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A Comparative Analysis of Dependence Levels in Intensity-Based and Merton-Style Credit Risk Models

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Advances in Risk Management

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Abstract

In finance, especially for credit portfolio modeling, basket credit derivatives (CDOs, n-th to default) pricing and hedging, the building of an accurate measure of the dependence between the underlying default events is becoming a key-challenge (see Crouhy, Galai and Mark, 2002; Koyluoglu and Hickman, 1998, for a review of the current credit risk portfolio models). This new frontier has induced a huge amount of literature for several years: Nyfeler (2000), Frey and McNeil (2001), Schönbucher and Schubert (2001), Das, Geng and Kapadia (2002), Elizalde (2003), Turnbull (2003), Yu (2003), among others.

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References

  • Andersen, P.K., Gill, R., Borgan, O. and Keiding, N. (1997) Statistical Models Based on Counting Processes (New York: Springer).

    Google Scholar 

  • Carr, P. and Wu, L. (2002) “The Finite Moment Log Stable Process and Option Pricing”, Working Paper.

    Google Scholar 

  • Chambers, J.M., Mallows, C.L. and Stuck, B.W. (1976) “A Method for Simulating Stable Random Variables”, Journal of the American Statistical Association, 71 (2): 340–4.

    Article  Google Scholar 

  • Clayton, D. and Cuzick, J. (1985) “Multivariate Generalizations of the Proportional Hazards Model”, Journal of the Royal Statistical Society, A, 148 (1): 82–117.

    Article  Google Scholar 

  • Crouhy, M., Galai, D. and Mark, R. (2000) “A Comparative Analysis of Current Credit Risk Models”, Journal of Banking and Finance, 24 (1–2): 59–117.

    Article  Google Scholar 

  • Dabrowska, D. (1988) “Kaplan-Meier on the Plane”, Annals of Statistics, 16 (4): 1475–89.

    Article  Google Scholar 

  • Das, S., Freed, L., Geng, G. and Kapadia, N. (2002) “Correlated Default Risk”, EFAAnnual Conference Paper 928 http://ssrn.com/abstract=331901).

    Google Scholar 

  • Duffie, D. and Singleton, K. (1999) “Modeling Term Structure of Defaultable Bonds”, Review of Financial Studies, 12 (4): 687–720.

    Article  Google Scholar 

  • Elizalde, A. (2003) “Credit Risk Models I: Default Correlation in Intensity Models”, Working Paper CEMFI, Universidad Pdblica de Navarra, Spain.

    Google Scholar 

  • Fermanian, J.D. (1997) “Multivariate Hazard Rates Under Random Censorship”, Journal of Multivariate Analysis, 62 (2): 273–309.

    Article  Google Scholar 

  • Frey, R. and McNeil, A. (2001) Modelling Dependent Defaults. ETH E-collection.

    Google Scholar 

  • Hougaard, P. (1986) “Survival Models for Heterogeneous Populations Derived from Stable Distributions”, Biometrika, 73 (2): 387–96.

    Article  Google Scholar 

  • Hougaard, P. (2000) Analysis of Multivariate Survival Data (New York, NY: Statistics for Biology and Health, Springer).

    Book  Google Scholar 

  • Hull, J. and White, A. (2001) “Valuing Credit Default Swaps II: Modeling Default Correlations”, Journal of Derivatives, 8 (3): 12–22.

    Article  Google Scholar 

  • Jarrow, R., Lando, D. and Turnbull, S. (1997) “A Markov Model for the Term Structure of Credit Risk Spreads”, Review of Financial Studies, 10 (3): 481–523.

    Article  Google Scholar 

  • Koyluoglu, H. and Hickman, A. (1998) “A Generalized Framework for Credit Risk Portfolio Models”, Working Paper (New York: Wyman & Co.).

    Google Scholar 

  • Luciano, E. (2004) “Credit Risk Assessment via Copulas: Association In-Variance and Risk-Neutrality”, Working Paper, University of Turin, Italy.

    Google Scholar 

  • Metayer, B. (2005) “Shared Frailty Model for Rating Transitions”, University of Zurich Working Paper.

    Google Scholar 

  • Mittnik, S. and Rachev, S.T. (1999) Stable Models in Finance (New York, NY: John Wiley & Sons).

    Google Scholar 

  • Nolan, J.P. (2004) “Stable Distributions: Models for Heavy Tailed Data”, Working Paper, American University, Washington, DC.

    Google Scholar 

  • Nyfeler, M. (2000) “Modeling Dependencies in Credit Risk Management”, Thesis, ETH, ZĂĽrich.

    Google Scholar 

  • Paik, M.C., Tsai, W. and Ottman, R. (1994) “Multivariate Survival Analysis Using Piecewise Gamma Frailty”, Biometrics, 50 (4): 975–88.

    Article  Google Scholar 

  • Parner, E. (1998) “Asymptotic Theory for the Correlated Gamma-Frailty Model”, Annals of Statistics, 26 (1): 183–214.

    Article  Google Scholar 

  • Samorodnitsky, G. and Taqqu, M.S. (1994) Stable Non-Gaussian Random Variables (New York, NY: Chapman and Hall).

    Google Scholar 

  • Schlögl, E. (2002) “Default Correlation Modeling”, Working Paper, University of Technology Sydney, Australia.

    Google Scholar 

  • Schönbucher, P.J. (2001) “Factor Models for Portfolio Credit Risk”, Bonn Economics Discussion Paper, No. 16.

    Google Scholar 

  • Schönbucher, P.J. (2003) Credit Derivatives Pricing Models: Models, Pricing and Implementation (New York, NY: John Wiley & Sons).

    Google Scholar 

  • Schönbucher, P.J. and Schubert, D. (2001) “Copula-Dependent Default Risk in Intensity Models”, Bonn University Working Paper.

    Google Scholar 

  • de Servigny, A. and Renault, O. (2002) “Default Correlation: Empirical Results”, Working Paper, Standard and Poor’s solutions.

    Google Scholar 

  • Standard & Poor’s (2003) Special Report. Rating performances 2002. FĂ©vrier 2003.

    Google Scholar 

  • Turnbull, S. (2003) “Practical Issues in Modeling Default Dependence”, Working Paper, Houston.

    Google Scholar 

  • Yashin, A.I. and lachine, I.A. (1995) “Genetic Analysis of Durations: Correlated Frailty Model Applied to Survival of Danish Twins”, Genetic Epidemiology, 12 (3): 529–38.

    Article  Google Scholar 

  • Yu, F. (2003) “Dependent Default in Intensity-based Models”, Working Paper, University of California, Irvine.

    Google Scholar 

  • Yue, H. and Chan, K.S. (1997) “A Dynamic Frailty Model for Multivariate Survival Data”, Biometrics, 53 (3): 785–93.

    Article  Google Scholar 

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© 2007 Jean-David Fermanian and Mohammed Sbai

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Fermanian, JD., Sbai, M. (2007). A Comparative Analysis of Dependence Levels in Intensity-Based and Merton-Style Credit Risk Models. In: Gregoriou, G.N. (eds) Advances in Risk Management. Finance and Capital Markets Series. Palgrave Macmillan, London. https://doi.org/10.1057/9780230625846_7

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