Disaggregated Credit Extension and Financial Distress in South Africa



Abstract. This study analyses the relationship between disaggregated credit extension and financial distress in South Africa. Of particular interest is to isolate the components of disaggregated credit extension that show a strong relationship with the measure of financial distress. The empirical results reveal that aggregate total domestic credit extension is robustly positively correlated with the composite indicator of financial distress, while there is a mixed relationship between the components of disaggregated credit extension and the composite indicator of financial distress. In particular, the study finds that total domestic credit extension, instalment sale credit, loans and advances to households, investments and total loans and advances are highly correlated with the composite indicator of financial distress. Therefore,the study conjures that these components could be aggregated into a single measure of credit extension that could be usedfor financial stability purposes in South Africa.

Keywords. Disaggregated credit extension, Financial distress.

JEL. C32, E44, E51, G21.


Disaggregated credit extension; Financial distress indicator

Full Text:


Amini, S.M. & Parmeter, C.F. (2011). Bayesian model averaging in R, Journal of Economic and Social Measurement, 36(4), 253-287. doi. 10.3233/JEM-2011-0350

Amini, S.M. & Parmeter, C.F. (2012). A Review of the BMS Package for R. Journal of Applied Econometrics, 27(5), 870-876. doi. 10.1002/jae.2288

Andersen, H., Giese, J., Bush, O., Castro, C., Farag, M. & Kapadia, S. (2013). Conference on Financial Stability Analysis, Cleveland: Federal Reserve Bank of Cleveland, May 31.

Balakrishnan, R., Danninger, S., Elekdag, S. & Tytell, I. (2009). The Transmission of Financial Stress from Advanced to Emerging Economies. IMF Working Paper, No. 09/133. doi. 10.5089/9781451872804.001

Basel Committee on Banking Supervision. (2010a). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems. Bank of International Settlements, December

Basel Committee on Banking Supervision. (2010b). Guidance for National Authorities Operating the Countercyclical Capital Buffer. Bank of International Settlements, December

Basel Committee on Banking Supervision. (2011). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems. Bank of International Settlements, Revised Version, June

Borgy, V., Laurent, C., & Jean-Paul, R. (2009). Asset price boom bust cycles and credit: What is the scope of macro prudential regulation?. Working Paper, Basel: Bank of International Settlements, doi. 10.2139/ssrn.1630093

Borio, C., Drehmann, M., Gambacorta, L., Jiminez, G. & Trucharte, C. (2010). Countercyclical capital buffers: Exploring options. Bank of International Settlements, Working Paper, No. 317. doi. 10.2139/ssrn.1648946

Borio, C., Drehmann, M. & Tsatsaronis, K. (2011). Anchoring countercyclical capital buffers: The role of credit aggregates. Bank of International Settlements, Working Paper, No. 355. [Retrieved from].

Cardarelli, R., Elekdag, S. & Lall, S. (2011). Financial stress and economic contractions. Journal of Financial Stability, 7(2), 78-97. doi. 10.1016/j.jfs.2010.01.005

Crespo Cuaresma, J. & Slacik, T. (2009). On the determinants of currency crises: The role of model uncertainty. Journal of Macroeconomics, 31(4), 621-632. doi. 10.1016/j.jmacro.2009.01.004

Crespo Cuaresma, J. (2010). Can emerging asset price bubbles be detected?. Paris: Organisation for Economic Co-operation and Development, Working Paper, No. 772. doi. 10.1787/5kmdfmztmqtj-en

Davis, E.P. (2009). Theories of financial instability and their practical relevance. Course on Financial Instability, Tallinn: Estonian Central Bank, December 9-11. [Retrieved from).

Demirguc-Kunt, A., & Detragiache, E. (1998). The determinants of banking crises in developing and developed Countries. IMF Staff Papers, No. 45(1), 81-109. doi. 10.2307/3867330

Demirguc-Kunt, A. & Detragiache, E. (1999). Monitoring banking sector fragility: A multinomial logit approach. IMF Working Paper, No. 99-147, doi. 10.5089/9781451856712.001

European Banking Federation. (2011). Credit Cycles and Their Role in Macroprudential Policy. Report to Economic and Monetary Affairs Committee, European Banking Federation, November. [Retrieved from].

Faust, J., Gilchrist, S., Wright, J.H., & Zakrajssek, E. (2013). Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach. Review of Economics and Statistics, 95(5), 1501–1519. doi. 10.1162/rest_a_00376

Feldkircher, M. (2014). The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk. Journal of International Money and Finance, 43, 19-49. doi. 10.1016/j.jimonfin.2013.12.003

Feldkircher, M., & Zeugner, S. (2009). Benchmark priors revisited: On adaptive shrinkage and the supermodel effect in Bayesian model averaging. IMF Working Paper, No. 09/202, doi. 10.5089/9781451873498.001

Fernandez, C., Ley, E., & Steel, M. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100(2), 381-427. doi. 10.1016/s0304-4076(00)00076-2

Gali, J., (2014). Monetary policy and rational asset price bubbles. American Economic Review, 104(3), 721-752. doi. 10.1257/aer.104.3.721

Gali, J., & Gambetti, L. (2015). The effects of monetary policy on stock market bubbles: Some evidence. American Economic Journal, 7(1), 233–257. doi. 10.1257/mac.20140003

Giese, J., Andersen, H., Bush, O., Castro, C., Farag, M., & Kapadia, S. (2014). The credit-to-GDP gap and complementary indicators for macroprudential policy: Evidence from the UK. International Journal of Finance & Economics, 19(1), 25-47, doi. 10.1002/ijfe.1489

Goodhart, C., & Hofmann, B. (2008). House prices, money, credit, and the macroeconomy. Oxford Review of Economic Policy, 24(1), 180-205. doi. 10.1093/oxrep/grn009

Hakkio, G., & Keeton, W. (2009). Financial stress: What is it, how can it be measured, and why does it matter?. Economic Review, 94(2), Federal Reserve Bank of Kansas City. [Retrieved from].

Illing, M., & Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. Journal of Financial Stability, 2(3), 243-265. doi. 10.1016/j.jfs.2006.06.002

Jeong, H. (2009). The procyclicality of bank lending and its funding structure: The case of Korea. Conference Paper, Bank of Korea. doi. 10.2139/ssrn.1663501

Jorda, O., Schularick, M., & Taylor, A. (2011). When credit bites back: Leverage, business cycles, and crises. NBER Working Paper, No. 17621, doi. 10.3386/w17621

Koop, G., & Korobilis, D. (2012). Forecasting inflation using dynamic model averaging. International Economic Review, 53(3), 867-886. doi. 10.1111/j.1468-2354.2012.00704.x

Ley, E., & Steel, M.F.J. (2009). On the effect of prior assumptions in Bayesian model averaging with applications to growth regression. Journal of Applied Econometrics, 24(4), 651-674. doi. 10.1002/jae.1057

Lo Duca, M., & Peltonen, T. (2011). Macro-financial vulnerabilities and future financial stress - assessing systemic risks and predicting systemic events. European Central Bank Working Paper, No. 1311. doi. 10.2139/ssrn.1803075

O’Hara, R.B., & Sillanpaa M.J. (2009). A review of Bayesian variable selection methods: What, how and which. Bayesian Analysis, 4(1), 85-117. doi. 10.1214/09-ba403

Schularick, M., & Taylor, A.M., (2012). Credit booms gone bust: Monetary policy, leverage cycles, and financial crises, 1870–2008. American Economic Review, 102(2), 1029-1061. doi. 10.1257/aer.102.2.1029

South African Reserve Bank. (2013). Quarterly Bulletin. Pretoria: South African Reserve Bank, September

Taylor, A. (2012). External imbalances and financial crises. NBER Working Paper, No. 18606, doi. 10.5089/9781484322260.001

Zellner, A. (1986). On assessing prior distributions and Bayesian regression analysis with g-prior distributions. in, Bayesian Inference and Decision Techniques: Essays in Honour of Bruno de Finetti, (Eds)Goel, P.K. &Zellner, A., Amsterdam: North Holland

Zeugner, S. (2012). Bayesian model averaging with BMS. R-package, Vs. 0.3.1, The R Project for Statistical Computing, [Retrieved from].

DOI: http://dx.doi.org/10.1453/jel.v3i2.737


  • There are currently no refbacks.


Journal of Economics Library - J. Econ. Lib. - JEL - www.kspjournals.org

ISSN: 2149-2379

Editor: jel@ksplibrary.org Secretarial: secretarial@ksplibrary.org   Istanbul - Turkey.

Copyright © KSP Library