Correlation scenarios and correlation stress testing

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.(c) 2022 Published by Elsevier B.V.

Automated summary

We propose a general approach for stress testing correlations of financial asset portfolios. The method specifies the correlation matrix of asset returns parametrically, where correlations are represented as a function of risk factors, such as country and industry factors. Bayesian variable selection methods are used to build a sparse factor structure linking assets and risk factors. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. The approach also serves as a reverse stress testing framework, allowing the inference of worst-case correlation scenarios using the Mahalanobis distance or Highest Density Regions (HDR) on the joint risk factor distribution. We provide examples of stress tests on a large portfolio of European and North American stocks.