Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

Journal article (2016)

Authors

F. Cong

C.W. Oosterlee

Department

Delft Institute of Applied Mathematics () (TU Delft)

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

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Published Date

03-03-2016

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Source:

Computational Economics, 2016

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Delft Institute of Applied Mathematics

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).

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