Global Sensitivity Analysis for Latent Factor Credit Risk Models
This paper proposes the use of global sensitivity analysis to evaluate the risk associated with a credit portfolio model. The main features of this approach are its ability to assess the relative importance of the input factors and to reveal interactions among them. The commonly used one-at-a-time sensitivity analysis cannot provide this information. We analyze the static and time-varying uncertainties of three key input factors in a latent factor credit risk model: the multivariate distribution (copula) of the latent variables, the correlation, and the default probabilities of the obligors. Results show that the relative importance of the factors strongly depends on the average default probability of the portfolio and the analyzed quantiles of the default distribution.
BAUR Dirk;
CARIBONI Jessica;
CAMPOLONGO Francesca;
2009-01-26
Inderscience
JRC31312
1466-8297,
https://publications.jrc.ec.europa.eu/repository/handle/JRC31312,
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