Topics: Credit risk, Credit rating, Asset Pages: 15 (4505 words) Published: February 24, 2013
19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011

Credit risk measurement methodologies
D. E. Allen and R. J. Powella

School of Accounting, Finance and Economics, Edith Cowan University (Email:

Abstract: The significant problems experienced by banks during the Global Financial Crisis have highlighted the critical importance of measuring and providing for credit risk. This paper will examine four popular methods used in the measurement of credit risk and provide an analysis of the relative shortcomings and advantages of each method. The study includes external ratings approaches, financial statement analysis models, the Merton / KMV structural model, and the transition based models of CreditMetrics and CreditPortfolioView. Each model assesses different criteria, and an understanding of the merits and disadvantages of the various models can assist banks and other credit modellers in choosing between the available credit modelling techniques. Keywords: credit models; credit value at risk; probability of default


Allen and Powell, Credit risk measurement methodologies 1. INTRODUCTION

High bank failures and the significant credit problems faced by banks during the Global Financial Crisis (GFC) are a stark reminder of the importance of accurately measuring and providing for a credit risk. There are a variety of available credit modelling techniques, leaving banks faced with the dilemma of deciding which model to choose. Historically, prominent methods include external ratings services like Moody’s, Standard & Poor’s (S&P) or Fitch, and financial statement analysis models (which provide a rating based on the analysis of financial statements of individual borrowers, such as the Altman z score and Moody’s RiskCalc). Credit risk models which measure default probability (such as Structural Models) or Value at Risk (VaR) attained a great deal more prominence with the advent of Basel II. This article examines four widely used modelling techniques, including external ratings, financial statement analysis models, the Merton / KMV structural model and the Transition models of CreditMetrics and CreditPortfolioView, including an overview of the models and a comparison of their relative strengths and weaknesses. Structural models are based on option pricing methodologies and obtain information from market data. A default event is triggered by the capital structure when the value of the obligor falls below its financial obligation (such as the Merton and KMV models). VaR based models provide a measurement of expected losses over a given time period at a given tolerance level. These include the JP Morgan CreditMetrics model which uses a Transition Matrix, and the CreditPortfolioView model which incorporates macroeconomic factors into a Transition approach. 2. CREDIT MODEL METHODOLOGIES 2.1. External Ratings Services The most prominent of the ratings services are Standard & Poor’s (S&P), Moody’s & Fitch. The ratings provide a measure of the relative creditworthiness of the entity, taking into account a wide range of factors such as environmental conditions, competitive position, management quality, and the financial strength of the business. Table 1 provides a calibration between the well known rating agencies. The definitions are based on Standard & Poor’s (2011). This calibration is important when loan portfolios comprise entities with contains ratings from different ratings services. Based on S&P definitions ratings are:- AAA: Extremely strong capacity to meet financial commitments- highest rating; AA: Very strong capacity to meet financial commitments; A: Strong capacity to meet financial commitments, but somewhat susceptible to adverse economic conditions and changes in circumstances; BBB: Considered lowest investment grade by market participants; BB: Less vulnerable in the near-term but faces major ongoing...

References: Allen, D. E., & Powell, R. (2009). Transitional Credit Modelling and its Relationship to Market at Value at Risk: An Australian Sectoral Perspective. Accounting and Finance, 49(3), 425-444. Allen, D. E., & Powell, R. (2011). Customers and Markets: Both are Essential to credit Risk Management in Australia. Australasian Accounting, Business and Finance Journal, 5(1), 57-75. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance 189-209. Altman, E. I. (2000). Predicting Financial Distress of Companies: Revisiting the z-score and Zeta® models. Retrieved 19 August 2009. Available at Australian Prudential Regulation Authority. (1999). Submission to the Basel Committee on Banking Supervision - Credit Risk Modelling: Current Practices and Applications. Retrieved 22 June 2011. Available at
Allen and Powell, Credit risk measurement methodologies Bank for international Settlements. (2011). Long-term Rating Scales Comparison. Retrieved 1 June 2011. Available at Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research 4, 71-111. Bharath, S. T., & Shumway, T. (2008). Forecasting Default with the Merton Distance-to-Default Model. The Review of Financial Studies, 21(3), 1339-1369. Crosbie, P., & Bohn, J. (2003). Modelling Default Risk: Moody 's KMV Company. Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking and Finance, 24 (2000), 59-117. D 'Vari, R., Yalamanchili, K., & Bai, D. (2003). Application of Quantitative Credit Risk Models in Fixed Income Portfolio Management. Retrieved 16 August 2009. Available at Du, Y., & Suo, W. (2007). Assessing Credit Quality from the Equity Market: Can a Structural Approach Forecast Credit Ratings? Canadian Journal of Administrative Sciences, 24(3), 212-228. Eom, Y., Helwege, J., & Huang, J. (2004). Structural Models of Corporate Bond Pricing: An Empirical Analysis. Review of Financial Studies, 17, 499-544. Grice, J., & Dugan, M. (2001). The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher. Review of Quantitative Finance and Accounting, 17(2), 151-166. Gupton, G. M., Finger, C. C., & Bhatia, M. (1997). CreditMetrics - Technical Document. New York: J.P. Morgan & Co. Incorporated. Gutzeit, G., & Yozzo, J. (2011). Z-Score Performance Amid Great Recession American Bankruptcy Insitute Journal, 30(2), 44-46. He, Y., & Kamath, R. (2006). Business failure prediction in retail industry: an empirical evaluation of generic bankruptcy prediction models. Academy of Accounting and Financial Studies Journal, 10(2), 97-110. Huang, M., & Huang, J. (2003). How Much of the Corporate-Treasury Yield Spread is Due to Credit Risk? Unpublished Manuscripy, Stanford University. Jarrow, R. A. (2001). Default Parameter Estimation Using Market Prices. Financial Analysts Journal, 57(5), 75. Jarrow, R. A., Lando, D., & Turnbull, S. (1997). A Markov Model for the Term Structure of Credit Spreads. Review of Financial Studies, 10, 481-523. Katz, S., Lilien, S., & Nelson, B. (1985). Stock Market Behavior Around Bankruptcy Model Distress and Recovery Predictions. Financial Analysts Journal 41 70-74. Kealhofer, S., & Bohn, J. R. (1993). Portfolio Management of Default Risk. Retrieved 11 June 2009. Available at Lechner, A., & Ovaert, T. (2010). Techniques to Account for Leptokurtosis and Assymetric Behaviour in Returns Distributions. Journal of Risk Finance, 11(5), 464-480. Moody 's KMV Company. (2003). RiskCalc Australia Fact Sheet. Retrieved 11 June 2009. Available at Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, Spring, 109-131. Pesaran, M. H., Schuermann, T., Treutler, B. J., & Weiner, S. M. (2003). Macroeconomic Dynamics and Credit Risk: A Global Perspective. Retrieved 1 June 2011. Available at Platt, H. D., & Platt, M. B. (1990). Development of a Class of Stable Predictive Variables: the Case of Bankruptcy Prediction. Journal of Business, Finance and Accounting 17(1), 31-51. Queen, M., & Roll, R. (1987). Firm Mortality: Using Market Indicators to Predict Survival. Financial Analysts Journal, 43, 9-26. Samanta, P., Azarchs, T., & Hill, N. (2005). Chasing Their Tails: Banks Look Beyond Value-At-Risk. , RatingsDirect. Saunders, A., & Allen, L. (2002). Credit Risk Measurement. New York: John Wiley & Sons, Inc. Standard & Poor 's. (2011). Ratings. Retrieved 1 June 2011. Available at Sy, W. (2007). A Causal Framework for Credit Default Theory: APRA, wp08-03. Sy, W. (2008). Credit Risk Models: Why They Failed in the Financial Crisis: APRA, wp0803. Vassalou, M., & Xing, Y. (2004). Default Risk in Equity Returns. Journal of Finance, 59, 831-868. Wilson, T. C. (1998). Portfolio Credit Risk. Economic Policy Review October, 4(3), 71-82. Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research 24, 59-82.
Continue Reading

Please join StudyMode to read the full document

Become a StudyMode Member

Sign Up - It's Free