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In Rememberance: World Trade Center (WTC)

Is Firm Interdependence within Industries Important for Portfolio Credit Risk?

by Kenneth Carling of IFAU, Uppsala, Sweden, & Dalarna University,
Lars Rönnegård of Uppsala University, and
Kasper Roszbach of Sveriges Riksbank

January 22, 2007

Abstract: A drawback of available portfolio credit risk models is that they fail to allow for default risk dependency across loans other than through common risk factors. Thereby, these models ignore that close ties can exist between companies due to legal, financial and business relations. In this paper, we integrate the insights from theoretical models of default correlation into a commonly used model of default and portfolio credit risk by explicitly allowing for dependencies between firm defaults through both common factors and industry specific disturbances in a duration model. An application using pooled data from two Swedish banks' business loan portfolios over the period 1994-2000 shows that estimates of individual default risk are little affected by including industry specific errors. However, accounting for the within-industry correlation of defaults increases estimates of VaR by 50-200 percent. We show that the model we propose manages to follow both the trend in credit losses and produce industry driven, time-varying, fluctuations in losses around that trend. A conventional model that contains only systematic factors as drivers of default correlation, although able to fit the broad trends in credit losses, cannot match these fluctuations because it fails to capture credit losses in bad times, when banks are typically hit by large unexpected credit losses. The model developed here should thus aid banks and supervisors in determining the appropriate size of economic capital requirements. Our estimations show that it is likely that banks need larger capital buffers than conventional models indicate.

JEL Classification: C34, C35, D61, D81, G21.

Keywords: correlation, default, value-at-risk, credit risk, portfolio credit risk, duration model, industry dependency, cluster.

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