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What Drives Aggregate Credit Risk?

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Author Info

  • Sigmund, Michael

    ()
    (Oesterreichische Nationalbank, Financial Markets Analysis and Surveillance Division)

  • Kerbl, Stefan

    ()
    (Oesterreichische Nationalbank, On-Site Banking Inspections Division – Large Banks)

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    Abstract

    A deep understanding of the drivers of credit risk is valuable for financial institutions as well as for regulators from multiple viewpoints. The systemic component of credit risk drives losses across portfolios and thus poses a threat to financial stability. Traditional approaches consider macroeconomic variables as drivers of aggregate credit risk (ACR). However, recent literature suggests the existence of a latent risk factor influencing ACR, which is regularly interpreted as the latent credit cycle. We explicitly model this latent factor by adding an unobserved component to our models, which already include macroeconomic variables. In this paper we make use of insolvency rates of Austrian corporate industry sectors to model realized probabilities of default. The contribution of this paper to the literature on ACR risk is threefold. First, in order to cope with the lack of theory behind ACR drivers, we implement state-of-the-art variable selection algorithms to draw from a rich set of macroeconomic variables. Second, we add an unobserved risk factor to a state space model, which we estimate via a Kalman filter in an expectation maximization algorithm. Third, we analyze whether the consideration of an unobserved component indeed improves the fit of the estimated models.

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    Bibliographic Info

    Article provided by Oesterreichische Nationalbank (Austrian Central Bank) in its journal Financial Stability Report.

    Volume (Year): (2011)
    Issue (Month): 22 ()
    Pages:

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    Handle: RePEc:onb:oenbfs:y:2011:i:22:b:2

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    Related research

    Keywords: credit risk; unobserved component models; state space; Kalman filter; stress testing;

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    References

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    1. Diana Bonfim, 2006. "Credit Risk Drivers: Evaluating the Contribution of Firm Level Information and of Macroeconomic Dynamics," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
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    3. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
    4. André Lucas & Siem Jan Koopman, 2005. "Business and default cycles for credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(2), pages 311-323.
    5. José Casals Carro & Alfredo García-Hiernaux & Miguel Jerez, 2010. "From general State-Space to VARMAX models," Documentos del Instituto Complutense de Análisis Económico 1002, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.
    6. Siem Jan Koopman & Andr� Lucas & Bernd Schwaab, 2008. "Forecasting Cross-Sections of Frailty-Correlated Default," Tinbergen Institute Discussion Papers 08-029/4, Tinbergen Institute.
    7. Koopman, Siem Jan & Kräussl, Roman & Lucas, André & Monteiro, André B., 2009. "Credit cycles and macro fundamentals," Journal of Empirical Finance, Elsevier, vol. 16(1), pages 42-54, January.
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    12. 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.
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    20. Gabriel Jiménez & Jesús Saurina, 2006. "Credit Cycles, Credit Risk, and Prudential Regulation," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
    21. Giacomo Giampieri & Mark Davis & Martin Crowder, 2005. "Analysis of default data using hidden Markov models," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 27-34.
    22. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768.
    23. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
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    25. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    26. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-32, Nov.-Dec..
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    Cited by:
    1. Neudorfer, Benjamin & Sigmund, Michael & Eidenberger, Judith & Stein, Ingrid, 2013. "Quantifying Financial Stability in Austria – New Tools for Macroprudential Supervision," Financial Stability Report, Oesterreichische Nationalbank (Austrian Central Bank), issue 26, pages 62-81.

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