Applying Quantile Regression to Industry Default Risk in Europe
Document Type
Journal Article
Faculty
Faculty of Business and Law
School
School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre
RAS ID
14520
Abstract
The problems experienced by banks worldwide during the Global Financial Crisis (GFC), including bank failures and capital shortages, demonstrated the importance of understanding extreme credit risk in dynamic economic circumstances. 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 highly significant 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. As a robustness check the QMC model is compared to a published Conditional Value at Risk (CVaR) based model. Overall, we find a significant increase in credit risk for all sectors using the QMC 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.
Access Rights
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Comments
Allen, D. E., Boffey, R. R., & Powell, R. (2012). Applying Quantile Regression to Industry Default Risk in Europe. International Review of Business Research Papers, 8(4), 20-29.