Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2018 034

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

Natalie Packham
Fabian Woebbeking

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. 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
ndings 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.

Correlation stress testing, scenario selection, market risk, "London Whale"

JEL Classication:
C58, G15, G17, G18