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).

Additional Metadata
Keywords Dynamic portfolio management · Simulation method · Least-square, regression · Taylor expansion · Fourier cosine expansion method
Persistent URL dx.doi.org//10.1007/s10614-016-9569-0
Journal Computational Economics
Citation
Cong, F, & Oosterlee, C.W. (2017). Accurate and Robust Numerical Methods for the Dynamic Portfolio Management Problem. Computational Economics , 49, 433–458. doi:/10.1007/s10614-016-9569-0