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Multistage Optioned Portfolio Selection: Mean-Variance Model and Target Tracking Model

In: Optimization and Control for Systems in the Big-Data Era

Author

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  • Jianfeng Liang

    (Lingnan (University) College, Sun Yat-sen University)

Abstract

Options form an indispensable part of the modern financial markets. One reason for this phenomenon is the versatile payoff structures of options, which can serve to form investment portfolios with desirable risk profiles. This chapter introduces mean-variance models and develops target tracking model for optioned portfolio selection problem in both static and dynamic formulations. We focus on the rich properties of the payoff functions and the solution methodologies. Two different solution techniques for multistage mean-variance model are discussed: one is based on stochastic programming and optimality conditions, and the other one is based on stochastic control and dynamic programming. In addition, tracking-error-variance optimization models are proposed and solved by dynamic programming. It turns out that the optimal tracking portfolio holds mean-variance efficiency. Close form relationships between the mean-variance model and the tracking model are proved, which bring new insights to dynamically solve the classical multistage mean-variance model. Throughout the chapter, numerical examples with real life data are used to illustrate and validate the results.

Suggested Citation

  • Jianfeng Liang, 2017. "Multistage Optioned Portfolio Selection: Mean-Variance Model and Target Tracking Model," International Series in Operations Research & Management Science, in: Tsan-Ming Choi & Jianjun Gao & James H. Lambert & Chi-Kong Ng & Jun Wang (ed.), Optimization and Control for Systems in the Big-Data Era, chapter 0, pages 185-216, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-53518-0_11
    DOI: 10.1007/978-3-319-53518-0_11
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