Document Type

Conference Proceeding


Faculty of Business and Law


School of Accounting, Finance and Economics / Finance, Economics, Markets and Accounting Research Centre




This article was originally published as: Allen, D. E., Kramadibrata, A. R., Powell, R. , & Singh, A. (2011). Innovative transition matrix techniques for measuring extreme risk: an Australian and U.S. comparison. Paper presented at the 19th International Congress on Modelling and Simulation. Australian Mathematical Sciences Institute. Perth, Australia. Original article available here


Comparing Australia and the U.S. both prior to and during the Global Financial Crisis (GFC), using a dataset which includes more than six hundred companies, this paper modifies traditional transition matrix credit risk modelling to address two important issues. Firstly, extreme credit risk can have a devastating impact on financial institutions, economies and markets as highlighted by the GFC. It is therefore essential that extreme credit risk is accurately measured and understood. Transition matrix methodology, which measur es the probability of a borrower transitioning from one credit rating to another, is traditionally used to m easure Value at Risk (VaR), a measure of risk below a specified threshold. An alternate measure to VaR is Conditional Value at Risk (CVaR), which was initially developed in the insurance industry and has been gaining popularity as a measure of extreme market risk. CVaR measures those risks beyond VaR. We incorp orate CVaR into transition matrix methodology to measure extreme credit risk. We find significant differences in the VaR an d CVaR measurements in both the US and Australian markets, as CVaR captures those extr eme risks that are ignored by VaR. We also find a greater differential between VaR and CVaR for the US as compared to Australia, reflecting the more extreme credit risk that was experience d in the US during the GFC. The second issue is that relative industry risk does not stay static over time, as highlighted by the problems experienced by financial sector during the GFC. Tr aditional transition matrix methodology assumes that all borrowers of the same credit rating transition equally, whereas we incorporate an adjustment based on industry share price fluctuations to allow for unequal transition among industries. The existing CreditPortfolioView model applies industry adjustment factors to credit transitio n based on macroeconomic variables. The financial sector regulator in Australia, APRA, has found that banks do not favour such credit modelling based on macroeconomic variables due to modelling complexity and forecasting inaccuracy. We use our own i Transition model, which incorporates industry factors derived from equity prices, a much simpler approach than macroeconomic modelling. The i Transition model shows a greater change between Pre-GFC and GFC total credit risk than the traditional model. This means that those industries that were riskiest during the GFC are not the same i ndustries that were riskiest Pre-GFC. The i Transition model also finds that the Australian portfolio, which has a much higher weighting towards financial stocks than the US portfolio, transitions very differently to the more balanced industry-weighted US portfolio. These results highlight the importance of including industry analysis into credit risk modelling. To ensure a thorough analysis of the topic we use va rious approaches to measur ing CVaR. This includes an analytical approach which is based on actual credit ratings as well as a Monte Carlo simulation approach which generates twenty thousand observ ations for each entity in the data set. We also incorporate historical default probabilities into the model in two different ways, one method using an average historical default rate over time, and the other method using annual default prob abilities which vary from year to year. Overall, this comprehensive analysis finds that innovative modelling techniques are better able to account for the impact of extreme risk circumstances and industry composition than traditional transition matrix techniques.

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