Portfolio Risk Assessment using Copula Models

TL;DR

This paper explores the use of copula models to better understand and predict multivariate dependence in financial portfolios, proposing an algorithm for risk measurement and comparing different copula types.

Contribution

It introduces a novel algorithm for risk measure computation using copula models and demonstrates their superior predictive ability over empirical methods.

Findings

Copula models provide lower risk estimates than historical data.

Gaussian, Student's t, and vine copulas outperform empirical methods in risk prediction.

All three copula models show improved accuracy in estimating risk measures.

Abstract

In the paper, we use and investigate copulas models to represent multivariate dependence in financial time series. We propose the algorithm of risk measure computation using copula models. Using the optimal mean-$CVaR$ portfolio we compute portfolio's Profit and Loss series and corresponded risk measures curves. Value-at-risk and Conditional-Value-at-risk curves were simulated by three copula models: full Gaussian, Student's $t$ and regular vine copula. These risk curves are lower than historical values of the risk measures curve. All three models have superior prediction ability than a usual empirical method. Further directions of research are described.