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Incorporating Asymmetric Distributional Information in Robust Value-at-Risk Optimization

Singapore-MIT Alliance and Department of Mathematics, National University of Singapore, Singapore 117543
Mathematics and Sciences Division, Babson College, Babson Park, Massachusetts 02457
Singapore-MIT Alliance and NUS Business School, National University of Singapore, Singapore 117592
matkbn{at}nus.edu.sg
dpachamanova{at}babson.edu
dscsimm{at}nus.edu.sg

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.

Key Words: value-at-risk; robust optimization; coherent risk measures
History: Received: August 15, 2005;