
  62. Crespo-Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava & Obersteiner, Michael, 2021. "Regime-dependent commodity price dynamics: A predictive analysis," IHS Working Paper Series 28, Institute for Advanced Studies.
  63. Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G., 2023. "Dynamic linear models with adaptive discounting," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1925-1944.
  64. Nonejad, Nima, 2019. "Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  65. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
  66. Hai Lin & Chunchi Wu & Guofu Zhou, 2018. "Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach," Management Science, INFORMS, vol. 64(9), pages 4218-4238, September.
  67. Miguel Belmonte & Gary Koop, 2014. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Advances in Econometrics, in: Bayesian Model Comparison, volume 34, pages 45-69, Emerald Group Publishing Limited.
  68. Pyun, Sungjune, 2019. "Variance risk in aggregate stock returns and time-varying return predictability," Journal of Financial Economics, Elsevier, vol. 132(1), pages 150-174.
  69. 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.
  70. Kartikay Gupta & Niladri Chatterjee, 2021. "Stocks Recommendation from Large Datasets Using Important Company and Economic Indicators," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 667-689, December.
  71. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
  72. Korobilis, D & Yilmaz, K, 2018. "Measuring Dynamic Connectedness with Large Bayesian VAR Models," Essex Finance Centre Working Papers 20937, University of Essex, Essex Business School.
  73. repec:ipg:wpaper:2013-020 is not listed on IDEAS
  74. 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.
  75. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
  76. Mönch, Emanuel & Stein, Tobias, 2021. "Equity premium predictability over the business cycle," Discussion Papers 25/2021, Deutsche Bundesbank.
  77. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
  78. James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  79. Gonçalo Faria & Fabio Verona, 2021. "Time-frequency forecast of the equity premium," Quantitative Finance, Taylor & Francis Journals, vol. 21(12), pages 2119-2135, December.
  80. repec:ipg:wpaper:20 is not listed on IDEAS
  81. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
  82. Marie-Hélène Gagnon & Gabriel Power & Dominique Toupin, 2018. "Forecasting International Index Returns using Option-implied Variables," Cahiers de recherche 1807, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
  83. repec:zbw:bofrdp:2020_006 is not listed on IDEAS
  84. Liu, Li & Ma, Feng & Wang, Yudong, 2015. "Forecasting excess stock returns with crude oil market data," Energy Economics, Elsevier, vol. 48(C), pages 316-324.
  85. Byrne, Joseph & Fu, Rong, 2016. "Stock Return Prediction with Fully Flexible Models and Coefficients," MPRA Paper 75366, University Library of Munich, Germany.
  86. 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.
  87. Dai, Zhifeng & Dong, Xiaodi & Kang, Jie & Hong, Lianying, 2020. "Forecasting stock market returns: New technical indicators and two-step economic constraint method," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
  88. Cai, Zongwu & Wang, Yunfei, 2014. "Testing predictive regression models with nonstationary regressors," Journal of Econometrics, Elsevier, vol. 178(P1), pages 4-14.
  89. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
  90. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
  91. Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.
  92. Joscha Beckmann & Gary Koop & Dimitris Korobilis & Rainer Alexander Schüssler, 2020. "Exchange rate predictability and dynamic Bayesian learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 410-421, June.
  93. Georgiev, Iliyan & Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2018. "Testing for parameter instability in predictive regression models," Journal of Econometrics, Elsevier, vol. 204(1), pages 101-118.
  94. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
  95. Nima Nonejad, 2021. "Using the conditional volatility channel to improve the accuracy of aggregate equity return predictions," Empirical Economics, Springer, vol. 61(2), pages 973-1009, August.
  96. 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.
  97. Kinateder, Harald & Papavassiliou, Vassilios G., 2019. "Sovereign bond return prediction with realized higher moments," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 53-73.
  98. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
  99. Ben R. Marshall & Nhut H. Nguyen & Nuttawat Visaltanachoti, 2017. "Time series momentum and moving average trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 405-421, March.
  100. Tom Engsted & Stig V. Møller & Magnus Sander, 2013. "Bond return predictability in expansions and recessions," CREATES Research Papers 2013-13, Department of Economics and Business Economics, Aarhus University.
  101. Borup, Daniel, 2019. "Asset pricing model uncertainty," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 166-189.
  102. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
  103. Liu, Li & Tan, Siming & Wang, Yudong, 2020. "Can commodity prices forecast exchange rates?," Energy Economics, Elsevier, vol. 87(C).
  104. He, Feng & Lucey, Brian & Wang, Ziwei, 2021. "Trade policy uncertainty and its impact on the stock market -evidence from China-US trade conflict," Finance Research Letters, Elsevier, vol. 40(C).
  105. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
  106. repec:zbw:bofrdp:2017_001 is not listed on IDEAS
  107. Alisa Yusupova & Nicos G. Pavlidis & Efthymios G. Pavlidis, 2019. "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting," Papers 1912.04661, arXiv.org.
  108. Daniele Bianchi & Massimo Guidolin & Manuela Pedio, 2020. "Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets," BAFFI CAREFIN Working Papers 20143, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  109. Nuno Silva, 2015. "Industry based equity premium forecasts," GEMF Working Papers 2015-19, GEMF, Faculty of Economics, University of Coimbra.
  110. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
  111. Dai, Zhifeng & Zhu, Huan & Kang, Jie, 2021. "New technical indicators and stock returns predictability," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 127-142.
  112. 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).
  113. Huang, Wenli & Liu, Wenqiong & Lu, Lei & Mu, Congming, 2023. "Hedge funds trading strategies and leverage," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
  114. 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.
  115. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
  116. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2013. "Time-varying combinations of predictive densities using nonlinear filtering," Journal of Econometrics, Elsevier, vol. 177(2), pages 213-232.
  117. Bianchi, Daniele & Tamoni, Andrea, 2016. "The dynamics of expected returns: evidence from multi-scale time series modelling," LSE Research Online Documents on Economics 118992, London School of Economics and Political Science, LSE Library.
  118. Alexander M. Chinco & Adam D. Clark-Joseph & Mao Ye, 2017. "Sparse Signals in the Cross-Section of Returns," NBER Working Papers 23933, National Bureau of Economic Research, Inc.
  119. Dangl, Thomas & Randl, Otto & Zechner, Josef, 2016. "Risk control in asset management: Motives and concepts," CFS Working Paper Series 546, Center for Financial Studies (CFS).
  120. 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.
  121. 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.
  122. Byrne, Joseph P. & Korobilis, Dimitris & Ribeiro, Pinho J., 2014. "On the Sources of Uncertainty in Exchange Rate Predictability," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon TN 2015-24, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  123. Niaz Bashiri Behmiri, Maryam Ahmadi, Juha-Pekka Junttila, and Matteo Manera, 2021. "Financial Stress and Basis in Energy Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
  124. Nuno Silva, 2015. "Time-Varying Stock Return Predictability: The Eurozone Case," Notas Económicas, Faculty of Economics, University of Coimbra, issue 41, pages 28-38, June.
  125. Lahr, Henry & Trombley, Timothy E., 2020. "Early indicators of fundraising success by venture capital firms," Journal of Corporate Finance, Elsevier, vol. 65(C).
  126. Camilla Muglia & Luca Santabarbara & Stefano Grassi, 2019. "Is Bitcoin a Relevant Predictor of Standard & Poor’s 500?," JRFM, MDPI, vol. 12(2), pages 1-10, May.
  127. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
  128. Florian Huber & Gary Koop & Michael Pfarrhofer, 2020. "Bayesian Inference in High-Dimensional Time-varying Parameter Models using Integrated Rotated Gaussian Approximations," Papers 2002.10274, arXiv.org.
  129. Nonejad, Nima, 2022. "Forecasting crude oil price volatility out-of-sample using news-based geopolitical risk index: What forms of nonlinearity help improve forecast accuracy the most?," Finance Research Letters, Elsevier, vol. 46(PA).
  130. 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.
  131. 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).
  132. Konstantin Styrin, 2018. "Forecasting inflation in Russia by Dynamic Model Averaging," Bank of Russia Working Paper Series wps39, Bank of Russia.
  133. Sarah Brown & Pulak Ghosh & Karl Taylor, 2016. "Household Finances and Social Interaction: Bayesian Analysis of Household Panel Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 467-488, September.
  134. Smith, Simon C., 2021. "International stock return predictability," International Review of Financial Analysis, Elsevier, vol. 78(C).
  135. Carlos Carvalho & Jared D. Fisher & Davide Pettenuzzo, 2018. "Optimal Asset Allocation with Multivariate Bayesian Dynamic Linear Models," Working Papers 123, Brandeis University, Department of Economics and International Business School.
  136. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
  137. G. Cubadda & S. Grassi & B. Guardabascio, 2022. "The Time-Varying Multivariate Autoregressive Index Model," Papers 2201.07069, arXiv.org.
  138. Golab, Anna & Bannigidadmath, Deepa & Pham, Thach Ngoc & Thuraisamy, Kannan, 2022. "Economic policy uncertainty and industry return predictability – Evidence from the UK," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 433-447.
  139. Li, Yan & Ng, David T. & Swaminathan, Bhaskaran, 2013. "Predicting market returns using aggregate implied cost of capital," Journal of Financial Economics, Elsevier, vol. 110(2), pages 419-436.
  140. Boucher, C. & Jasinski, A. & Tokpavi, S., 2023. "Conditional mean reversion of financial ratios and the predictability of returns," Journal of International Money and Finance, Elsevier, vol. 137(C).
  141. Shamsi Zamenjani, Azam, 2021. "Do financial variables help predict the conditional distribution of the market portfolio?," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 327-345.
  142. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.
  143. Baltas, Nick & Karyampas, Dimitrios, 2018. "Forecasting the equity risk premium: The importance of regime-dependent evaluation," Journal of Financial Markets, Elsevier, vol. 38(C), pages 83-102.
  144. Ribeiro, Pinho J., 2017. "Selecting exchange rate fundamentals by bootstrap," International Journal of Forecasting, Elsevier, vol. 33(4), pages 894-914.
  145. repec:zbw:bofrdp:2016_029 is not listed on IDEAS
  146. Demirer, Riza & Pierdzioch, Christian & Zhang, Huacheng, 2017. "On the short-term predictability of stock returns: A quantile boosting approach," Finance Research Letters, Elsevier, vol. 22(C), pages 35-41.
  147. 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.
  148. Smith, Simon C., 2022. "Time-variation, multiple testing, and the factor zoo," International Review of Financial Analysis, Elsevier, vol. 84(C).
  149. de Oliveira Souza, Thiago, 2019. "Predictability concentrates in bad times. And so does disagreement," Discussion Papers on Economics 8/2019, University of Southern Denmark, Department of Economics.
  150. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
  151. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
  152. Valeriy Zakamulin & Javier Giner, 2020. "Trend following with momentum versus moving averages: a tale of differences," Quantitative Finance, Taylor & Francis Journals, vol. 20(6), pages 985-1007, June.
  153. Nonejad, Nima, 2018. "Déjà vol oil? Predicting S&P 500 equity premium using crude oil price volatility: Evidence from old and recent time-series data," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 260-270.
  154. Giulia Dal Pra & Massimo Guidolin & Manuela Pedio & Fabiola Vasile, 2016. "Do Regimes in Excess Stock Return Predictability Create Economic Value? An Out-of-Sample Portfolio Analysis," BAFFI CAREFIN Working Papers 1637, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
  155. Bart Diris & Franz Palm & Peter Schotman, 2015. "Long-Term Strategic Asset Allocation: An Out-of-Sample Evaluation," Management Science, INFORMS, vol. 61(9), pages 2185-2202, September.
  156. Roy P. P. M. Hoevenaars & Roderick D. J. Molenaar & Peter C. Schotman & Tom B. M. Steenkamp, 2014. "Strategic Asset Allocation For Long‐Term Investors: Parameter Uncertainty And Prior Information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 353-376, April.
  157. Faria, Gonçalo & Verona, Fabio, 2018. "Forecasting stock market returns by summing the frequency-decomposed parts," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 228-242.
  158. 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.
  159. Yudong Wang & Zhiyuan Pan & Chongfeng Wu, 2017. "Time‐Varying Parameter Realized Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 566-580, August.
  160. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
  161. Anwen Yin, 2019. "Equity Premium Prediction with Structural Breaks: A Two-Stage Forecast Combination Approach," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 11(12), pages 1-50, December.
  162. Bruno P. C. Levy & Hedibert F. Lopes, 2021. "Dynamic Ordering Learning in Multivariate Forecasting," Papers 2101.04164, arXiv.org, revised Nov 2021.
  163. Dorra Zouari & Achraf Ghorbel & Sonia Ghorbel-Zouari & Younes Boujelbène, 2014. "Volatility spillovers and dynamic correlation between liquidity risk factors in Tunisian banks," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 6(1), pages 1-26.
  164. 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).
  165. 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.
  166. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
  167. Matteo Iacopini & Luca Rossini, 2019. "Bayesian nonparametric graphical models for time-varying parameters VAR," Papers 1906.02140, arXiv.org.
  168. Zhao, Albert Bo & Cheng, Tingting, 2022. "Stock return prediction: Stacking a variety of models," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 288-317.
  169. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
  170. Kartikay Gupta & Niladri Chatterjee, 2019. "Top performing stocks recommendation strategy for portfolio," Papers 1901.11013, arXiv.org, revised Aug 2019.
  171. Luo, Shikong & Yan, Xinyan & Yang, Haoyi, 2021. "Let’s take a smooth break: Stock return predictability revisited," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 300-314.
  172. Sharma, Susan Sunila & Narayan, Paresh Kumar, 2022. "Technology shocks and stock returns: A long-term perspective," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 67-83.
  173. Kim, Byungoh & Suh, Sangwon, 2018. "Sentiment-based momentum strategy," International Review of Financial Analysis, Elsevier, vol. 58(C), pages 52-68.
  174. Tsiakas, Ilias & Li, Jiahan & Zhang, Haibin, 2020. "Equity premium prediction and the state of the economy," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 75-95.
  175. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
  176. 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.
  177. Pohl, Walter & Schmedders, Karl & Wilms, Ole, 2021. "Asset pricing with heterogeneous agents and long-run risk," Journal of Financial Economics, Elsevier, vol. 140(3), pages 941-964.
  178. Jiang, Danling, 2013. "The second moment matters! Cross-sectional dispersion of firm valuations and expected returns," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3974-3992.
  179. 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.
  180. Mykola Babiak & Jozef Barunik, 2020. "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers 2009.03394, arXiv.org, revised Jul 2021.
  181. Kenwin Maung, 2021. "Estimating high-dimensional Markov-switching VARs," Papers 2107.12552, arXiv.org.
  182. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
  183. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
  184. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
  185. Liu, Li & Bu, Ruijun & Pan, Zhiyuan & Xu, Yuhua, 2019. "Are financial returns really predictable out-of-sample?: Evidence from a new bootstrap test," Economic Modelling, Elsevier, vol. 81(C), pages 124-135.
  186. Jurdi, Doureige J., 2022. "Predicting the Australian equity risk premium," Pacific-Basin Finance Journal, Elsevier, vol. 71(C).
  187. Yin, Anwen, 2019. "Out-of-sample equity premium prediction in the presence of structural breaks," International Review of Financial Analysis, Elsevier, vol. 65(C).
  188. Lopes, Hedibert F. & McCulloch, Robert E. & Tsay, Ruey S., 2022. "Parsimony inducing priors for large scale state–space models," Journal of Econometrics, Elsevier, vol. 230(1), pages 39-61.
  189. Møller, Stig V. & Sander, Magnus, 2017. "Dividends, earnings, and predictability," Journal of Banking & Finance, Elsevier, vol. 78(C), pages 153-163.
  190. 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.
  191. E Bárcena-Martín & M Rodríguez-Fernández & S Borrego-Domínguez, 2017. "Golf, supply and demand," Tourism Economics, , vol. 23(6), pages 1220-1234, September.
  192. Nuno Silva, 2015. "Industry based equity premium forecasts," GEMF Working Papers 2015-19, GEMF, Faculty of Economics, University of Coimbra.
  193. Dai, Zhifeng & Zhu, Huan, 2020. "Stock return predictability from a mixed model perspective," Pacific-Basin Finance Journal, Elsevier, vol. 60(C).
  194. Ko, Byoung-Wook, 2018. "Dynamic patterns of dry bulk freight spot rates through the lens of a time-varying coefficient model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 319-330.
  195. Stefano Cassella & Huseyin Gulen, 2018. "Extrapolation Bias and the Predictability of Stock Returns by Price-Scaled Variables," Review of Financial Studies, Society for Financial Studies, vol. 31(11), pages 4345-4397.
  196. Feng, Jiabao & Wang, Yudong & Yin, Libo, 2017. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries," Energy Economics, Elsevier, vol. 68(C), pages 240-254.
  197. repec:zbw:bofrdp:2018_007 is not listed on IDEAS
  198. 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.
  199. Mauro Bernardi & Daniele Bianchi & Nicolas Bianco, 2022. "Variational inference for large Bayesian vector autoregressions," Papers 2202.12644, arXiv.org, revised Jun 2023.
  200. Álvarez-Díaz, Marcos & Hammoudeh, Shawkat & Gupta, Rangan, 2014. "Detecting predictable non-linear dynamics in Dow Jones Islamic Market and Dow Jones Industrial Average indices using nonparametric regressions," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 22-35.
  201. Jacopo Piana & Daniele Bianchi, 2017. "Expected Spot Prices and the Dynamics of Commodity Risk Premia," 2017 Meeting Papers 1149, Society for Economic Dynamics.
  202. Schneider, Paul, 2019. "An anatomy of the market return," Journal of Financial Economics, Elsevier, vol. 132(2), pages 325-350.
  203. Liu, Li & Pan, Zhiyuan, 2020. "Forecasting stock market volatility: The role of technical variables," Economic Modelling, Elsevier, vol. 84(C), pages 55-65.
  204. Navratil, Robert & Taylor, Stephen & Vecer, Jan, 2021. "On equity market inefficiency during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 77(C).
  205. Leopoldo Catania & Stefano Grassi & Francesco Ravazzolo, 2018. "Forecasting Cryptocurrencies Financial Time Series," Working Papers No 5/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  206. 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.
  207. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
  208. Jin-Gil Jeong & Sandip Mukherji, 2018. "Flexible Optimal Models For Predicting Stock Market Returns," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 12(2), pages 39-48.
  209. Athambawa Jahfer & Abdul Hameed Mulafara, 2016. "Dividend policy and share price volatility: evidence from Colombo stock market," International Journal of Managerial and Financial Accounting, Inderscience Enterprises Ltd, vol. 8(2), pages 97-108.
  210. Marcos Álvarez-Díaz & Shawkat Hammoudeh & Rangan Gupta, 2013. "Detecting Predictable Non-linear Dynamics in Dow Jones Industrial Average and Dow Jones Islamic Market Indices using Nonparametric Regressions," Working Papers 201385, University of Pretoria, Department of Economics.
  211. Cenesizoglu, Tolga, 2022. "Return decomposition over the business cycle," Journal of Banking & Finance, Elsevier, vol. 143(C).
  212. Zhifeng Dai & Huiting Zhou, 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
  213. 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).
  214. Yu, Fanchao, 2023. "Macroeconomic information, global economic policy uncertainty and gold futures return predictability," Finance Research Letters, Elsevier, vol. 55(PA).
  215. Procasky, William J. & Yin, Anwen, 2023. "Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
  216. Kroencke, Tim A., 2022. "Recessions and the stock market," Journal of Monetary Economics, Elsevier, vol. 131(C), pages 61-77.
  217. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.
  218. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
  219. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
  220. Haibin Xie & Shouyang Wang, 2018. "Timing the market: the economic value of price extremes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-24, December.
  221. Dai, Zhifeng & Kang, Jie & Wen, Fenghua, 2021. "Predicting stock returns: A risk measurement perspective," International Review of Financial Analysis, Elsevier, vol. 74(C).
  222. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
  223. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Equity Premia using Bayesian Dynamic Model Averaging," CQE Working Papers 2914, Center for Quantitative Economics (CQE), University of Muenster.
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