CDO pricing with nested Archimedean copulas
Abstract
Companies in the same industry sector are usually more correlated than firms in different sectors, as they are similarly affected by macroeconomic effects, political decisions, and consumer trends. Despite the many stock return models taking this fact into account, there are only a few credit default models that take it into consideration. In this paper we present a default model based on nested Archimedean copulas that is able to capture hierarchical dependence structures among the obligors in a credit portfolio. Nested Archimedean copulas have a surprisingly simple and intuitive interpretation. The dependence among all companies in the same sector is described by an inner copula and the sectors are then coupled via an outer copula. Consequently, our model implies a larger default correlation for companies in the same industry sector than for companies in different sectors. A calibration to CDO tranche spreads of the European iTraxx portfolio is performed to demonstrate the fitting capability of the model. This portfolio consists of CDS on 125 companies from six different industry sectors and is therefore an excellent portfolio for a comparison of our generalized model with a traditional copula model of the same family that does not take different sectors into account.Download Info
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Bibliographic Info
Article provided by Taylor and Francis Journals in its journal Quantitative Finance.
Volume (Year): 11 (2011)
Issue (Month): 5 ()
Pages: 775-787
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Handle: RePEc:taf:quantf:v:11:y:2011:i:5:p:775-787
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For corrections or technical questions regarding this item, or to correct its listing, contact: (Michael McNulty).
Related research
Keywords: Nested Archimedean copula; Hierarchial dependence structure; CDO; Monte-Carlo pricing;References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Paul Embrechts & Marius Hofert, 2011. "Comments on: Inference in multivariate Archimedean copula models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 20(2), pages 263-270, August.
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