Solving dynamic portfolio choice problems by recursing on optimized portfolio weights or on the value function?
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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Volume (Year): 29 (2007)
Issue (Month): 3 (May)
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- 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.
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