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Transitional credit modelling and its relationship to market value at risk: an Australian sectoral perspective


  • David E. Allen
  • Robert Powell


Internal credit risk modelling is important for banks for the calculation of capital adequacy in terms of the Basel Accords, and for the management of sectoral exposure. We examine Credit Value at Risk (VaR), Conditional Credit Value at Risk (Credit CVaR) and the relationship between market and credit risk. Significant association is found between different Credit CVaR methods, and between market and credit risk. Simpler Credit CVaR methods are found to be viable alternatives to more complex methodology. The relationship between market and credit risk is used to develop a new model that allows banks to incorporate industry risk into transition modelling, without macroeconomic analysis. Copyright (c) The Authors. Journal compilation (c) 2009 AFAANZ.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:acctfi:v:49:y:2009:i:3:p:425-444

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    Cited by:

    1. Allen, David E & Powell, Robert, 2008. "Structural Credit Modelling and Its Relationship to Market Value at Risk: An Australian Sectoral Perspective," MPRA Paper 47206, University Library of Munich, Germany.
    2. repec:blg:reveco:v:69:y:2017:i:3:p:19-28 is not listed on IDEAS
    3. Henry Asante Antwi & Zhou Lulin & Ethel Yiranbon & James Onuche Ayegba & Mary-Ann Yebaoh & Emmanuel Osei Bonsu, 2014. "Risk Modelling in Healthcare Markets: a Comparative Analysis of three Risk Measurement Approaches," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 4(2), pages 271-281, April.
    4. 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.
    5. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.

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