Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", Berlin
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.