Effectiveness of Copula-Extreme Value Theory in Estimating Value-at-Risk : Empirical Evidence from Asian Emerging Markets

Traditional Monte Carlo simulation using linear correlations induces estimation bias in measuring portfolio value-at-risk (VaR) due to the well-documented existence of fat-tail, skewness, truncations, and non-linear relations of return distributions. In this paper, we evaluate the effectiveness using copula-extreme-value-based semi-parametric approaches in assessing portfolio risks in six Asian markets based on their different return distribution shapes. We incorporate extreme value theory (EVT) to model the tails of the return distributions and various copulas to build the joint distribution of returns. The backtesting analysis of the Monte Carlo VaR simulation suggests that the Clayton copula-EVT has the best performance regardless of the shapes of the return distributions, and in general the copulas with the EVT perform better estimation of VaRs than the traditional copulas. This concludes the economic significance in incorporating the down-side shock in risk management