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Is Firm Interdependence within Industries Important for Portfolio Credit Risk?

Author

Listed:
  • Carling, Kenneth

    (IFAU, Uppsala and Dalarna University)

  • Rönnegård, Lars

    (Dalarna University)

  • Roszbach, Kasper

    (Research Department, Central Bank of Sweden)

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, thesemodels 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 allowing for dependency between firm default risk through both common factors and industry specific errors in a duration model. An application using pooled data from two Swedish banks’ business loan portfolios over the period 1996-2000 shows that estimates of individual default risk are little affected by including industry specific errors. However, accounting for these industry effects increases VaR estimates by 50-200 percent. A traditional model with only systematic factors, 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 we propose manages to follow both the trend in credit losses and produce industry driven, time-varying, fluctuations in losses around that trend. Consequently, this model will better aid banks and regulators in determining the appropriate size of economic capital requirements. Capital buffers derived from our model will be larger for periods with large ”aggregate” disturbances and smaller in better times, and avoid both overcapitalization in good times and undercapitalization in bad times.

Suggested Citation

  • Carling, Kenneth & Rönnegård, Lars & Roszbach, Kasper, 2004. "Is Firm Interdependence within Industries Important for Portfolio Credit Risk?," Working Paper Series 168, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0168
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    References listed on IDEAS

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    Cited by:

    1. Collet, Jerome & Ielpo, Florian, 2018. "Sector spillovers in credit markets," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 267-278.
    2. Barro, Diana & Basso, Antonella, 2010. "Credit contagion in a network of firms with spatial interaction," European Journal of Operational Research, Elsevier, vol. 205(2), pages 459-468, September.
    3. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2013. "Firm Default And Aggregate Fluctuations," Journal of the European Economic Association, European Economic Association, vol. 11(4), pages 945-972, August.
    4. Egloff, Daniel & Leippold, Markus & Vanini, Paolo, 2007. "A simple model of credit contagion," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2475-2492, August.
    5. Diana Barro & Antonella Basso, 2006. "A credit contagion model for loan portfolios in a network of firms with spatial interaction," Working Papers 143, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    6. Tor Jacobson & Jesper Lindé & Kasper Roszbach, 2005. "Credit Risk Versus Capital Requirements under Basel II: Are SME Loans and Retail Credit Really Different?," Journal of Financial Services Research, Springer;Western Finance Association, vol. 28(1), pages 43-75, October.
    7. Carling, Kenneth & Alam, Moudud, 2007. "Computationally feasible estimation of the covariance structure in Generalized linear mixed models(GLMM)," Working Papers 2007:14, Örebro University, School of Business.

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    More about this item

    Keywords

    value-at-risk; credit risk; portfolio credit risk; duration model; default correlation; industry dependency; cluster errors;
    All these keywords.

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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