We consider the problem of accurately measuring the credit risk of a portfolio consisting of loss exposures such as loans, bonds and other financial assets. We are particularly interested in the probability of large portfolio losses. We describe the popular models in the credit risk framework including factor models and copula models. To this end, we revisit the most efficient probability estimation algorithms within current copula credit risk literature, namely importance sampling. We illustrate the workings and developments of these algorithms for large portfolio loss probability estimation and quantile estimation. We then propose a modification to the dynamic splitting method which allows application to the credit risk models described. Our proposed algorithm for the unbiased estimation of rare-event probabilities, exploits the quasi-monotonic property of functions to embed a static simulation problem within a time-dependent Markov process. A study of our proposed algorithm is then conducted through numerical experiments with its performance benchmarked against current popular importance sampling algorithms.
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