Importance Sampling Estimation of Joint Default Probability under Structural-Form Models with Stochastic Correlation

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 23)

Abstract

This paper aims to estimate joint default probabilities under the structural-form model with a random environment; namely stochastic correlation. By means of a singular perturbation method, we obtain an asymptotic expansion of a two-name joint default probability under a fast mean-reverting stochastic correlation model. The leading order term in the expansion is a joint default probability with an effective constant correlation. Then we incorporate an efficient importance sampling method used to solve a first passage time problem. This procedure constitutes a homogenized importance sampling to solve the full problem of estimating the joint default probability with stochastic correlation models.

Keywords

Importance Sampling Random Environment Default Probability Credit Derivative Constant Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Work Supported by NSC-99-2115-M007-006-MY2 and TIMS at National Taiwan University. We are grateful to an anonymous referee for helpful comments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Quantitative FinanceNational Tsing Hua UniversityHsinchuTaiwan (ROC)

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