Modelling default contagion using multivariate phase-type distributions
- 259 Downloads
- 8 Citations
Abstract
We model dynamic credit portfolio dependence by using default contagion in an intensity-based framework. Two different portfolios (with ten obligors), one in the European auto sector, the other in the European financial sector, are calibrated against their market CDS spreads and the corresponding CDS-correlations. After the calibration, which are perfect for the banking portfolio, and good for the auto case, we study several quantities of importance in active credit portfolio management. For example, implied multivariate default and survival distributions, multivariate conditional survival distributions, implied default correlations, expected default times and expected ordered default times. The default contagion is modelled by letting individual intensities jump when other defaults occur, but be constant between defaults. This model is translated into a Markov jump process, a so called multivariate phase-type distribution, which represents the default status in the credit portfolio. Matrix-analytic methods are then used to derive expressions for the quantities studied in the calibrated portfolios.
Keywords
Portfolio credit risk Intensity-based models Dynamic dependence modelling CDS-correlation Default contagion Markov jump processes Multivariate phase-type distributions Matrix-analytic methodsJEL Classification
Primary G33 G13 Secondary C02 C63 G32Preview
Unable to display preview. Download preview PDF.
References
- Asmussen S. (2000) Matrix-analytic models and their analysis. Scandinavian Journal of Statistics 27: 193–226CrossRefGoogle Scholar
- Asmussen S. (2003) Applied probability and queues. 2nd edn. Springer, LondonGoogle Scholar
- Assaf D., Langbert N. A., Savis T. H., Shaked M. (1984) Multivariate phase-type distributions. Operations Research 32: 688–701CrossRefGoogle Scholar
- Avellaneda M., Wu L. (2001) Credit contagion: Pricing cross country risk in the brady debt markets. International Journal of Theoretical and Applied Finance 4: 921–939CrossRefGoogle Scholar
- Bielecki, T. R., Crépey, S., Jeanblanc, M., & Rutkowski, M. (2005). Valuation of basket credit derivatives in the credit migrations enviroment. Working paper.Google Scholar
- Bielecki T. R., Rutkowski M. (2001) Credit risk: Modeling, valuation and hedging. Springer, BerlinGoogle Scholar
- Bielecki, T. R., Vidozzi, A., & Vidozzi, L. (2006). An efficent approach to valuation of credit basket products and rating triggered step-up bonds. Working paper.Google Scholar
- Collin-Dufresne P., Goldstein R. S., Hugonnier J. (2004) A general formula for valuing defaultable securities. Econometrica 72: 1377–1407CrossRefGoogle Scholar
- Davis M., Esparragoza J.C. (2007) Large portfolio credit risk modelling. International Journal of Theoretical and applied Finance 10(4): 653–678CrossRefGoogle Scholar
- Davis M., Lo V. (2001a) Infectious defaults. Quantitative Finance 1: 382–387CrossRefGoogle Scholar
- Davis M., Lo V. (2001b) Modelling default correlation in bond portfolios. In: Alexander C. (eds) Mastering risk volume 2: Applications. Financial Times Prentice Hall, Englewood Cliffs, pp 141–151Google Scholar
- Frey, R., & Backhaus, J. (2004). Portfolio credit risk models with interacting default intensities: A Markovian approach. Working paper. Department of Mathematics, University of Leipzig.Google Scholar
- Frey, R., & Backhaus, J. (2006). Credit derivatives in models with interacting default intensities: A Markovian approach. Working paper. Department of Mathematics, University of Leipzig.Google Scholar
- Giesecke K., Weber S. (2004) Cyclical correlation, credit contagion and portfolio losses. Journal of Banking and Finance 28: 3009–3036CrossRefGoogle Scholar
- Giesecke K., Weber S. (2006) Credit contagion and aggregate losses. Journal of Economic Dynamics and Control 30(5): 741–767CrossRefGoogle Scholar
- Gross D., Miller D. R. (1984) The randomization technique as a modelling tool and solution procedure for transient markov processes. Operations Research 32(2): 343–361CrossRefGoogle Scholar
- Herbertsson, A. (2005). Dynamic dependence modelling in credit risk. Licentiate thesis. Department of Mathematics, Chalmers University of Technology. Defended 2005-05-11. Opponent: Prof.Dr Rüdiger Frey, Universität Leipzig.Google Scholar
- Herbertsson A. (2008) Pricing synthetic CDO tranches in a model with default contagion using the matrix-analytic approach. The Journal of Credit Risk 4(4): 3–35Google Scholar
- Herbertsson A., Rootzén H. (2008) Pricing kth-to-default swaps under default contagion: The matrix-analytic approach. The Journal of Computational Finance 12(1): 49–78Google Scholar
- Heyman D. P., Sobel M. J. (1982) Stochastic models in operations research. McGraw-Hill, New YorkGoogle Scholar
- Jarrow R. A., Yu F. (2001) Counterparty risk and the pricing of defaultable securities. Journal of Finance 16: 1765–1800CrossRefGoogle Scholar
- Kraft H., Steffensen M. (2007) Bankruptcy, counterparty risk and contagion. Review of Finance 11(2): 209–252CrossRefGoogle Scholar
- Lando D. (2004) Credit risk modeling: Theory and applications. Princeton University Press, PrincetonGoogle Scholar
- McNeil A. J., Frey R., Embrechts P. (2005) Quantitative risk management. Princeton University Press, OxfordGoogle Scholar
- Rogge, E., & Schönbucher, P. J. (2003). Modelling dynamic portfolio credit risk. Working paper.Google Scholar
- Schönbucher, P. J., & Schubert, D. (2001). Copula-dependent default risk in intensity models. Working paper. Department of Statistics, Bonn University.Google Scholar
- Sidje R. B., Stewart W. J. (1999) A numerical study of large sparse matrix exponentials arising in Markov chains. Computational Statistics and Data Analysis 29(3): 345–368CrossRefGoogle Scholar
- Yu F. (2007) Correlated defaults in intensity-based models. Mathematical Finance 17(2): 155–173CrossRefGoogle Scholar