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Linking Global Economic Dynamics to a South African-Specific Credit Risk Correlation Model

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  • Albert H. De Wet

    (First Rand Bank)

  • Renee´ Van Eyden

    ()
    (Department of Economics, University of Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

Abstract

In order to address practical questions in credit portfolio management it is necessary to link the cyclical or systematic components of firm credit risk with the firm’s own idiosyncratic credit risk as well as the systematic credit risk component of every other exposure in the portfolio. This paper builds on the methodology proposed by Pesaran, Schuermann, and Weiner (2004) and supplemented by Pesaran, Schuermann, Treutler and Weiner (2006) which has made a significant advance in credit risk modelling in that it avoids the use of proprietary balance sheet and distance-to-default data, focusing on credit ratings which are more freely available. In this paper a country-specific macroeconometric risk driver engine which is compatible with and could feed into the GVAR model and framework of PSW (2004) is constructed, using vector error-correcting (VECM) techniques. This allows conditional loss estimation of a South African-specific credit portfolio but also opens the door for credit portfolio modelling on a global scale, as such a model can easily be linked to the GVAR model. The set of domestic factors are extended beyond those used in PSW (2004) in such a way that the risk driver model is applicable for both retail and corporate credit risk. As such, the model can be applied to a total bank balance sheet, incorporating the correlation and diversification between both retail and corporate credit exposures. Assuming statistical over-identification restrictions, the results indicate that it is possible to construct a South African component for the GVAR model that can easily be integrated into the global component. From a practical application perspective the framework and model is particularly appealing since it can be used as a theoretically consistent correlation model within a South African-specific credit portfolio management tool.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 200719.

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Length: 31 pages
Date of creation: Sep 2007
Date of revision:
Handle: RePEc:pre:wpaper:200719

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Keywords: Credit portfolio management; multifactor model; vector error correction model (VECM); credit correlations;

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References

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  1. Pesaran, M. H. & Shin, Y., 1997. "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics 9710, Faculty of Economics, University of Cambridge.
  2. Johansen, Soren, 1992. "Cointegration in partial systems and the efficiency of single-equation analysis," Journal of Econometrics, Elsevier, vol. 52(3), pages 389-402, June.
  3. Seth B. Carpenter & William Whitesell & Egon Zakrajsek, 2001. "Capital requirements, business loans, and business cycles: an empirical analysis of the standardized approach in the new Basel Capital Accord," Finance and Economics Discussion Series 2001-48, Board of Governors of the Federal Reserve System (U.S.).
  4. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  5. Pesaran, M.H. & Weiner, S.M., 2001. "Modelling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Cambridge Working Papers in Economics 0119, Faculty of Economics, University of Cambridge.
  6. 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.
  7. Pesaran, M. Hashem & Shin, Yongcheol & Smith, Richard J., 2000. "Structural analysis of vector error correction models with exogenous I(1) variables," Journal of Econometrics, Elsevier, vol. 97(2), pages 293-343, August.
  8. Til Schuermann & Björn-Jakob Treutler & Scott M. Weiner & M. Hashem Pesaran, 2003. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," CESifo Working Paper Series 995, CESifo Group Munich.
  9. Pesaran, M. H. & Smith, Ron P., 1998. "Structural Analysis of Cointegrating VARs," Cambridge Working Papers in Economics 9811, Faculty of Economics, University of Cambridge.
  10. Stephen G. Hall & Jennifer V. Greenslade & S. G. Brian Henry, 1999. "On the Identification of Cointegrated Systems in Small Samples: Practical Procedures with an Application to UK Wages and Prices," Computing in Economics and Finance 1999 643, Society for Computational Economics.
  11. Carey, Mark, 2002. "A guide to choosing absolute bank capital requirements," Journal of Banking & Finance, Elsevier, vol. 26(5), pages 929-951, May.
  12. Stephane Dees & Filippo di Mauro & M. Hashem Pesaran & L. Vanessa Smith, 2004. "Exploring the International Linkages of the Euro Area: A Global VAR Analysis," IEPR Working Papers 04.6, Institute of Economic Policy Research (IEPR).
  13. Linda Allen & Anthony Saunders, 2004. "Incorporating Systemic Influences Into Risk Measurements: A Survey of the Literature," Journal of Financial Services Research, Springer, vol. 26(2), pages 161-191, October.
  14. Mark Carey, 2002. "A guide to choosing absolute bank capital requirements," International Finance Discussion Papers 726, Board of Governors of the Federal Reserve System (U.S.).
  15. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  16. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, September.
  17. M. Hashem Pesaran & Ron P. Smith, 1998. "Structural Analysis of Cointegrating VARs," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 471-505, December.
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Cited by:
  1. Alexander Chudik & Hashem Pesaran, 2014. "Theory and Practice of GVAR Modeling," Cambridge Working Papers in Economics 1408, Faculty of Economics, University of Cambridge.
  2. Melisso Boschi, 2012. "Long- and short-run determinants of capital flows to Latin America: a long-run structural GVAR model," Empirical Economics, Springer, vol. 43(3), pages 1041-1071, December.
  3. Ballestra, Luca Vincenzo & Pacelli, Graziella, 2014. "Valuing risky debt: A new model combining structural information with the reduced-form approach," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 261-271.

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