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MANSCI
2008

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

9 years 8 months ago
Incorporating Asymmetric Distributional Information in Robust Value-at-Risk Optimization
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.
Karthik Natarajan, Dessislava Pachamanova, Melvyn
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where MANSCI
Authors Karthik Natarajan, Dessislava Pachamanova, Melvyn Sim
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