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Modelling the distribution of credit losses with observable and latent factors

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  • Jiménez, Gabriel
  • Mencía, Javier

Abstract

This paper proposes a dynamic model to estimate the credit loss distribution of the aggregate portfolio of loans granted in a banking system. We consider a sectoral approach distinguishing between corporates and households. The evolution of their default frequencies and the size of the loans portfolio are expressed as functions of macroeconomic conditions as well as unobservable credit risk factors, which capture contagion effects between sectors. In addition, we model the distributions of the Exposures at Default and the Losses Given Default. We apply our framework to the Spanish banking system, where we find that sectoral default frequencies are not only affected by economic cycles but also by a persistent latent factor. Finally, we identify the riskier sectors, perform stress tests and compare the relative risk of small and large institutions.

Suggested Citation

  • Jiménez, Gabriel & Mencía, Javier, 2009. "Modelling the distribution of credit losses with observable and latent factors," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 235-253, March.
  • Handle: RePEc:eee:empfin:v:16:y:2009:i:2:p:235-253
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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. 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.
    3. 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.
    4. Lee, Yongwoong & Yang, Kisung, 2019. "Modeling diversification and spillovers of loan portfolios' losses by LHP approximation and copula," International Review of Financial Analysis, Elsevier, vol. 66(C).
    5. Maria Stefanova, 2012. "Recovery Risiko in der Kreditportfoliomodellierung," Springer Books, Springer, number 978-3-8349-4226-5, January.
    6. Apergis, Emmanuel & Apergis, Iraklis & Apergis, Nicholas, 2019. "A new macro stress testing approach for financial realignment in the Eurozone," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 52-80.
    7. 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.
    8. Ferrari, Stijn & Van Roy, Patrick & Vespro, Cristina, 2021. "Sensitivity of credit risk stress test results: Modelling issues with an application to Belgium," Journal of Financial Stability, Elsevier, vol. 52(C).
    9. Stefan Kerbl & Michael Sigmund, 2011. "What Drives Aggregate Credit Risk?," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 22, pages 72-87.
    10. Carlos Pérez Montes, 2013. "Estimation of Regulatory Credit Risk Models," Working Papers 1305, Banco de España.
    11. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2021. "Systematic credit risk in securitised mortgage portfolios," Journal of Banking & Finance, Elsevier, vol. 122(C).
    12. Philip Arestis & Maggie Mo Jia, 2019. "Credit risk and macroeconomic stress tests in China," Journal of Banking Regulation, Palgrave Macmillan, vol. 20(3), pages 211-225, September.
    13. Miroslav Misina & David Tessier, 2008. "Non-Linearities, Model Uncertainty, and Macro Stress Testing," Staff Working Papers 08-30, Bank of Canada.
    14. Memmel, Christoph & Roling, Christoph, 2021. "Risks in domestic banks' corporate lending business," Technical Papers 08/2021, Deutsche Bundesbank.
    15. 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.
    16. 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.
    17. Memmel, Christoph & Roling, Christoph, 2021. "Risiken im Unternehmenskreditgeschäft inländischer Banken [Risks in domestic banks' corporate lending business]," Technical Papers 08/2021, Deutsche Bundesbank.
    18. Kusaya, Charles & O’Keefe, John P. & Ufier, Alexander B., 2023. "Bridging the gap from the current deposit insurance fund to a fund target," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 148-157.
    19. 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.
    20. 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.

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    More about this item

    Keywords

    Credit risk Probability of default Loss distribution Stress test Contagion Latent factors;

    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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