A smoothing method for solving portfolio optimization with CVaR and applications in allocation of generation asset

Xiaojiao Tong, Liqun Qi, Felix Wu, Hui Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

This paper focuses on the computation issue of portfolio optimization with scenario-based CVaR. According to the semismoothness of the studied models, a smoothing technology is considered, and a smoothing SQP algorithm then is presented. The global convergence of the algorithm is established. Numerical examples arising from the allocation of generation assets in power markets are done. The computation efficiency between the proposed method and the linear programming (LP) method is compared. Numerical results show that the performance of the new approach is very good. The remarkable characteristic of the new method is threefold. First, the dimension of smoothing models for portfolio optimization with scenario-based CVaR is low and is independent of the number of samples. Second, the smoothing models retain the convexity of original portfolio optimization problems. Third, the complicated smoothing model that maximizes the profit under the CVaR constraint can be reduced to an ordinary optimization model equivalently. All of these show the advantage of the new method to improve the computation efficiency for solving portfolio optimization problems with CVaR measure.
Original languageEnglish
Pages (from-to)1723-1740
Number of pages18
JournalApplied Mathematics and Computation
Volume216
Issue number6
DOIs
Publication statusPublished - 15 May 2010

Keywords

  • Allocation of generation asset
  • Conditional value-at-risk (CVaR)
  • Portfolio optimization
  • Smoothing method

ASJC Scopus subject areas

  • Computational Mathematics
  • Applied Mathematics

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