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

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Author Info

  • Jules Binsbergen

    ()

  • Michael Brandt

    ()

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. Copyright Springer Science+Business Media, LLC 2007

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 29 (2007)
Issue (Month): 3 (May)
Pages: 355-367

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Handle: RePEc:kap:compec:v:29:y:2007:i:3:p:355-367

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: Dynamic portfolio choice; Simulation method;

References

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  1. Michael W. Brandt & Amit Goyal & Pedro Santa-Clara & Jonathan R. Stroud, 2005. "A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning About Return Predictability," Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 831-873.
  2. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, American Finance Association, vol. 55(1), pages 225-264, 02.
  3. John H. Cochrane, 1988. "The Sensitivity of Tests of the Intertemporal Allocation of Consumption to Near-Rational Alternatives," NBER Working Papers 2730, National Bureau of Economic Research, Inc.
  4. Balduzzi, Pierluigi & Lynch, Anthony W., 1999. "Transaction costs and predictability: some utility cost calculations," Journal of Financial Economics, Elsevier, Elsevier, vol. 52(1), pages 47-78, April.
  5. Dammon, Robert M & Spatt, Chester S & Zhang, Harold H, 2001. "Optimal Consumption and Investment with Capital Gains Taxes," Review of Financial Studies, Society for Financial Studies, vol. 14(3), pages 583-616.
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Cited by:
  1. Taras Bodnar & Nestor Parolya & Wolfgang Schmid, 2012. "A Closed-Form Solution of the Multi-Period Portfolio Choice Problem for a Quadratic Utility Function," Papers 1207.1003, arXiv.org.

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