Xtreme Credit Risk Models: Implications for Bank Capital Buffers

22 Pages Posted: 1 Mar 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

Akhmad Kramadibrata

Edith Cowan University - School of Accounting, Finance and Economics

Robert J. Powell

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

Abhay Kumar-Singh

Edith Cowan University

Date Written: March 1, 2011

Abstract

The Global Financial Crisis (GFC) highlighted the importance of measuring and understanding extreme credit risk. This paper applies Conditional Value at Risk (CVaR) techniques, traditionally used in the insurance industry to measure risk beyond a predetermined threshold, to four credit models. For each of the models we use both Historical and Monte Carlo Simulation methodology to create CVaR measurements. The four extreme models are derived from modifications to the Merton structural model (which we term Xtreme-S), the CreditMetrics Transition model (Xtreme-T), Quantile regression (Xtreme-Q), and the author’s own unique iTransition model (Xtreme-i) which incorporates industry factors into transition matrices. For all models, CVaR is found to be significantly higher than VaR, and there are also found to be significant differences between the models in terms of correlation with actual bank losses and CDS spreads. The paper also shows how extreme measures can be used by banks to determine capital buffer requirements.

Keywords: credit risk, conditional value at risk, conditional probability of default, historical simulation, Monte Carlo simulation

JEL Classification: G01, G21, G28

Suggested Citation

Allen, David Edmund and Kramadibrata, Akhmad and Powell, Robert J. and Kumar-Singh, Abhay, Xtreme Credit Risk Models: Implications for Bank Capital Buffers (March 1, 2011). Systemic Risk, Basel III, Financial Stability and Regulation 2011. Available at SSRN: https://ssrn.com/abstract=1773363 or http://dx.doi.org/10.2139/ssrn.1773363

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

Akhmad Kramadibrata

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

270 Joondalup Drive
Joondalup, WA g027
Australia
+61 8 6304 5265 (Phone)

Robert J. Powell (Contact Author)

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

Abhay Kumar-Singh

Edith Cowan University ( email )

Mount Lawley Campus
Perth
Churchlands 6018 WA, Victoria
Australia

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