A Quantile Monte Carlo Approach to Measuring Extreme Credit Risk

8 Pages Posted: 24 Oct 2011

See all articles by David E. Allen

David E. Allen

School of Mathematics and Statistics, The University of Sydney; Financial Research Network (FIRN); Department of Finance; School of Business and Law, Edith Cowan University

Ray Boffey

School of Finance and Business Economics; Financial Research Network (FIRN)

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics; Financial Research Network (FIRN)

Date Written: October, 23 2011

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.

Keywords: Value at Risk, Distance to Default, Probability of Default, Monte Carlo, Quantile Regression

JEL Classification: G01, G21, G28

Suggested Citation

Allen, David Edmund and Boffey, Ray and Powell, Robert J., A Quantile Monte Carlo Approach to Measuring Extreme Credit Risk (October, 23 2011). Available at SSRN: https://ssrn.com/abstract=1948311 or http://dx.doi.org/10.2139/ssrn.1948311

David Edmund Allen

School of Mathematics and Statistics, The University of Sydney ( email )

School of Mathematics and Statistics F07
University of Sydney
Sydney, New South Wales 2006
Australia

HOME PAGE: http://www.maths.usyd.edu.au

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Department of Finance ( email )

Taiwan
Taiwan

School of Business and Law, Edith Cowan University

100 Joondalup Drive
Joondalup, WA 6027
Australia

HOME PAGE: http://www.dallenwapty.com

Ray Boffey (Contact Author)

School of Finance and Business Economics ( email )

Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Robert J. Powell

Edith Cowan University - School of Accounting, Finance and Economics ( email )

Joondalup Campus
Perth
Joondalup 6027, WA
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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