Efficient Computation of Portfolio Credit Risk with Chain Default

Yuki Ikeda

MPRA Paper from University Library of Munich, Germany

Abstract: Many banks consider the chain default or bankruptcy when they compute the credit loss distribution. One way to consider the chain default is the good-old Monte Carlo simulation, however, it is typically time-consuming. In this paper, we extend the efficient Monte Carlo simulation using the importance sampling introduced by Glasserman and Li (2005) to realize an efficient Monte Carlo simulation of the Value at Risk (VaR) that allows the chain defaults. In addition, we see that another method, the saddle point approximation, can also be modified for the case of the chain defaults. Moreover, we give a simple method of shifting the means of the multivariate factors using the well-known EM-algorithm to further reduce the variance of the simulated VaR. Simulation studies show that these proposed methods have superior numerical performance.

Keywords: Value-at-risk; Risk contributions; Importance sampling; Saddle point approximation; EM-algorithm (search for similar items in EconPapers)
JEL-codes: C58 C63 (search for similar items in EconPapers)
Date: 2021-03-16
New Economics Papers: this item is included in nep-ban, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:106652

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