Quantifying credit portfolio losses under multi-factor models

Genmma Colldeforns-Papiol, Luis Ortiz-Gracia, Cornelis W. Oosterlee

Research output: Contribution to journalArticleScientificpeer-review


In this work, we investigate the challenging problem of estimating credit risk measures of portfolios with exposure concentration under the multi-factor Gaussian and multi-factor t-copula models. It is well-known that Monte Carlo (MC) methods are highly demanding from the computational point of view in the aforementioned situations. We present efficient and robust numerical techniques based on the Haar wavelets theory for recovering the cumulative distribution function of the loss variable from its characteristic function. To the best of our knowledge, this is the first time that multi-factor t-copula models are considered outside the MC framework. The analysis of the approximation error and the results obtained in the numerical experiments section show a reliable and useful machinery for credit risk capital measurement purposes in line with Pillar II of the Basel Accords.

Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalInternational Journal of Computer Mathematics
Publication statusE-pub ahead of print - 2018


  • Credit risk
  • expected shortfall
  • Fourier transform inversion
  • Gaussian copula
  • Haar wavelets
  • multi-factor models
  • t-copula
  • Value-at-Risk


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