Approximations and control variates for pricing portfolio credit derivatives

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Approximations and control variates for pricing portfolio credit derivatives

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Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents

Washington D.C.

SESSION: Risk analysis: credit risk table of contents

Pages 976-983

Year of Publication: 2007

ISBN:1-4244-1306-0

Authors

Zhiyong Chen	Bear, Stearns & Co., Inc., New York, NY
Paul Glasserman	Columbia University, New York, NY

Sponsors

INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society

Publisher

IEEE Press Piscataway, NJ, USA

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Downloads (6 Weeks): 7, Downloads (12 Months): 22, Citation Count: 0

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abstract references collaborative colleagues

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ABSTRACT

Portfolio credit derivatives that depend on default correlation are increasingly widespread in the credit market. Valuing such products often entails Monte Carlo simulation. However, for large portfolios, plain Monte Carlo simulation can be slow. In this paper, we develop approximation methods for pricing collateralized debt obligation (CDO) tranches in the widely used factor copula approach. We also discuss using the approximations as control variates to improve the precision of Monte Carlo estimates. These approximation methods and control variate techniques could be applied to pricing other portfolio credit derivatives as well.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Andersen, L., J. Sidenius, and S. Basu. 2003. All your hedges in one basket. Risk 16:67--72.
	2	Bruyere, R., R. Cont, R. Copinot, L. Fery, C. Jaecl, T. Spitz, and G. Smart. 2006. Credit Derivatives and Structured Credit: A Guide for Investors. Chichester, England: Wiley.
	3	Chen, Z., and P. Glasserman. 2006. Fast pricing of basket default swaps. To appear in Operations Research.
	4	Duffie, D., and K. Singleton. 2003. Credit Risk: Pricing, Measurement, and Management. Princeton, New Jersey: Princeton University Press.
	5	Gupton, G., C. Finger, and M. Bhatia. 1997. Creditmetrics Technical Document. Technical report, J. P. Morgan & Co., New York.
	6	Hull, J., and A. White. 2004. Valuation of a CDO and an nth to default CDS without Monte Carlo simulation. Journal of Derivatives 12, 2.
	7	Li, D. 2000. On default correlation: A copula function approach. Journal of Fixed Income 9:43--54.
	8	Schönbucher, P. 2003. Credit Derivatives Pricing Models: Model, Pricing and Implementation. Princeton, New Jersey: Princeton University Press.

Collaborative Colleagues:

Zhiyong Chen: colleagues

Paul Glasserman: colleagues

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