Abstract
The problem of the asymmetric behaviour and fat tails of portfolios of credit risky corporate assets such as bonds has become very important, not only because of the impact of both defaults end migration from one rating class to another. This paper discusses the use of different copulas for credit risk management. Usual Monte Carlo (MC) techniques are compared with a variable reduction method i.e. Importance Sampling (IS) in order to reduce the variability of the estimators of the tails of the Profit & Loss distribution of a portfolio of bonds. This provides speed up for computing economic capital in the rare event quantile of the loss distribution that must be held in reserve by a lending institution for solvency. An application to a simulated portfolio of bonds ends the paper.
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Schoier, G., Marsich, F. (2013). Importance Sampling: A Variance Reduction Method for Credit Risk Models. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_38
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DOI: https://doi.org/10.1007/978-3-319-00032-9_38
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