Correlation scenarios and correlation stress testing

TL;DR

This paper presents a comprehensive method for stress testing asset portfolio correlations by modeling them through risk factors, enabling both scenario analysis and reverse stress testing to identify worst-case correlation conditions.

Contribution

It introduces a parametric correlation modeling approach with Bayesian variable selection, facilitating economically meaningful stress scenarios and reverse stress testing of financial portfolios.

Findings

Effective stress testing of large stock portfolios.

Identification of worst-case correlation scenarios.

Application to European and North American stocks.

Abstract

We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or highest density regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.