Portfolio Optimization with Nonparametric Value at Risk: A Block Coordinate Descent Method
| Autor: | Xiaoling Sun, Duan Li, Xueting Cui, Rujun Jiang, Shushang Zhu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
050208 finance Optimization problem Computer science 05 social sciences General Engineering Nonparametric statistics 01 natural sciences Normal distribution 010104 statistics & probability Kernel (statistics) 0502 economics and business 0101 mathematics Portfolio optimization Coordinate descent Value at risk Parametric statistics |
| Zdroj: | INFORMS Journal on Computing. 30:454-471 |
| ISSN: | 1526-5528 1091-9856 |
| Popis: | 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. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0793 . |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|