Conditional Tail-Related Risk Estimation Using Composite Asymmetric Least Squares and Empirical Likelihood

TL;DR

This paper introduces a novel two-step, data-driven approach combining composite asymmetric least squares and empirical likelihood to accurately estimate conditional VaR and ES in GARCH models, with proven theoretical properties.

Contribution

It develops a new method integrating CALS and empirical likelihood for tail risk estimation, providing theoretical guarantees and empirical validation.

Findings

Method is theoretically consistent and asymptotically normal.

Performs competitively against existing tail risk estimation methods.

Validated through Monte Carlo simulations and empirical data analysis.

Abstract

In this article, by using composite asymmetric least squares (CALS) and empirical likelihood, we propose a two-step procedure to estimate the conditional value at risk (VaR) and conditional expected shortfall (ES) for the GARCH series. First, we perform asymmetric least square regressions at several significance levels to model the volatility structure and separate it from the innovation process in the GARCH model. Note that expectile can serve as a bond to make up the gap from VaR estimation to ES estimation because there exists a bijective mapping from expectiles to specific quantile, and ES can be induced by expectile through a simple formula. Then, we introduce the empirical likelihood method to determine the relation above; this method is data-driven and distribution-free. Theoretical studies guarantee the asymptotic properties, such as consistency and the asymptotic normal distribution of the estimator obtained by our proposed method. A Monte Carlo experiment and an empirical application are conducted to evaluate the performance of the proposed method. The results indicate that our proposed estimation method is competitive with some alternative existing tail-related risk estimation methods.