Abstract
Abstract. This paper analyses the behaviour of alternative measures of credit extension for countercyclical buffer decisions in South Africa. These measures include the deviation of the ratio of private sector credit extension to gross domestic product from its long term trend, the deviation of the logarithm of private sector credit extension from its long term trend as well as the annual percentage change in private sector credit extension. The cyclical properties of these measures are examined over the economic and the financial cycles. The results show that the deviation of the ratio of private sector credit extension to gross domestic product from its long term trend is countercyclical with the economic cycle. The results further show that the deviation of the logarithm of private sector credit extension from its long term trend is procyclical with both the economic and the financial cycle. The results finally show that the annual percentage change in private sector credit extension generally performs poorly in cyclical terms with both the economic and the financial cycle. Consequently, of the three alternative measures of private sector credit extension considered, the deviation of the logarithm of private sector credit extension from its long term trend could be used as a common reference guide for implementing the countercyclical capital buffers for financial institutions in South Africa.
Keywords. Credit extension, Countercyclical capital buffers.
JEL. C32, E44, E51, G21.
References
Amini, S. & Parmeter, C. (2011). Bayesian model Averaging in R, Journal of Economic and Social Measurement, 36(4), 253-287. doi. 10.3233/JEM-2011-0350
Amini, S. & Parmeter, C. (2012). Comparisons of Model Averaging Techniques: Assessing Growth Determinants, 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. (2014). The Credit-to-GDP Gap and Complementary Indicators for Macroprudential Policy: Evidence from the UK, International Journal of Finance and Economics, 19(1), 25-47. doi. 10.1002/ijfe.1489
Balakrishnan, R., Danninger, S., Elekdag, S., & Tytell, I. (2009). The Transmission of Financial Stress from Advanced to Emerging Economies, Working Paper, 09/133, International Monetary Fund
Basel Committee on Banking Supervision. (2010a). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems, Bank for International Settlements.
Basel Committee on Banking Supervision. (2010b). Guidance for National Authorities Operating the Countercyclical Capital Buffer, Bank for International Settlements.
Basel Committee on Banking Supervision. (2011). Basel III: A Global Regulatory Framework for More Resilient Banks and Banking Systems. Revised Version, Bank for International Settlements.
Bartels, L. (1997). Specification Uncertainty and Model Averaging, American Journal of Political Science, 41(2). 641-674.
Behn, M., Detken, C., Peltonen, T., & Schudel, W. (2013). Setting countercyclical capital buffers based on early warning models: would it work?, Working Paper, 1604, European Central Bank
Bernstein, J., Raputsoane, L., & Schaling, E. (2014). Credit Procyclicality and Financial Regulation in South Africa, Working Paper, 445, Economic Research Southern Africa.
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, 263, Bank for International Settlements.
Borio, C., Drehmann, M., Gambacorta, L., Jiminez, G., & Trucharte, C. (2010). Countercyclical Capital Buffers: Exploring Options, Working Paper, 317, Bank of International Settlements.
Borio, C., Drehmann, M., & Tsatsaronis, K. (2011). Anchoring Countercyclical Capital Buffers: The Role of Credit Aggregates, Working Paper, 355, Bank of International Settlements.
Borio, C. (2012). The Financial Cycle and Macroeconomics: What have we Learned, Working Paper, 395, Bank of International Settlements.
Drehmann, M., & Juselius, M. (2014). Evaluating early warning indicators of banking crises: satisfying policy requirements, International Journal of Forecasting, 30(3), 759-780. doi. 10.1016/j.ijforecast.2013.12.003
Drehmann, M., & Tsatsaronis, K. (2014). The credit-to GDP Gap and Countercyclical Capital Buffers: Questions and Answers, Quarterly Review, Bank for International Settlements.
Edge, R., & Meisenzahl, R. (2011). The Unreliability of Credit-to-GDP Ratio Gaps in Real-Time: Implications for Countercyclical Capital Buffers, International Journal of Central Banking, 7(4), 261-298.
Feldkircher, M., & Zeugner, S. (2009). Benchmark Priors Revisited: On Adaptive Shrinkage and the Supermodel Effect in Bayesian Model Averaging, Working Paper, 09/202, International Monetary Fund.
Gersl, A., & Jakubik, P. (2010). Procyclicality of the Financial System and Simulation of the Feedback Effect, CNB Financial Stability Report, 2010/2011, 112-122.
Gersl, A., & Seidler, J. (2011). Excessive Credit Growth as an Indicator Of Financial (In)Stability and its Use in Macroprudential Policy, Working Paper, 42541, Munich Personal RePEc Archive.
Giannone, D., Lenza, M., & Reichlin, L. (2012). Money, Credit, Monetary Policy and the Business Cycle in the Euro Area, Discussion Paper, 8944, Centre for Economic Policy Research.
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, Q II, Federal Reserve Bank of Kansas City.
Hodrick, R., & Prescott, E. (1997). Postwar U.S. Business Cycles: An Empirical Investigation, Journal of Money, Credit, and Banking, 29(1), 1-16.
Hoeting, J., Madigan, D., Raftery, A., & Volinsky, C. (1999). Bayesian Model Averaging: A Tutorial, Statistical Science, 14(4), 382-417.
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
Jorda, O., Schularick, M., & Taylor, A. (2013). When Credit Bites Back: Leverage, Business Cycles, and Crises, Journal of Money, Credit and Banking, 45(s2), 3-28. doi. 10.1111/jmcb.12069
Kliesen, K., Owyang, M. and Vermann, K. (2012). Disentangling Diverse Measures: A Survey of Financial Stress Indexes,Review, 94(5): 369-398, Federal Reserve Bank of St. Louis.
Leamer, E. (1978). Specification searches: Ad hoc inference with nonexperimental data, New York: Wiley.
Lim, V., & Siregar, R. (2010). The Role of Central Banks in Sustaining Economic Recovery and in Achieving Financial Stability, Journal of Advanced Studies in Finance, 1(1): 83-99.
Lo Duca, M., & Peltonen, T. (2011). Macro-Financial Vulnerabilities and Future Financial Stress - Assessing Systemic Risks and Predicting Systemic Events, Working Paper, 1311, European Central Bank.
Mise, E., Kimand, T. H., & Newbold, P. (2005). On Suboptimality of the Hodrick-Prescott Filter At Time Series Endpoints, Journal of Macroeconomics, 27(1), 53-67. doi. 10.1016/j.jmacro.2003.09.003
Nigam, K., (2013). Credit to GDP as a Forward Looking Indicator of Systemic Credit Risk: A Critical Evaluation Using Data from Uganda, Working Paper, 11/2013, Bank of Uganda.
Raputsoane, L. (2014). Disaggregated credit extension and financial distress in South Africa, Working paper, 435, Economic Research Southern Africa.
Repullo, R., & Saurina, J. (2011). The Countercyclical Capital Buffer of Basel III: A Critical Assessment, Discussion Paper, 8304, Centre for Economic Policy Research.
Schularick, M., & Taylor, A. (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
Taylor, A. (2012). External Imbalances and Financial Crises, Working Paper, 18606, Massachusetts: National Bureau of Economic Research.
Varian, H. (2014). Big Data: New Tricks for Econometrics, Journal of Economic Perspectives, 28(2): 3-28. doi. 10.1257/jep.28.2.3
Zellner, A. (1986). On Assessing Prior Distributions and Bayesian Regression Analysis with g-prior Distributions, in Goel, P.K. and Zellner, A. (Eds),Bayesian Inference and Decision Techniques: Essays in Honour of Bruno de Finetti, Amsterdam.
Zeugner, S. (2012). Bayesian Model Averaging with BMS, R-package, 0.3.1, The R Project for Statistical Computing.
