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Article

Issue:2023 №1 (81)
Section:Finances, accounting and taxation
UDK:030
DOI:https://doi.org/10.33271/ebdut/81.062
Article language:English
Pages:62-67
Title:Financial crisis probability measurement model
Author:Mshvidobadze T. I., Gori State University
Annotation:Methods. The research used the method of analysis and synthesis – to clarify the nature of modern financial crises, the method of grouping – to determine the types of financial crises, general and specific – to differentiate between different types of financial crises, econometric methods – to quantify the level of systemic risk in the financial sector that leads to the financial crisis. Results. Excessive credit growth, the main cause of financial crises, is reflected in the insufficient capitalization of the financial sector. The paper briefly reviews the theoretical and empirical studies on the developments in these markets around the financial crisis. Market-based measures of systemic risk, such as SRISK, which stands for systemic risk, allow monitoring of how such vulnerabilities emerge and progress in real time. Novelty. This paper presents a quantitative assessment of the level of systemic risk in the financial sector that leads to a financial crisis. The model builds on the theory that deleveraging will have a price impact and the greater the magnitude of the deleveraging, the more dangerous the adjustment. In its most extreme case, the real economy has restricted access to credit as the financial sector experiences a fire sale, thus endogenously generating a financial crisis. Practical value. In an econometric framework, the relationship between SRISK and severity of financial crisis for different developed countries is given. The paper focuses on financial crises characterized by disruptions in credit supply, the lower tail of which may be related to various factors. A report on the probability of a financial crisis is provided in real-time from an indication of excessive credit growth. The study shows the important role of the cross-border external effect of financial noncapitalization. 
Keywords:Financial crisis, Measure, Method SRISK, Macroeconomic, Tobit model, Currency
File of the article:EV20231_062-067.pdf
Literature:
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  • 2. Romer, C.D., and Romer, D.H. (2017). New evidence on the impact of financial crises in advanced countries. Am. Econ. Rev. 107, 3072-3118. https://doi.org/10.1257/aer.20150320
  • 3. Jordà, O., Schularick, M., and Taylor, A.M. (2011). Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons. IMF Economic Review, Palgrave Macmillan, vol. 59(2), pages 340-378, June. https://doi.org/10.1057/imfer.2011.8
  • 4. Laeven, L., and Valencia, F. (2013). Systemic Banking Crises. S. Claessens, M. A. Kose, L. Laeven, and F. Valencia, eds. Financial Crises: Causes, Consequences, and Policy Responses, forthcoming, IMF.
  • 5. Claessens, S., Kose, M.A., and Terrones, M.E. (2009). What Happens during Recessions, Crunches and Busts? Economic Policy, Vol. 60, pp. 653-700. https://doi.org/10.1111/j.1468-0327.2009.00231.x
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  • 7. Hutchison, M.M., and Noy, I. (2006). Sudden Stops and the Mexican Wave: Currency Crises, Capital Flow Reversals and Output Loss in Emerging Markets. Journal of Development Economics, Vol. 79, pp. 225-248. https://doi.org/10.1016/j.jdeveco.2004.12.002
  • 8. Engle, R.F. (2016). Dynamic conditional beta. J. Financ. Econom. 14, 643-667. https://doi.org/10.1093/jjfinec/nbw006
  • 9. Englea, R., and Ruanb, T. (2019). Stern School of Business, New York University, New York, NY 10012; and bNUS Business School, National University of Singapore, Singapore 119245.
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