An Approach of Estimating the Value at Risk of Heavy-tailed Distribution using Copulas

Abstract

The value at risk (VaR) plays a fundamental role in modeling risk in financial studies. We propose a approach in estimating the VaR for heavy-tailed distribution by taking into account the effects of certain covariates on the variable of interest. This method, involves estimating the extreme conditional quantiles by using the assciated copula. Morever, we use Bernstein copulas to estimate the intermediate conditional quantile in a non-parametric approach of the direct method.Then, the extreme conditional quantile is also estimated and we study the asymptotic properties of this new estimator.