Incorporating asymmetric distributional information in robust value-at-risk optimization

• 成果类型: Article
• 署名作者: Natarajan, Karthik; Pachamanova, Dessislava; Sim, Melvyn
• 署名单位: National University of Singapore; Babson College; Nanyang Technological University; National University of Singapore; Singapore-MIT Alliance for Research & Technology Centre (SMART); Massachusetts Institute of Technology (MIT); National University of Singapore
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.1070.0769
• 发表日期: 2008
• 页码: 573-585
• 关键词: value-at-risk; robust optimization; Coherent risk measures

摘要

Value-at-Risk (VaR) is one of the most widely accepted risk measures in the financial and insurance industries, yet efficient optimization of VaR remains a very difficult problem. We propose a computationally tractable approximation method for minimizing the VaR of a portfolio based on robust optimization techniques. The method results in the optimization of a modified VaR measure, Asymmetry-Robust VaR (ARVaR), that takes into consideration asymmetries in the distributions of returns and is coherent, which makes it desirable from a financial theory perspective. We show that ARVaR approximates the Conditional VaR of the portfolio as well. Numerical experiments with simulated and real market data indicate that the proposed approach results in lower realized portfolio VaR, better efficient frontier, and lower maximum realized portfolio loss than alternative approaches for quantile-based portfolio risk minimization.