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Changing Risk-Return Profiles

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We show that realized volatility in market returns and financial sector stock returns have strong predictive content for the future distribution of market returns. This is a robust feature of the last century of U.S. data and, most importantly, can be exploited in real time. Current realized volatility has the most information content on the uncertainty of future returns, whereas it has only limited content about the location of the future return distribution. When volatility is low, the predicted distribution of returns is less dispersed and probabilistic forecasts are sharper.

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  • Richard K. Crump & Miro Everaert & Domenico Giannone & Sean Hundtofte, 2018. "Changing Risk-Return Profiles," Staff Reports 850, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:850
    Note: Revised August 2023.
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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. 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.
    3. Gara M. dup Afonso & João A. C. Santos, 2015. "What Do Rating Agencies Think about “Too-Big-to-Fail” since Dodd-Frank," Liberty Street Economics 20150629, Federal Reserve Bank of New York.
    4. Giannone, Domenico & De Mol, Christine & Daubechies, Ingrid & Brodie, Joshua, 2007. "Sparse and Stable Markowitz Portfolios," CEPR Discussion Papers 6474, C.E.P.R. Discussion Papers.
    5. Chousakos, K. & Gorton, G., 2017. "Bank health post-crisis," Financial Stability Review, Banque de France, issue 21, pages 55-67, April.
    6. Rossi, Barbara & Sekhposyan, Tatevik, 2019. "Alternative tests for correct specification of conditional predictive densities," Journal of Econometrics, Elsevier, vol. 208(2), pages 638-657.
    7. Kyriakos Chousakos & Gary Gorton & Guillermo Ordonez, 2018. "Aggregate Information Dynamics," 2018 Meeting Papers 167, Society for Economic Dynamics.
    8. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    9. Matthew Baron & Wei Xiong, 2017. "Credit Expansion and Neglected Crash Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 713-764.
    10. Anne Opschoor & Dick van Dijk & Michel van der Wel, 2017. "Combining density forecasts using focused scoring rules," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1298-1313, November.
    11. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    12. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    13. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    14. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    15. Garland Durham & John Geweke, 2014. "Improving Asset Price Prediction When All Models are False," Journal of Financial Econometrics, Oxford University Press, vol. 12(2), pages 278-306.
    16. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    17. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    18. Daniele Massacci, 2015. "Predicting the Distribution of Stock Returns: Model Formulation, Statistical Evaluation, VaR Analysis and Economic Significance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(3), pages 191-208, April.
    19. Giglio, Stefano & Kelly, Bryan & Pruitt, Seth, 2016. "Systemic risk and the macroeconomy: An empirical evaluation," Journal of Financial Economics, Elsevier, vol. 119(3), pages 457-471.
    20. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    21. 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.
    22. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    23. Kyriakos T. Chousakos & Gary B. Gorton, 2017. "Bank Health Post-Crisis," NBER Working Papers 23167, National Bureau of Economic Research, Inc.
    24. Alan Moreira & Tyler Muir, 2017. "Volatility-Managed Portfolios," Journal of Finance, American Finance Association, vol. 72(4), pages 1611-1644, August.
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    Cited by:

    1. Richard K. Crump & João A. C. Santos, 2018. "Review of New York Fed studies on the effects of post-crisis banking reforms," Economic Policy Review, Federal Reserve Bank of New York, issue 24-2, pages 71-90.
    2. Busetto, Filippo, 2024. "Asymmetric expectations of monetary policy," Bank of England working papers 1058, Bank of England.
    3. Nina Boyarchenko & Domenico Giannone & Or Shachar, 2018. "Flighty liquidity," Staff Reports 870, Federal Reserve Bank of New York.
    4. Martina Hengge, 2019. "Uncertainty as a Predictor of Economic Activity," IHEID Working Papers 19-2019, Economics Section, The Graduate Institute of International Studies.

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    More about this item

    Keywords

    stock returns; realized volatility; density forecasts; optimal pools;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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