A nonparametric copula approach to conditional Value-at-Risk

TL;DR

This paper introduces a nonparametric copula-based method for estimating conditional Value-at-Risk, offering a more robust and flexible alternative to traditional parametric models, with promising results in real-world back-testing.

Contribution

It proposes a novel nonparametric framework utilizing copula density estimation to improve conditional VaR estimation in financial risk management.

Findings

Potentially superior performance in back-testing compared to industry standards

Robustness and flexibility over traditional parametric models

Effective extraction of conditional distribution quantiles

Abstract

Value-at-Risk and its conditional allegory, which takes into account the available information about the economic environment, form the centrepiece of the Basel framework for the evaluation of market risk in the banking sector. In this paper, a new nonparametric framework for estimating this conditional Value-at-Risk is presented. A nonparametric approach is particularly pertinent as the traditionally used parametric distributions have been shown to be insufficiently robust and flexible in most of the equity-return data sets observed in practice. The method extracts the quantile of the conditional distribution of interest, whose estimation is based on a novel estimator of the density of the copula describing the dynamic dependence observed in the series of returns. Real-world back-testing analyses demonstrate the potential of the approach, whose performance may be superior to its industry counterparts.