Empirical Likelihood Intervals for Conditional Value-at-Risk in Heteroscedastic Regression Models

Abstract

. Non-parametric regression models have been studied well including estimating the conditional mean function, the conditional variance function and the distribution function of errors. In addition, empirical likelihood methods have been proposed to construct confidence intervals for the conditional mean and variance. Motivated by applications in risk management, we propose an empirical likelihood method for constructing a confidence interval for the pth conditional value-at-risk based on the non-parametric regression model. A simulation study shows the advantages of the proposed method.