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A credit contagion model for the dynamics of the rating transitions in a SME bank loan portfolio

Author

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  • Antonella Basso

    () (Department of Applied Mathematics, University of Venice)

  • Riccardo Gusso

    () (Department of Applied Mathematics, University of Venice)

Abstract

In this work we analyze the effects of credit contagion on the credit quality of a portfolio of bank loans issued to SMEs. To this aim we start from the discrete time model proposed in Barro and Basso (2005), that considers the counterparty risk generated by the business relations in a network of firms, and we modify it by introducing different rating classes in order to manage the case of firms with different credit qualities. The transitions from a rating class to another occurs when a proxy for the asset value of the firm crosses some rating specific thresholds. We assume that the initial rating transition matrix of the system is known, and compute the thresholds using the probability distribution of the steady state of the model. A wide Monte Carlo simulation analysis is carried out in order to study the dynamic behaviour of the model, and in particular to analyze how the default contagion present in the model affects the output rating transition matrix of the portfolio.

Suggested Citation

  • Antonella Basso & Riccardo Gusso, 2008. "A credit contagion model for the dynamics of the rating transitions in a SME bank loan portfolio," Working Papers 162, Department of Applied Mathematics, Università Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpaper:162
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    References listed on IDEAS

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    1. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
    2. 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.
    3. Neu, Peter & Kühn, Reimer, 2004. "Credit risk enhancement in a network of interdependent firms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 639-655.
    4. Robert A. Jarrow & Fan Yu, 2008. "Counterparty Risk and the Pricing of Defaultable Securities," World Scientific Book Chapters,in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 20, pages 481-515 World Scientific Publishing Co. Pte. Ltd..
    5. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
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    More about this item

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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