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The Role of Industry, Geography and Firm Heterogeneity in Credit Risk Diversification

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  • M. Hashem Pesaran
  • Til Schuermann
  • Björn-Jakob Treutler

Abstract

In theory the potential for credit risk diversifcation for banks could be substantial. Portfolios are large enough that idiosyncratic risk is diversifed away leaving exposure to systematic risk. The potential for portfolio diversifcation is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. We propose a model for exploring these dimensions of credit risk diversifcation: across industry sectors and across di¤erent countries or regions. We find that full firm-level parameter heterogeneity matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity greatly reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.

Suggested Citation

  • M. Hashem Pesaran & Til Schuermann & Björn-Jakob Treutler, 2005. "The Role of Industry, Geography and Firm Heterogeneity in Credit Risk Diversification," IEPR Working Papers 05.25, Institute of Economic Policy Research (IEPR).
    • Handle: RePEc:scp:wpaper:05-25
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    References listed on IDEAS

    as
    1. Jeffery D. Amato & Eli M Remolona, 2005. "The pricing of unexpected credit losses," BIS Working Papers 190, Bank for International Settlements.
    2. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
    3. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    4. Amato, Jeffery D. & Furfine, Craig H., 2004. "Are credit ratings procyclical?," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2641-2677, November.
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    Citations

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    Cited by:

    1. Kapetanios, G. & Pesaran, M.H., 2005. "Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns," Cambridge Working Papers in Economics 0520, Faculty of Economics, University of Cambridge.
    2. Klomp, Jeroen, 2013. "Government interventions and default risk: Does one size fit all?," Journal of Financial Stability, Elsevier, vol. 9(4), pages 641-653.
    3. Klaus Düllmann & Nancy Masschelein, 2006. "Sector Concentration in Loan Portfolios and Economic Capital," Working Paper Research 105, National Bank of Belgium.
    4. Masschelein, Nancy & Düllmann, Klaus, 2006. "Sector concentration in loan portfolios and economic capital," Discussion Paper Series 2: Banking and Financial Studies 2006,09, Deutsche Bundesbank.

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    More about this item

    Keywords

    Risk management; default dependence; economic interlinkages; portfolio choice;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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