Optioned portfolio selection: Models and analysis

Jianfeng Liang, Shuzhong Zhang, Duan Li

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The mean-variance model of Markowitz and many of its extensions have been playing an instrumental role in guiding the practice of portfolio selection. In this paper we study a mean-variance formulation for the portfolio selection problem involving options. In particular, the portfolio in question contains a stock index and some European style options on the index. A refined mean-variance methodology is adopted in our approach to formulate this problem as multistage stochastic optimization. It turns out that there are two different solution techniques, both lead to explicit solutions of the problem: one is based on stochastic programming and optimality conditions, and the other one is based on stochastic control and dynamic programming. We introduce both techniques, because their strengths are very different so as to suit different possible extensions and refinements of the basic model. Attention is paid to the structure of the optimal payoff function, which is shown to possess rich properties. Further refinements of the model, such as the request that the payoff should be monotonic with respect to the index, are discussed. Throughout the paper, various numerical examples are used to illustrate the underlying concepts.

Original languageEnglish (US)
Pages (from-to)569-593
Number of pages25
JournalMathematical Finance
Volume18
Issue number4
DOIs
StatePublished - Oct 2008

Bibliographical note

Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.

Keywords

  • Dynamic programming
  • Multistage mean-variance model
  • Optioned portfolio selection
  • Scenario tree
  • Stochastic control
  • Stochastic programming

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