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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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  1. 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.
  2. Marquering, W.A. & Verbeek, M.J.C.M., 2001. "The Economic Value of Predicting Stock Index Returns and Volatility," ERIM Report Series Research in Management ERS-2001-75-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  3. Cochrane, John H. & Campbell, John, 1999. "By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior," Scholarly Articles 3119444, Harvard University Department of Economics.
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  6. Engsted, Tom & Pedersen, Thomas Q., 2012. "Return predictability and intertemporal asset allocation: Evidence from a bias-adjusted VAR model," Journal of Empirical Finance, Elsevier, vol. 19(2), pages 241-253.
  7. Campbell, John Y & Chan, Yeung Lewis & Viceira, Luis M, 2001. "A Multivariate Model of Strategic Asset Allocation," CEPR Discussion Papers 3070, C.E.P.R. Discussion Papers.
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  9. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive Density Evaluation," Handbook of Economic Forecasting, Elsevier.
  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. Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
  12. Kirby, Chris, 1997. "Measuring the Predictable Variation in Stock and Bond Returns," Review of Financial Studies, Society for Financial Studies, vol. 10(3), pages 579-630.
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  16. 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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  27. repec:cup:cbooks:9780521845731 is not listed on IDEAS
  28. 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.
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