Computational Economics

, Volume 29, Issue 3–4, pp 355–367 | Cite as

Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function?

Original Paper

Abstract

Most dynamic programming methods deployed in the portfolio choice literature involve recursions on an approximated value function. The simulation-based method proposed recently by Brandt, Goyal, Santa-Clara, and Stroud (Review of Financial Studies, 18, 831–873, 2005), relies instead on recursive uses of approximated optimal portfolio weights. We examine the relative numerical performance of these two approaches. We show that when portfolio weights are constrained by short sale restrictions for example, iterating on optimized portfolio weights leads to superior results. Value function iterations result in a lower variance but disproportionately higher bias of the solution, especially when risk aversion is high and the investment horizon is long.

Keywords

Dynamic portfolio choice Simulation method 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Balduzzi P., Lynch A.W. (1999). Transaction costs and predictability: some utility cost calculations. Journal of Financial Economics 52:47–78CrossRefGoogle Scholar
  2. Barberis N. (2000) Investing for the long run when returns are predictable. Journal of Finance 55:225–264CrossRefGoogle Scholar
  3. van Binsbergen, J.H., Brandt, M.W. (2006) Optimal asset allocation in asset liability management. Working Paper, Duke University.Google Scholar
  4. Brandt, M. W. (2005). Portfolio choice problems. In Y. Ait-Sahalia, & L. P. Hansen, (Eds.), Handbook of Financial Econometrics, forthcoming.Google Scholar
  5. Brandt M.W., Goyal A., Santa-Clara P., Stroud J.R. (2005). A simulation approach to dynamic portfolio choice with an application to learning about return predictability. Review of Financial Studies 18:831–873CrossRefGoogle Scholar
  6. Cochrane J.H. (1989). The sensitivity of tests of the intertemporal allocation of consumption to near-rational alternatives. American Economic Review 79:319–337Google Scholar
  7. Dammon R.M., Spatt C.S., Zhang H.H. (2001). Optimal consumption and investment with capital gains taxes. Review of Financial Studies 14:583–616CrossRefGoogle Scholar
  8. Newey W.K., West K.D. (1987). A simple, positive definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55:703–708CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Jules H. van Binsbergen
    • 1
  • Michael W. Brandt
    • 1
    • 2
  1. 1.School of BusinessDuke UniversityDurhamUSA
  2. 2.NBERCambridgeUSA

Personalised recommendations