Mramr

19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011 http://mssanz.org.au/modsim2011

Credit risk measurement methodologies
D. E. Allen and R. J. Powella a School of Accounting, Finance and Economics, Edith Cowan University (Email: [e-mail])

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

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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

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