Penalty Method for Portfolio Selection with Capital Gains Tax

46 Pages Posted: 24 Aug 2019 Last revised: 5 Sep 2019

See all articles by Baojun Bian

Baojun Bian

Tongji University

Xinfu Chen

Independent

Min Dai

The Hong Kong Polytechnic University

Shuaijie Qian

National University of Singapore (NUS)

Date Written: August 23, 2019

Abstract

Many finance problems can be formulated as a singular stochastic control problem, where the associated Hamilton-Jacobi-Bellman (HJB) equation takes the form of variational inequality and its penalty approximation equation is linked to a regular control problem. The penalty method, as a finite difference scheme for the penalty equation, has been widely used to numerically solve singular control problems, and its convergence analysis in literature relies on the uniqueness of solution to the original HJB equation problem. We consider a singular stochastic control problem arising from continuous-time portfolio selection with capital gains tax, where the associated HJB equation problem admits infinitely many solutions. We show that the penalty method still works and converges to the value function which is the minimal (viscosity) solution of the HJB equation problem. Numerical results are presented to demonstrate the efficiency of the penalty method and to better understand optimal investment strategy in the presence of capital gains tax. Our approach sheds light on the robustness of the penalty method for general singular stochastic control problems.

Suggested Citation

Bian, Baojun and Chen, Xinfu and Dai, Min and Qian, Shuaijie, Penalty Method for Portfolio Selection with Capital Gains Tax (August 23, 2019). Available at SSRN: https://ssrn.com/abstract=3441553 or http://dx.doi.org/10.2139/ssrn.3441553

Baojun Bian

Tongji University ( email )

Shanghai
China

Xinfu Chen

Independent ( email )

Min Dai (Contact Author)

The Hong Kong Polytechnic University ( email )

Shuaijie Qian

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

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