A Quantile Monte Carlo Approach to Measuring Extreme Credit Risk
8 Pages Posted: 24 Oct 2011
Date Written: October, 23 2011
We apply a novel Quantile Monte Carlo (QMC) model to measure extreme risk of various European industrial sectors both prior to and during the Global Financial Crisis (GFC). The QMC model involves an application of Monte Carlo Simulation and Quantile Regression techniques to the Merton structural credit model. Two research questions are addressed in this study. The first question is whether there is a significant difference in distance to default (DD) between the 50% and 95% quantiles as measured by the QMC model. A substantial difference in DD between the two quantiles was found. The second research question is whether relative industry risk changes between the pre-GFC and GFC periods at the extreme quantile.
Changes were found with the worst deterioration experienced by Energy, Utilities, Consumer Discretionary and Financials; and the strongest improvement shown by Telecommunication, IT and Consumer goods. Overall, we find a significant increase in credit risk for all sectors using this model as compared to the traditional Merton approach. These findings could be important to banks and regulators in measuring and providing for credit risk in extreme circumstances.
Keywords: Value at Risk, Distance to Default, Probability of Default, Monte Carlo, Quantile Regression
JEL Classification: G01, G21, G28
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