A factor-model approach for correlation scenarios and correlation stress-testing

TL;DR

This paper introduces a factor model for correlations enabling scenario-based stress testing, demonstrated through the 'London Whale' case, highlighting the impact of correlation changes on portfolio risk, especially for large portfolios.

Contribution

The paper develops a novel factor-model approach for correlation stress testing, including analytical results and methods to identify adverse scenarios using Mahalanobis distance.

Findings

Correlation stress tests reveal significant risk impacts for large portfolios.

Adverse correlation scenarios can be systematically identified.

Correlation and volatility stress tests can be effectively combined.

Abstract

In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for scenario-based stress testing of correlations. We derive a number of analytical results related to a portfolio of homogeneous assets. Using the concept of Mahalanobis distance, we show how to identify adverse scenarios of correlation risk. In addition, we demonstrate how correlation and volatility stress tests can be combined. As an example, we apply the factor-model approach to the "London Whale" portfolio and determine the value-at-risk impact from correlation changes. Since our findings are particularly relevant for large portfolios, where even small correlation changes can have a large impact, a further application would be to stress test portfolios of central counterparties, which are of systemically relevant size.