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A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability

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Author Info
Michael W. Brandt
Amit Goyal
Pedro Santa-Clara
Jonathan R. Stroud
Abstract

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values. Copyright 2005, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/rfs/hhi019
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Publisher Info
Article provided by Oxford University Press for Society for Financial Studies in its journal The Review of Financial Studies.

Volume (Year): 18 (2005)
Issue (Month): 3 ()
Pages: 831-873
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:oup:rfinst:v:18:y:2005:i:3:p:831-873

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  1. Luca Benzoni & Pierre Collin-Dufresne & Robert S. Goldstein, 2007. "Portfolio choice over the life-cycle when the stock and labor markets are cointegrated," Working Paper Series WP-07-11, Federal Reserve Bank of Chicago. [Downloadable!]
  2. Koijen, Ralph S.J. & Nijman, Theo E. & Werker, Bas J.M., 2006. "Optimal portfolio choice with annuitization," Discussion Paper 78, Tilburg University, Center for Economic Research. [Downloadable!]
  3. Lorenzo Garlappi & Georgios Skoulakis, 2009. "Numerical Solutions to Dynamic Portfolio Problems: The Case for Value Function Iteration using Taylor Approximation," Computational Economics, Springer, vol. 33(2), pages 193-207, March. [Downloadable!] (restricted)
  4. Jules Binsbergen & Michael Brandt, 2007. "Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function?," Computational Economics, Springer, vol. 29(3), pages 355-367, May. [Downloadable!] (restricted)
  5. Thomas Breuer & Martin Jandačka, 2008. "Portfolio selection with transaction costs under expected shortfall constraints," Computational Management Science, Springer, vol. 5(4), pages 305-316, October. [Downloadable!] (restricted)
  6. Ferstl, Robert & Weissensteiner, Alex, 2009. "Asset-Liability Management under time-varying Investment Opportunities," MPRA Paper 15068, University Library of Munich, Germany, revised 25 May 2009. [Downloadable!]
  7. Simon Lysbjerg Hansen, 2005. "A Malliavin-based Monte-Carlo Approach for Numerical Solution of Stochastic Control Problems: Experiences from Merton's Problem," Computing in Economics and Finance 2005 391, Society for Computational Economics. [Downloadable!]
  8. Markus Haas, 2007. "Do investors dislike kurtosis?," Economics Bulletin, Economics Bulletin, vol. 7(2), pages 1-9. [Downloadable!]
  9. Alois Geyer & Michael Hanke & Alex Weissensteiner, 2009. "A stochastic programming approach for multi-period portfolio optimization," Computational Management Science, Springer, vol. 6(2), pages 187-208, May. [Downloadable!] (restricted)
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