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

Author

Listed:
  • 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.

Suggested Citation

  • Albert H. De Wet & Renee´ Van Eyden & Rangan Gupta, 2007. "Linking Global Economic Dynamics to a South African-Specific Credit Risk Correlation Model," Working Papers 200719, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:200719
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    2. Zedginidze Zviad, 2012. "Linking Macroeconomic Dynamics to Georgian Credit Portfolio Risk," EERC Working Paper Series 12/07e, EERC Research Network, Russia and CIS.
    3. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    4. 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.
    5. Annari Waal & Reneé Eyden, 2014. "Monetary policy and inflation in South Africa: A VECM augmented with foreign variables," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 117-140, March.
    6. Miora Rakotonirainy & Jean Razafindravonona & Christian Rasolomanana, 2020. "Macro Stress Testing Credit Risk: Case of Madagascar Banking Sector," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(2), pages 199-218.
    7. Alizadeh Janvisloo, Mohammad Reza & Sherafatian-Jahromi, Reza, 2019. "Macroeconomic, International Linkage and Effects of External Shocks in Southeast Asian Emerging Economies," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 14(2), pages 205-230, April.
    8. Mevlüt TATLIYER, 2017. "Determinants of Private Saving Level: Evidence from TurkeyAbstract: This paper attempts to ascertain the determinants of private saving level in Turkey. We implemented OLS estimations and constructed a VEC model in order to circumvent possible endoge," Sosyoekonomi Journal, Sosyoekonomi Society.
    9. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
    10. 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.

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    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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