About the event
We develop a general approach for stress testing correlations in stock and credit portfolios. Using Bayesian variable selection methods; we build a sparse factor structure; linking individual names or stocks with country and industry factors. Based on methods from modelling correlations in interest rate modelling; especially in the context of market models; we calibrate a parametric correlation matrix; where correlations of stocks / names are represented as a function of the country and industry factors. Economically meaningful stress scenarios on the factors can then be translated into stressed correlations. The method also lends itself as a reverse tress testing framework: using e.g. the Mahalanobis distance on the joint risk factor distribution; allows to infer worst-case correlation scenarios. We give examples of stress tests including an application to analyse a USD 6.2 bn loss by JP Morgan in 2012; known as the “London Whale”. (This is joint work with Fabian Woebbeking)
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