| Reference: | Basel Committee on Banking Supervision. Basel II: international convergence of capital measurement and capital standards: A revised framework, Consultative Document, Bank for International Settlements, 2004.
Basel Committee on Banking Supervision. Basel II: International convergence of capital measurement and capital standards: A revised framework-comprehensive version, Consultative Document, Bank for International Settlements, 2006.
Bluhm, C., Overbeck, L., and Wagner, C., An Introduction to Credit Risk Modeling, Chapman & Hall, New York, 2002.
Booth, J. G. and Hobert, J. P., Maximizing generalized linear mixed models likelihoods with an automated Monte Carlo EM algorithm, Journal of the Royal Statistical Society Series B, Vol.61, No.1, pp.265-285, 1999.
Carter, C. K. and Kohn, R., On Gibbs sampling for state space models, Biometrika, Vol. 81. No.3, pp.541-553, 1994.
Crouhy, M. G., Jarrow, R. A. and Turnbull, S. M., The subprime credit crisis of 2007, The Journal of Derivatives, Vol.16, No.1, pp.81-110, 2008.
Czado, C. and Pflűger, C., Modeling dependencies between rating categories and their effects on prediction in a credit risk portfolio, Applied Stochastic Models in Business and Industry, Vol.24, No.3, pp.237-259, 2008.
de Jong, P. and Shephard, N., The simulation smoother for time series models, Biometrika, Vol.82, No.2, pp.339-350, 1995.
Dagpunar, J. S., Simulation and Monte Carlo - with Applications in Finance and MCMC, Wiley, New York, 2007.
Dempster, A. P., Laird, N. M., and Rubin, D., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society B, Vol.39, pp.1-38, 1997.
Dwyer, D. W., The distribution of defaults and Bayesian model validation, Journal of Risk Model Validation, Vol.1, No.1, pp.23-53, 2007.
Durbin, J. and Koopman, S. J., Monte Carlo maximum likelihood estimation for non-Gaussian state space models, Biometrika, Vol.84, No.3, pp.669-684, 1997.
Durbin, J. and Koopman, S. J., A simple and efficient simulation smoother for state-space time series models, Biometrika, Vol.89, No.3, pp.603-616, 2002.
Ebnöther, S. and Vanini, P., Credit portfolios: What defines risk horizons and risk measurement?, Journal of Banking & Finance, Vol.31, No.12, pp.3663-3679, 2007.
Fruhwirth-Schnatter, S., Data augmentation and dynamic linear models, Journal of Time Series Analysis, Vol.15, No.2, pp.183-202, 1994.
Gelfand, A. E. and Smith, A. F. M, Sampling based approaches to calculate marginal densities, Journal of American Statistical Association, Vol.85, pp.398-409,1990.
Gelfand, A., Model determination using sampling-based methods, in: W. Gilks, S. Richardson, and D. Spiegelhalter, (eds.), Markov Chain Monte Carlo in Practice, Chapman & Hall, London, pp.145-161, 1996.
Geweke, J., Monte carlo simulation and numerical integration, in: H. M. Amman, D. A. Kendrick, and J. Rust, (eds.), Handbook of Computational Economics, North-Holland, Amsterdam, pp.731-800, 1996.
Glasserman, P. and Li, J., Importance sampling for portfolio credit risk. Management Science, Vol.51, No.11, pp.1643-1656, 2005.
Gordy, M. and Heitfield, E., Estimating default correlation from short panels of credit rating, Working Paper, Federal Reserve Board, 2002.
Gordy, M. B., A risk-factor model foundation for ratings-based capital rules, Journal of Finanial Intermediation, Vol.12, No.3, pp.199-232, 2003.
Gössl, M., Predictions based on certain uncertainties - a Bayesian credit portfolio approach, Disscuss Paper, HypoVereinsbank, 2005.
Greenberg, E., Introduction to Bayesian Econometrics, Cambridge University Press, New York, 2007.
Gupton, G., Finger, C. and Bhatia, M., CreditMetricsTM, technical document, CreditMetrics, 1997.
Hanson, S. and Schuermann, T., Confidence intervals for probabilities of default, Journal of Banking & Finance, Vol.30, No.8, pp.2281-2301, 2006.
Kiefer, N. M., The probability approach to default probabilities, Risk, Vol.20, No.7, pp.146-150, 2007.
Kiefer, N. M., Default estimation for low--default portfolios, Journal of Empirical Finance, Vol.16, No.1, pp.164-173, 2009.
Kiefer, N. M., Default estimation and expert information, Journal of Business and Economic Statistics, Vol.28, No.2, pp.320-328, 2010.
Kiefer, N. M., Default estimation, correlated defaults, and expert information, Journal of Applied Econometrics, Vol.26, No.2, pp.173-192, 2011.
Kitagawa, G, Monte Carlo filter and smoother for non-Gaussian nonlinear state space model, Journal of Computational and Graphical Statistics, Vol.5, pp.1-25, 1996.
Kitagawa, G, A self-organizing state-space model, Journal of the American Statistical Association, Vol.93, pp.1203-1215, 1998.
Koopman, S. J. and Lucas, A., A non-Gaussian panel time series model for estimating and decomposing default risk, Journal of Business & Economic Statistics, Vol.26, pp.510-525, 2008.
McNeil, A. J. and Wendin, J. P., Bayesian inferences for generalized linear mixed models of portfolio credit risk, Journal of Empirical Finance, Vol.14, No.2, pp.131-149, 2007.
Moody’s, Moody’s: Global default rate on the rise, Announcement.
Rachev, S. T., Hsu, J. S. J., Bagasheva, B. S. and Fabozzi, F. J., Bayesian Methods in Finance, Wiley, New York, 2008.
Robert, C. and Casella, G., Monte Carlo Statistical Models, Springer, New York, 2004.
Rösch, D, An empirical comparison of default risk forecasts from alternative credit rating philosophies, International Journal of Forecasting, Vol.21, pp.37-51, 2005.
Schönbucher, P., Factor models: Portfolio credit risks when defaults are correlated, Journal of Risk Finance, Vol.3, No.1, pp.45-56, 2001.
Shephard, N., Partial non-Gaussian state space, Biometrika, Vol.81, pp.115-132, 1994.
Shephard, N. and Pitt, M. K., Likelihood analysis of non-Gaussian measurement time series, Biometrika, Vol.84, pp.653-667, 1997.
Song, P. X.-K., Correlated Data Analysis: Modeling, Analytics, and Applications, Springer, New York, 2007.
Soros, G., The New Paradigm for Financial Markets: The Credit Crisis of 2008 and What it Means, PublicAffairs, New York, 2008.
Standard & Poor`s, Default, Transition, and Recovery: 2009 Annual Global Corporate Default Study and Rating Transitions, Technical Report, Global Fixed Income Research, 2009.
Vasicek, O., Loan portfolio value, Risk, Vol.15, No.12, pp.160-162, 2002.
Wei, G. C. G. and Tanner, M. A., A Monte Carlo implementation of the EM algorithm and the poor man`s data augmentation algorithms, Journal of American Statistical Association, Vol.85, pp.699-704, 1990.
Wu, L., Non-linear mixed-effect models with non-ignorably missing covariates, The Canadian Journal of Statistics, Vol.32, No.1, pp.27-37, 2004. |
