Use of causal analysis to improve the monitoring of the banking system stability
-
DOIhttp://dx.doi.org/10.21511/bbs.13(2).2018.06
-
Article InfoVolume 13 2018, Issue #2, pp. 62-76
- Cited by
- 1507 Views
-
140 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
According to the stages of the banking system stability monitoring, the analysis of caus¬al links is used to identify the causes of the crisis trends spreading and the rationale for the most effective levers of regulatory influence on the banking system parameters by the central bank.
The research is based on the use of the canonical correlation method for structuring causal links between the indicators for the assessment of the banking system stability, which are grouped into four sub-indices (assessing the intensity of credit and financial interaction in the interbank market, the effectiveness of the banking system functions, structural changes and financial disproportions in the banking system, activities of systemically important banks); the method of regression analysis and the calculation of elasticity coefficients is also used to assess the sensitivity of the banking system stability to changes in parameters that characterize the banking regulation instruments.
The article analyzes the results of quantitative and qualitative assessment of the banking system stability (comparison of actual results of the evaluation with the data for previous years and comparison of values of stability indicators with critical values). The causes of detected deviations are determined taking into account the results of applying the canonical correlations method. Regression models have been constructed to confirm the dependence of the banking system stability index on the change in parameters that characterize banking regulation instruments, and to determine the most effective of them. Practical testing of submitted proposals is realized based on the Ukrainian banking system indicators for 2007–2016.
- Keywords
-
JEL Classification (Paper profile tab)G01, G20, G28
-
References41
-
Tables4
-
Figures4
-
- Figure 1. Hypothetically determined causal relationships between indicators for assessing the banking system stability
- Figure 2. Dynamics of changes in the stability index of the Ukrainian banking system for the 2007–2016 period
- Figure 3. Dynamics of changes in values of components of the Ukrainian banking system stability index for 2007–2016
- Figure 4. Dynamics of changes in values of sub-index components of the effective implementation of the Ukrainian banking system functions for 2007–2016
-
- Table 1. Comparison of the assessment findings for banking system stability during the 2015–2016 period
- Table 2. Threshold (critical) values of indicators for assessing the banking system stability
- Table 3. Canonical models of relationships between the indicators for assessing the banking system financial stability
- Table 4. Substantiating the most effective bank regulation tools based on the influence on the level of Ukrainian banking system stability
-
- Barca, V., & Carraro, L. (2013). Monitoring Implementation and Evaluating Performance Experiences from cash social assistance in Moldova. Working paper, OPM, Oxford, UK.
- Bezrodna, O. S., & Lesik, V. O. (2017). Теоретико-методичні аспекти оцінювання фінансової стабільності банківської системи [Teoretyko-metodychni aspekty otsiniuvannia finansovoi stabilnosti bankivskoi systemy]. Problems of Economy, 2, 251-263.
- Brave, S., & Butters, R. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives. Federal Reserve Bank of Chicago, 35(1), 22-43.
- Caranovic, G., & Caranovic, B. (2015). Developing an Aggregate Index for Measuring Financial Stability in the Balkans. Procedia Economics and Finance, 33, 3-17.
- Chaplowe, S. G. (2008). Monitoring and Evaluation Planning. Washington, DC and Baltimore, MD: American Red Cross/Catholic Relief Services.
- Church, Ch., & Rogers, M. M. (2006). Designing for Results: Integrating Monitoring and Evaluation in Conflict Transformation Programs. Search for Common Ground.
- Demirguc-Kunt, A., & Detragiache, E. (2000). Monitoring banking sector fragility: a multivariate logit approach. The World Bank Economic Review, 14(2), 287-307.
- Dubrov, A. M., Mhitarjan, B. C., & Troshin, L. I. (2003). Mногомерные статистические методы [Mnogomernyye statisticheskiye metody]. Ekonomika i Finansy, 352.
- Dumičić, M. (2016). Financial Stability Indicators – The Case of Croatia. Journal of Central Banking Theory and Practice, 5(1), 113-140.
- Flood, M., Lemieux, V., Varga, M., & Wong, W. (2016). The Application of Visual Analytics to Financial Stability Monitoring. Journal of Financial Stability, 27, 180-197.
- Gordon, L. (2015). The Absorption Ratio as an Indicator for Macro-prudential Monitoring in Jamaica (Bank of Jamaica Working Paper Series).
- Handbook on Planning, Monitoring and Evaluating for Development Results (2009). United Nations Development Programme. New York, NY.
- Hanschel, E., & Monnin, P. (2005). Measuring and forecasting stress in the banking sector: evidence from Switzerland. BIS Papers, 22, 431-449.
- Hartmann, P., Straetmans, S., & de Vries, C. (2005). Banking system stability: Acrossatlantic perspective (NBER Working Paper. No. 11698).
- Hawkesby, C. (2000). Maintaining financial system stability: the role of macroprudential indicators. Reserve Bank of New Zealand Bulletin, 63(2), 38-52.
- Jahn, N., & Kick, T. K. (2012). Early Warning Indicators for the German Banking System: A Macroprudential Analysis. Bundesbank Discussion Paper, 27.
- Jili, N. N., & Mthethwa, R. M. (2016). Challenges in implementing monitoring and evaluation (M&E): the case of the Mfolozi Municipality. African Journal of Public Affairs, 9(4), 102-113.
- Kočišová, K. (2014). Banking Stability Index: A Cross-Country Study. In Proceedings of the 15th International Conference on Finance and Banking (pp. 197-208). Praha, Czech Republic.
- Kozaric, K., & Zunic, E. (2014). Financial soundness indicators in Bosnia and Herzegovina banking sector. UTMS Journal of Economics, 5(2), 159-168.
- Kusek, J. Z., & Rist, R. C. (2004). Ten Steps to a Results-based Monitoring and Evaluation System. Washington, DC: World Bank.
- Laker, J. F. (1999). Monitoring Financial System Stability. Reserve Bank of Australia Bulletin, 1-13.
- Lesik, V. O. (2017). Оцінювання фінансової стабільності банківської системи з урахуванням властивості емерджентності [Otsiniuvannia finansovoi stabilnosti bankivskoi systemy z urahuvanniam vlastyvosti emerdzhentnosti]. Business Inform, 3, 294-301.
- Lesik, V. O. (2017). Удосконалення методичного інструментарію діагностування кризових явищ у банківській системі в процесі моніторингу її фінансової стабільності [Udoskonalennia metodychnoho instrumentariiu diahnostuvannia kryzovykh yavyshch u bankivskii systemi v protsesi monitorynhu yii finansovoi stabilnosti]. Visnyk Odeskoho Natsionalnoho Universytetu, 7(60), 22.
- Mörttinen, L., Poloni, P., Sandars, P., & Vesala, J. (2005). Analysing banking sector conditions – how to use macro-prudential indicators (ECB Occasional Paper, No. 26).
- National Bank of Ukraine (2017). Banking system indicators.
- Petrovska, M., & Mucheva-Mihajlovska, E. (2013). Measures of Financial Stability in Macedonia. Journal of Central Banking Theory and Practice, 2(3), 85-110.
- Polius, T., & Sahely, L. (2011). Monitoring Banking Sector Soundness in the Eastern Caribbean Currency Union: A Multivariate Data Analysis Approach. International Research Journal of Applied Finance, 2(2), 110-143.
- Popovska, J. (2014). Modeling financial stability: the case of the banking sector in Macedonia. Journal of Applied Economics and Business, 2(1), 68-91.
- Rahim, S. R. N., & Zakaria, R. (2013). Comparison on Stability Between Islamic and Conventional Banks in Malaysia. Journal of Islamic Economics, Banking and Finance, 9(3), 131-149.
- Ramskyi, A., Loiko, V., Sobolieva-Tereshchenko, O., Loiko D. & Zharnikova, V. (2017). Integration of Ukraine into the European banking system: cleaning, rebooting and Basel III. Banks and Bank Systems, 12(4), 163-174.
- Ryan, E. (2017). The Role of Macroprudential Indicators in Monitoring Systemic Risk and Setting Policy. Quarterly Bulletin Articles, Central Bank of Ireland, 2, 62-80.
- Sales, A., Areosaz, W., & Areosax, M. (2012). Some Financial Stability Indicators for Brazil. (Working Paper Series, No. 287, 1-22).
- Sarlin, P. (2010). Visual monitoring of financial stability with a self-organizing neural network. In Proceedings of the 10th IEEE International Conference on Intelligent Systems Design and Applications (ISDA’10), Cairo, Egypt, 28-30 November, 248-253.
- Sere-Ejembi, A., Udom, Ini S., Salihu, A., Atoi, Ngozi V., Yaaba, & Baba, N. (2014). Developing Banking System Stability Index for Nigeria. CBN Journal of Applied Statistics. The Central Bank of Nigeria, 5(1), 49-77.
- Shar, A. H. (2010). Performance Evaluation of Banking Sector in Pakistan: An Application of Bankometer. International Journal of Business and Management, 5(8), 81-86.
- Sinenko, N., & Lielkalne, O. (2015). Cobweb diagram as a tool for assessing changes in the most important financial stability risks. Discussion Papers, 1.
- Slav’yuk, R., Shkvarchuk, L., & Kondrat, I. (2017). Financial market imbalance: reasons and peculiarities of occurrence in Ukraine. Investment Management and Financial Innovations, 14(1-1), 227-235.
- Swamy, V. (2013). Banking System Resilience and Financial Stability – An Evidence from Indian Banking. Journal of International Business and Economy, 14(1), 87-117.
- Worrell, R. (2004). Quantitative Assessment of the Financial Sector: An Integrated Approach (IMF Working Papers No. WP/04/153). International Monetary Fund.
- Yahya, A. T., Akhtar, A., & Tabash, M. I. (2017). The impact of political instability, macroeconomic and bank-specific factors on the profitability of Islamic banks: an empirical evidence. Investment Management and Financial Innovations, 14(4), 30-39.
- Zahra, S. F., Ascarya, A., & Huda, N. (2018). Stability Measurement of Dual Banking System in Indonesia: Markov Switching Approach. Journal of Islamic Economics, 10(1), 25-53.