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Modelling tail credit risk using transition matrices


  • Allen, D.E.
  • Kramadibrata, A.R.
  • Powell, R.J.
  • Singh, A.K.


Innovative transition matrix techniques are used to compare extreme credit risk for Australian and US companies both prior to and during the global financial crisis (GFC). Transition matrix methodology is traditionally used to measure Value at Risk (VaR), a measure of risk below a specified threshold. We use it to measure Conditional Value at Risk (CVaR) which is the risk beyond VaR. We find significant differences in VaR and CVaR measurements in both the US and the Australian markets. 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 experienced in the US during the GFC. Traditional 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. Our revised model shows greater change between Pre-GFC and GFC total credit risk than the traditional model, meaning that those industries that were riskiest during the GFC are not the same industries that were riskiest Pre-GFC. Overall, our analysis finds that our innovative modelling techniques are better able to account for the impact of extreme risk circumstances and industry composition than traditional transition matrix techniques.

Suggested Citation

  • Allen, D.E. & Kramadibrata, A.R. & Powell, R.J. & Singh, A.K., 2013. "Modelling tail credit risk using transition matrices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 67-75.
  • Handle: RePEc:eee:matcom:v:93:y:2013:i:c:p:67-75
    DOI: 10.1016/j.matcom.2012.09.011

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    References listed on IDEAS

    1. David E. Allen & Robert Powell, 2009. "Transitional credit modelling and its relationship to market value at risk: an Australian sectoral perspective," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 49(3), pages 425-444.
    2. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    3. Thomas C. Wilson, 1998. "Portfolio credit risk," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 71-82.
    4. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
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    Cited by:

    1. Michael McAleer & Felix Chan & Les Oxley, 2013. "Modeling and Simulation: An Overview," Working Papers in Economics 13/18, University of Canterbury, Department of Economics and Finance.
    2. Kun Liang & Cuiqing Jiang & Zhangxi Lin & Weihong Ning & Zelin Jia, 2017. "The nature of sellers’ cyber credit in C2C e-commerce: the perspective of social capital," Electronic Commerce Research, Springer, vol. 17(1), pages 133-147, March.


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