Portfolio optimization with nonparametric value at risk : A block coordinate descent method

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

1 Scopus Citations
View graph of relations

Author(s)

  • Xueting Cui
  • Xiaoling Sun
  • Shushang Zhu
  • Rujun Jiang
  • Duan Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)454-471
Journal / PublicationINFORMS Journal on Computing
Volume30
Issue number3
Online published11 Sep 2018
Publication statusPublished - 2018

Abstract

In this paper, we investigate a portfolio optimization methodology using nonparametric value at risk (VaR). In particular, we adopt kernel VaR and quadratic VaR as risk measures. As the resulting models are nonconvex and nonsmooth optimization problems, albeit with some special structures, we propose some specially devised block coordinate descent (BCD) methods for finding approximate or local optimal solutions. Computational results show that the BCD methods are efficient for finding local solutions with good quality and they compare favorably with the branch-and-bound method-based global optimal solution procedures. From the simulation test and empirical analysis that we carry out, we are able to conclude that the mean-VaR models using kernel VaR and quadratic VaR are more robust compared to those using historical VaR or parametric VaR under the normal distribution assumption, especially when the information of the return distribution is limited.

Research Area(s)

  • Bcd Method, Kernel, Nonparametric Var, Portfolio Selection

Citation Format(s)

Portfolio optimization with nonparametric value at risk : A block coordinate descent method. / Cui, Xueting; Sun, Xiaoling; Zhu, Shushang; Jiang, Rujun; Li, Duan.

In: INFORMS Journal on Computing, Vol. 30, No. 3, 2018, p. 454-471.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal