Importance sampling with splitting for portfolio credit risk

Jinyoung Kim, Sunggon Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We consider a credit portfolio with highly skewed exposures. In the portfolio, small number of obligors have very high exposures compared to the others. For the Bernoulli mixture model with highly skewed exposures, we propose a new importance sampling scheme to estimate the tail loss probability over a threshold and the corresponding expected shortfall. We stratify the sample space of the default events into two subsets. One consists of the events that the obligors with heavy exposures default simultaneously. We expect that typical tail loss events belong to the set. In our proposed scheme, the tail loss probability and the expected shortfall corresponding to this type of events are estimated by a conditional Monte Carlo, which results in variance reduction. We analyze the properties of the proposed scheme mathematically. In numerical study, the performance of the proposed scheme is compared with an existing importance sampling method.

Original languageEnglish
Pages (from-to)327-347
Number of pages21
JournalCommunications for Statistical Applications and Methods
Issue number3
StatePublished - 2020


  • Bernoulli mixture model
  • Credit portfolio
  • Importance sampling
  • Skewed exposures


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