Title

A Quantile Monte Carlo Approach to Measuring Extreme Credit Risk

Document Type

Conference Proceeding

Publisher

World Business Institute Australia

Faculty

Faculty of Business and Law

School

School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre

RAS ID

12965

Comments

This article was originally published as: Allen, D. E., Boffey, R. R., & Powell, R. (2011). A quantile Monte Carlo approach to measuring extreme credit risk. Paper presented at the World Business Economics and Finance Conference. Bangkok 2011. Original article available here

Abstract

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.

DOI

10.2139/ssrn.1948311

 

Link to publisher version (DOI)

10.2139/ssrn.1948311