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
School of Accounting, Finance and Economics, Edith Cowan University (Email: email@example.com)
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...
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