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A Quantile Monte Carlo approach to measuring extreme credit risk

  • 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)

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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File URL: http://www.ecu.edu.au/__data/assets/pdf_file/0011/296993/Wp1102rb.pdf
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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
Contact details of provider: Postal: 270 Joondalup Drive, Joondalup, Western Australia, 6027
Web page: http://www.ecu.edu.au/schools/business/overview

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  1. Philip Gharghori & Howard Chan & Robert Faff, 2007. "Are the Fama-French Factors Proxying Default Risk?," Australian Journal of Management, Australian School of Business, vol. 32(2), pages 223-249, December.
  2. Omar Arias & Kevin F. Hallock & Walter Sosa Escudero, 1999. "Individual Heterogeneity in the Returns to Schooling: Instrumental Variables Quantile Regression using Twins Data," Department of Economics, Working Papers 016, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata.
  3. 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.
  4. 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.
  5. Jose A. Lopez, 2002. "The empirical relationship between average asset correlation, firm probability of default and asset size," Working Paper Series 2002-05, Federal Reserve Bank of San Francisco.
  6. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  7. Sreedhar T. Bharath & Tyler Shumway, 2008. "Forecasting Default with the Merton Distance to Default Model," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1339-1369, May.
  8. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
  9. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  10. 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.
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