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Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics

Author

Listed:
  • Andre Lucas

    (VU University Amsterdam)

  • Bernd Schwaab

    (European Central Bank, Financial Markets Research)

  • Xin Zhang

    (VU University Amsterdam, and Sveriges Riksbank, Research Division)

Abstract

Forthcoming in the 'Journal of Applied Econometrics'. We develop a novel high-dimensional non-Gaussian modeling framework to infer conditional and joint risk measures for many financial sector firms. The model is based on a dynamic Generalized Hyperbolic Skewed-t block-equicorrelation copula with time-varying volatility and dependence parameters that naturally accommodates asymmetries, heavy tails, as well as non-linear and time-varying default dependence. We demonstrate how to apply a conditional law of large numbers in this setting to define risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple financial firm defaults in the euro area during the 2008-2012 financial and sovereign debt crisis. We document unprecedented tail risks during 2011-12, as well as their steep decline after subsequent policy actions.

Suggested Citation

  • Andre Lucas & Bernd Schwaab & Xin Zhang, 2013. "Measuring Credit Risk in a Large Banking System: Econometric Modeling and Empirics," Tinbergen Institute Discussion Papers 13-063/IV/DSF56, Tinbergen Institute, revised 13 Oct 2014.
  • Handle: RePEc:tin:wpaper:20130063
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    References listed on IDEAS

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    1. Drew Creal & Bernd Schwaab & Siem Jan Koopman & Andr� Lucas, 2014. "Observation-Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 898-915, December.
    2. Duffie, Darrell & Saita, Leandro & Wang, Ke, 2007. "Multi-period corporate default prediction with stochastic covariates," Journal of Financial Economics, Elsevier, vol. 83(3), pages 635-665, March.
    3. Siem Jan Koopman & André Lucas & Bernd Schwaab, 2012. "Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 521-532, May.
    4. P. Hartmann & S. Straetmans & C. G. de Vries, 2004. "Asset Market Linkages in Crisis Periods," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 313-326, February.
    5. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
    6. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
    7. Lucas, Andre & Klaassen, Pieter & Spreij, Peter & Straetmans, Stefan, 2001. "An analytic approach to credit risk of large corporate bond and loan portfolios," Journal of Banking & Finance, Elsevier, vol. 25(9), pages 1635-1664, September.
    8. Philipp Hartmann & Stefan Straetmans & Casper de Vries, 2007. "Banking System Stability. A Cross-Atlantic Perspective," NBER Chapters, in: The Risks of Financial Institutions, pages 133-188, National Bureau of Economic Research, Inc.
    9. Dong Hwan Oh & Andrew J. Patton, 2018. "Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(2), pages 181-195, April.
    10. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    11. Kjersti Aas & Ingrid Hobaek Haff, 2006. "The Generalized Hyperbolic Skew Student's t-Distribution," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(2), pages 275-309.
    12. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    13. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    14. Suh, Sangwon, 2012. "Measuring systemic risk: A factor-augmented correlated default approach," Journal of Financial Intermediation, Elsevier, vol. 21(2), pages 341-358.
    15. Carey, Mark & Stulz, René M. (ed.), 2007. "The Risks of Financial Institutions," National Bureau of Economic Research Books, University of Chicago Press, number 9780226092850, December.
    16. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    17. Daniel Covitz & Nellie Liang & Tobias Adrian, 2015. "Financial Stability Monitoring," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 357-395, December.
    18. Bauwens, Luc & Laurent, Sebastien, 2005. "A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 346-354, July.
    19. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    20. Mr. Renzo G Avesani & Ms. Jing Li & Antonio I Garcia Pascual, 2006. "A New Risk Indicator and Stress Testing Tool: A Multifactor Nth-to-Default CDS Basket," IMF Working Papers 2006/105, International Monetary Fund.
    21. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    22. Black, Lamont & Correa, Ricardo & Huang, Xin & Zhou, Hao, 2016. "The systemic risk of European banks during the financial and sovereign debt crises," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 107-125.
    23. Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2012. "Stationarity and Ergodicity of Univariate Generalized Autoregressive Score Processes," Tinbergen Institute Discussion Papers 12-059/4, Tinbergen Institute.
    24. Segoviano, Miguel A. & Goodhart, Charles, 2009. "Banking stability measures," LSE Research Online Documents on Economics 24416, London School of Economics and Political Science, LSE Library.
    25. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    26. André Lucas & Bernd Schwaab & Xin Zhang, 2014. "Conditional Euro Area Sovereign Default Risk," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 271-284, April.
    27. Xin Zhang & Drew Creal & Siem Jan Koopman & Andre Lucas, 2011. "Modeling Dynamic Volatilities and Correlations under Skewness and Fat Tails," Tinbergen Institute Discussion Papers 11-078/2/DSF22, Tinbergen Institute.
    28. Mr. C. A. E. Goodhart & Miguel A. Segoviano, 2009. "Banking Stability Measures," IMF Working Papers 2009/004, International Monetary Fund.
    29. Schwaab, Bernd & Eser, Fabian, 2013. "Assessing asset purchases within the ECB’s securities markets programme," Working Paper Series 1587, European Central Bank.
    30. Andre Lucas & Pieter Klaassen & Peter Spreij & Stefan Straetmans, 2003. "Tail behaviour of credit loss distributions for general latent factor models," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(4), pages 337-357.
    31. Mencia, Javier F. & Sentana, Enrique, 2004. "Estimation and testing of dynamic models with generalised hyperbolic innovations," LSE Research Online Documents on Economics 24742, London School of Economics and Political Science, LSE Library.
    32. Andres, Philipp, 2014. "Maximum likelihood estimates for positive valued dynamic score models; The DySco package," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 34-42.
    33. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    34. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    35. Branco, Márcia D. & Dey, Dipak K., 2001. "A General Class of Multivariate Skew-Elliptical Distributions," Journal of Multivariate Analysis, Elsevier, vol. 79(1), pages 99-113, October.
    36. Eser, Fabian & Carmona Amaro, Marta & Iacobelli, Stefano & Rubens, Marc, 2012. "The use of the Eurosystem's monetary policy instruments and operational framework since 2009," Occasional Paper Series 135, European Central Bank.
    37. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    38. Viral Acharya & Robert Engle & Matthew Richardson, 2012. "Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks," American Economic Review, American Economic Association, vol. 102(3), pages 59-64, May.
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    Citations

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    Cited by:

    1. Francisco Blasques & Siem Jan Koopman & Katarzyna Lasak & André Lucas, 2015. "In-Sample Bounds for Time-Varying Parameters of Observation Driven Models," Tinbergen Institute Discussion Papers 15-027/III, Tinbergen Institute, revised 07 Sep 2015.
    2. Kurose, Yuta & Omori, Yasuhiro, 2016. "Dynamic equicorrelation stochastic volatility," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 795-813.
    3. Marco Bazzi & Francisco Blasques & Siem Jan Koopman & Andre Lucas, 2017. "Time-Varying Transition Probabilities for Markov Regime Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 458-478, May.
    4. Jouchi Nakajima & Tsuyoshi Kunihama & Yasuhiro Omori, 2017. "Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(7), pages 1248-1268, May.
    5. Blasques, Francisco & Koopman, Siem Jan & Łasak, Katarzyna & Lucas, André, 2016. "In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models," International Journal of Forecasting, Elsevier, vol. 32(3), pages 875-887.
    6. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Information Theoretic Optimality of Observation Driven Time Series Models," Tinbergen Institute Discussion Papers 14-046/III, Tinbergen Institute.
    7. Rutger-Jan Lange & Andre Lucas & Arjen H. Siegmann, 2016. "Score-Driven Systemic Risk Signaling for European Sovereign Bond Yields and CDS Spreads," Tinbergen Institute Discussion Papers 16-064/IV, Tinbergen Institute.

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    More about this item

    Keywords

    systemic risk; dynamic equicorrelation model; generalized hyperbolic distribution; Law of Large Numbers;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • 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

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