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A Quantile Monte Carlo approach to measuring extreme credit risk

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Author Info

  • David E Allen

    ()
    (School of Accounting Finance & Economics, Edith Cowan University)

  • R.R Boffey

    ()
    (School of Accounting Finance & Economics, Edith Cowan University)

  • R. J. Powell

    ()
    (School of Accounting Finance & Economics, Edith Cowan University)

Abstract

We apply a novel Quantile Monte Carlo (QMC) model to measure extreme risk of various European industrial sectors both prior to and during the Global Financial Crisis (GFC). The QMC model involves an application of Monte Carlo Simulation and Quantile Regression techniques to the Merton structural credit model. Two research questions are addressed in this study. The first question is whether there is a significant difference in distance to default (DD) between the 50% and 95% quantiles as measured by the QMC model. A substantial difference in DD between the two quantiles was found. The second research question is whether relative industry risk changes between the pre-GFC and GFC periods at the extreme quantile. Changes were found with the worst deterioration experienced by Energy, Utilities, Consumer Discretionary and Financials; and the strongest improvement shown by Telecommunication, IT and Consumer goods. Overall, we find a significant increase in credit risk for all sectors using this model as compared to the traditional Merton approach. These findings could be important to banks and regulators in measuring and providing for credit risk in extreme circumstances.

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Bibliographic Info

Paper provided by Edith Cowan University, School of Business in its series Working papers with number 2011-02.

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Length: 9 pages
Date of creation: Feb 2011
Date of revision:
Handle: RePEc:ecu:wpaper:2011-02

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Related research

Keywords: Asset Selection; Factor Model; DEA; Quantile Regression;

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References

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  1. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
  2. David Edmund Allen & Robert John Powell & Abhay Kumar Singh, 2012. "Beyond reasonable doubt: multiple tail risk measures applied to European industries," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 671-676, May.
  3. David E Allen & Akhmad R. Kramadibrata & R. J. Powell & Abhay Kumar Singh, 2011. "Comparing Australian and US Corporate Default Risk using Quantile Regression," Working papers 2011-04, Edith Cowan University, School of Business.
  4. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
  5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-70, May.
  6. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, December.
  7. Lopez, Jose A., 2004. "The empirical relationship between average asset correlation, firm probability of default, and asset size," Journal of Financial Intermediation, Elsevier, vol. 13(2), pages 265-283, April.
  8. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
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Cited by:
  1. David E Allen & Akhmad R. Kramadibrata & R. J. Powell & Abhay Kumar Singh, 2011. "Tail Risk for Australian Emerging Market Entities," Working papers 2011-07, Edith Cowan University, School of Business.

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