Title: A factor-model approach for correlation scenarios and correlation stress-testing,
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.