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Modeling the distribution of credit losses with observable and latent factors

Author

Listed:
  • Gabriel Jiménez

    () (Banco de España)

  • Javier Mencía

    () (Banco de España)

Abstract

This paper develops a flexible and computationally efficient model to estimate the credit loss distribution of the loans in a banking system. We consider a sectorial structure, where default frequencies and the total number of loans are allowed to depend on macroeconomic conditions as well as on unobservable credit risk factors, which can capture contagion effects between sectors. In addition, we also model the distributions of the Exposure at Default and the Loss Given Default. We apply our model to the Spanish credit market, where we find that sectorial default frequencies are affected by a persistent latent factor. Finally, we also identify the potentially riskier sectors and perform stress tests.

Suggested Citation

  • Gabriel Jiménez & Javier Mencía, 2007. "Modeling the distribution of credit losses with observable and latent factors," Working Papers 0709, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:0709
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    References listed on IDEAS

    as
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    Cited by:

    1. Sreejata Banerjee & Divya Murali, 2015. "Stress Test of Banks in India: A VAR Approach," Working Papers 2015-102, Madras School of Economics,Chennai,India.
    2. Stefan Kerbl & Michael Sigmund, 2011. "What Drives Aggregate Credit Risk?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 72-87.
    3. Lee, Yongwoong & Poon, Ser-Huang, 2014. "Forecasting and decomposition of portfolio credit risk using macroeconomic and frailty factors," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 69-92.
    4. Patrick Van Roy & Stijn Ferrari & Cristina Vespro, 2018. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Working Paper Research 338, National Bank of Belgium.
    5. Carlos Pérez Montes, 2013. "Estimation of Regulatory Credit Risk Models," Working Papers 1305, Banco de España;Working Papers Homepage.
    6. Bermingham, Colin & Conefrey, Thomas, 2014. "The Irish macroeconomic response to an external shock with an application to stress testing," Journal of Policy Modeling, Elsevier, vol. 36(3), pages 454-470.
    7. Petr Gapko & Martin Smid, 2016. "Multi-Period Structural Model of a Mortgage Portfolio with Cointegrated Factors," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 565-574, December.
    8. Carlos Perez Montes, 2015. "Estimation of Regulatory Credit Risk Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 48(2), pages 161-191, October.
    9. 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.
    10. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    11. Pérez Montes, Carlos, 2014. "The effect on competition of banking sector consolidation following the financial crisis of 2008," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 124-136.

    More about this item

    Keywords

    credit risk; probability of default; loss distribution; stress test; contagion;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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