Efficient Importance Sampling for Reduced Form Models in Credit Risk

Author

Bassamboo, Achal ; Jain, Sachin

Author_Institution

Kellogg Sch. of Manage., Northwestern Univ., Evanston, IL

fYear

2006

fDate

3-6 Dec. 2006

Firstpage

741

Lastpage

748

Abstract

In this paper we study the problem of estimating probability of large losses in the framework of doubly stochastic credit risk models. We derive a logarithmic asymptote for the probability of interest in a specific asymptotic regime and propose an asymptotically optimal importance sampling algorithm for efficiently estimating the same. The numerical results corroborate our theoretical findings

Keywords

estimation theory; finance; importance sampling; probability; reduced order systems; risk management; stochastic processes; asymptotically optimal importance sampling algorithm; doubly stochastic credit risk models; large losses; logarithmic asymptotes; probability estimation; Computational modeling; Discrete event simulation; Distributed computing; Footwear industry; Monte Carlo methods; Portfolios; Pricing; Risk management; Stochastic processes; Surges;

fLanguage

English

Publisher

ieee

Conference_Titel

Simulation Conference, 2006. WSC 06. Proceedings of the Winter

Conference_Location

Monterey, CA

Print_ISBN

1-4244-0500-9

Electronic_ISBN

1-4244-0501-7

Type

conf

DOI

10.1109/WSC.2006.323154

Filename

4117678

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1804305