Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem

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• 作者：Fei Cong ; Cornelis W. Oosterlee
• 关键词：Dynamic portfolio management ; Simulation method ; Least ; square regression ; Taylor expansion ; Fourier cosine expansion method
• 刊名：Computational Economics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：49
• 期：3
• 页码：433-458
• 全文大小：714KB
• 刊物类别：Business and Economics
• 刊物主题：Economic Theory/Quantitative Economics/Mathematical Methods; Computer Appl. in Social and Behavioral Sciences; Operation Research/Decision Theory; Behavioral/Experimental Economics; Math Applications
• 出版者：Springer US
• ISSN：1572-9974
• 卷排序：49

文摘

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).