A Quantile Regression Approach to the Multiple Period Value at Risk Estimation

Abstract

This research focuses on methods for multiple period Value at Risk (VaR) estimation by utilizing some common approaches like RiskMetrics and empirical distribution and examining quantile regression. In a simulation study we compare the least square and quantile regression percentiles with the actual percentiles for different error distributions. We also discuss the method of selecting response and explanatory variables for the quantile regression approach. In an empirical study, we apply the three VaR estimation approaches to the aggregate returns of four major market indices. The results indicate that the quantile regression approach is better than the other two approaches.