Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem
Fei Cong (f.cong@tudelft.nl) and
Cornelis Oosterlee
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Fei Cong: Delft Institute of Applied Mathematics, TU Delft
Computational Economics, 2017, vol. 49, issue 3, No 5, 433-458
Abstract:
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).
Keywords: Dynamic portfolio management; Simulation method; Least-square regression; Taylor expansion; Fourier cosine expansion method (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10614-016-9569-0
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