Bayesian inference for generalized linear mixed models of portfolio credit risk

Journal of Empirical Finance

Volumn 14, Issue 2, 2007, Pages 131-149

  McNeil, Alexander J ^a   Wendin, Jonathan P ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

Bayesian inference; Credit risk; Generalized linear mixed model; State space model

Indexed keywords

EID: 33847255363     PISSN: 09275398     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jempfin.2006.05.002     Document Type: Article

References (34)

1. Bangia A., Diebold F., Kronimus A., Schagen C., and Schuermann T. Ratings migration and the business cycle, with application to credit portfolio stress testing. J. Bank. Finance 26 (2002) 445-474
2. Basel Committee on Banking Supervision (2002, October). Quantitative impact, Study 3, Technical guidance. Bank of International Settlements.
3. Breslow N., and Clayton D. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88 (1993) 9-25
4. Brockwell P., and Davis R. Time Series: Theory and Methods. 2nd ed. (1991), Springer, New York
5. Clayton D. Generalized linear mixed models. In: Gilks W., Richardson S., and Spiegelhalter D. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall, London 275-301
6. +, a Credit Risk Management Framework. Technical report (1997)
7. Crowder M., Davis M., and Giampieri G. Analysis of default data using hidden Markov models. Quantit. Finance 5 1 (2005) 27-34
8. Egloff, D., Leippold, M., Vanini, P., 2004. A simple model of credit contagion. Preprint, Swiss Banking Institute, University of Zurich.
9. Fahrmeir L., and Tutz G. Multivariate Statistical Modelling Based On Generalized Linear Models (1994), Springer, New York
10. Frey R., and McNeil A. Dependent defaults in models of portfolio credit risk. J. Risk 6 1 (2003) 59-92
11. Gagliardini P., and Gouriéroux C. Stochastic migration models with application to corporate risk. J. Financ. Econ. 3 2 (2005) 188-226
12. Gelfand A. Model determination using sampling-based methods. In: Gilks W., Richardson S., and Spiegelhalter D. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall, London 145-161
13. Gelfand A., Hills S., Racine-Poon A., and Smith A. Illustration of Bayesian inference in normal data models using Gibbs sampling. J. Am. Stat. Assoc. 85 (1990) 972-985
14. Gilks W. Derivative-free adaptive rejection sampling for Gibbs sampling. In: Bernardo J., Berger J., Dawid A., and Smith A. (Eds). Bayesian Statistics vol. 4 (1992), Oxford University Press, Oxford 641-649
15. Gilks W. Full conditional distributions. In: Gilks W., Richardson S., and Spiegelhalter D. (Eds). Markov Chain Monte Carlo in Practice (1996), Chapman & Hall, London 75-88
16. Gordy M., and Heitfield E. Estimating default correlations from short panels of credit rating performance data. Technical Report (2002), Federal Reserve Board
17. Gouriéroux C., and Monfort A. Simulation-Based Econometric Methods. CORE Lecture Series (1996), Oxford University Press, New York
18. Joe H. Multivariate Models and Dependence Concepts (1997), Chapman & Hall, London
19. Kealhofer S., and Bohn J.R. Portfolio management of default risk. Technical Report (2001), KMV
20. Koopman S., Lucas A., and Daniels R. A non-Gaussian panel time series model for estimating and decomposing default risk. Working Paper (2005), Tinbergen Institute
21. Koopman, S., Lucas, A., Klaassen, P., 2005. Empirical credit cycles and capital buffer formation. J. Bank. Financ. 29 (12), 3159-3179.
22. Kurbat M., and Korablev I. Methodology for testing the level of the EDF™ credit measure. Technical Report, No. 020729 (2002), Moody's KMV
23. Lopez J. The empirical relationship between average asset correlation, firm probability of default, and asset size. J. Financ. Intermed. 13 (2004) 265-283
24. MacDonald I., and Zucchini W. Hidden Markov Models and Other Models for Discrete-valued Time Series (1997), Chapman & Hall, London
25. McCullagh P., and Nelder J. Generalized Linear Models. 2nd ed. (1989), Chapman & Hall, London
26. McCulloch C. Maximum likelihood algorithms for generalized linear mixed models. J. Am. Stat. Assoc. 92 (1997) 162-170
27. Merton R. On the pricing of corporate debt: the risk structure of interest rates. J. Finance 29 (1974) 449-470
28. Myles J., and Clayton D. GLMMGibbs: An R Package for Estimating Bayesian Generalised Linear Mixed Models by Gibbs Sampling (2001). http:////www.maths.lth.se/help/R/.R/doc/html/packages.html http://www.maths.lth.se/help/R/.R/doc/html/packages.html
29. Nickell P., Perraudin W., and Varotto S. Stability of rating transitions. J. Bank. Finance 24 (2000) 203-227
30. Robert C., and Casella G. Monte Carlo Statistical Methods (1999), Springer, New York
31. Rösch D. An empirical comparison of default risk forecasts from alternative credit rating philosophies. Int. J. Forecast. 21 (2005) 37-51
32. Skrondal A., and Rabe-Hesketh S. Generalized Latent Variable Modeling (2004), Chapman & Hall (CRC), Boca Raton
33. Wendin, J., 2006. Bayesian Methods in Portfolio Credit Risk Management. Ph.D. thesis, Department of Mathematics, ETH Zürich.
34. Wilson T. Portfolio credit risk I and II. Risk 10 (1997) 9-10