Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

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Abstract

This paper enhances a well-known dynamic portfolio management algorithm, the BGSS algorithm, proposed by Brandt et al. (Review of Financial Studies, 18(3):831–873, 2005). We equip this algorithm with the components from a recently developed method, the Stochastic Grid Bundling Method (SGBM), for calculating conditional expectations. When solving the first-order conditions for a portfolio optimum, we implement a Taylor series expansion based on a nonlinear decomposition to approximate the utility functions. In the numerical tests, we show that our algorithm is accurate and robust in approximating the optimal investment strategies, which are generated by a new benchmark approach based on the COS method (Fang and Oosterlee, in SIAM Journal of Scientific Computing, 31(2):826–848, 2008).

Original languageEnglish
Pages (from-to)433-458
Number of pages26
JournalComputational Economics
Volume49
Issue number3
DOIs
Publication statusPublished - 3 Mar 2016

Keywords

  • Dynamic portfolio management
  • Fourier cosine expansion method
  • Least-square regression
  • Simulation method
  • Taylor expansion

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