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Predictable return distributions

  • Thomas Q. Pedersen

    ()

    (School of Economics and Management, Aarhus University and CREATES)

This paper provides detailed insights into predictability of the entire stock and bond return distribution through the use of quantile regression. This allows us to examine speci?c parts of the return distribution such as the tails or the center, and for a suf?ciently ?ne grid of quantiles we can trace out the entire distribution. A univariate quantile regression model is used to examine stock and bond return distributions individually, while a multivariate model is used to capture their joint distribution. An empirical analysis on US data shows that certain parts of the return distributions are predictable as a function of economic state variables. The results are, however, very different for stocks and bonds. The state variables primarily predict only location shifts in the stock return distribution, while they also predict changes in higher-order moments in the bond return distribution. Out-of-sample analyses show that the relative accuracy of the state variables in predicting future returns varies across the distribution. A portfolio study shows that an investor with power utility can obtain economic gains by applying the empirical return distribution in portfolio decisions instead of imposing an assumption of lognormally distributed returns.

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File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_38.pdf
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-38.

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Length: 47
Date of creation: 01 Jul 2010
Date of revision:
Handle: RePEc:aah:create:2010-38
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  3. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2009. "Non-linear predictability in stock and bond returns: when and where is it exploitable?," Working Papers 2008-010, Federal Reserve Bank of St. Louis.
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  9. Koenker, Roger & Bassett, Gilbert, Jr, 1982. "Robust Tests for Heteroscedasticity Based on Regression Quantiles," Econometrica, Econometric Society, vol. 50(1), pages 43-61, January.
  10. Gilbert W. Bassett Jr. & Hsiu-Lang Chen, 2001. "Portfolio style: Return-based attribution using quantile regression," Empirical Economics, Springer, vol. 26(1), pages 293-305.
  11. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, 09.
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  13. Tom Engsted & Thomas Q. Pedersen, 2008. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," CREATES Research Papers 2008-27, School of Economics and Management, University of Aarhus.
  14. Christiansen, Charlotte & Ranaldo, Angelo, 2005. "Realized Bond-Stock Correlation: Macroeconomic Announcement Effects," Finance Research Group Working Papers F-2005-05, University of Aarhus, Aarhus School of Business, Department of Business Studies.
  15. Massimo Guidolin & Allan Timmerman, 2005. "An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns," Working Papers 2005-003, Federal Reserve Bank of St. Louis.
  16. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  17. Guidolin, Massimo & Timmermann, Allan, 2007. "Asset allocation under multivariate regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3503-3544, November.
  18. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
  19. John Y. Campbell & John H. Cochrane, 1994. "By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," CRSP working papers 412, Center for Research in Security Prices, Graduate School of Business, University of Chicago.
  20. Valentina Corradi & Norman Swanson, 2006. "Predictive Density Evaluation. Revised," Departmental Working Papers 200621, Rutgers University, Department of Economics.
  21. Lieven Baele & Geert Bekaert & Koen Inghelbrecht, 2009. "The Determinants of Stock and Bond Return Comovements," NBER Working Papers 15260, National Bureau of Economic Research, Inc.
  22. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
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  24. Koenker, Roger & Park, Beum J., 1996. "An interior point algorithm for nonlinear quantile regression," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 265-283.
  25. De Gooijer J.G. & Zerom D., 2003. "On Additive Conditional Quantiles With High Dimensional Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 135-146, January.
  26. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, 06.
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