Pricing Credit Derivatives and Measuring Credit Risk in Multifactor Models
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Date: 03-22-2007
Start Time:
5:30pm
End Time: 7:00pm
Speaker: Paul Glasserman, Columbia Business School
Location: Dahesh Auditorium, 580 Madison Ave (Madison & 56th street)
ABSTRACT
The Gaussian copula remains a standard model for pricing multi-name credit derivatives and measuring portfolio credit risk. In practice, the model is most widely used in its single-factor form, though this model is too simplistic to match the pattern of implied correlations observed in market prices of CDOs, and too simplistic for credible risk measurement. We discuss the use of multifactor versions of the model. An obstacle to using a multifactor model is the efficient calculation of the loss distribution. We develop two fast and accurate approximations for this problem. The first method is a correlation expansion technique that approximates multifactor models in powers of a parameter that scales correlation; this reduces pricing to more tractable single-factor models. The second method approximates the characteristic function of the loss distribution in a multifactor model and applies numerical transform inversion. We analyze the errors in both methods and illustrate their performance numerically. This talk is based on joint work with Sira Suchintabandid.
BIO
Paul Glasserman is Jack R. Anderson Professor of Business at Columbia University. His research and teaching address risk management, the pricing of derivative securities and Monte Carlo simulation in financial engineering. Prior to joining Columbia, he worked at Bell Laboratories and was a visiting professor at Princeton University. He is author of "Monte Carlo Methods in Financial Engineering" (Springer, 2004), which received the 2005 Outstanding Simulation Publication Award from INFORMS, a member of the Education and Standards Committeeof PRMIA, the Professional Risk Managers International Association and has served as a consultant to industrial corporations, management consulting firms and financial firms.