Determining and predicting correlation of macroeconomic indicators on credit risk caused by overdue credit
-
DOIhttp://dx.doi.org/10.21511/bbs.13(3).2018.11
-
Article InfoVolume 13 2018, Issue #3, pp. 114-119
- Cited by
- 1382 Views
-
350 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The banking system guarantees the economic strength of the country. Its sustainability is due to the sustainability of the credit portfolio. Therefore, scientific research on banking risks is always relevant. Basel recommendations and central bank regulations provide risk minimization in case of default of borrower by creating risk reserve, but the high range of macroeconomic factors creates a basis for creating credit risk. The model, which determines the risk factors, may be structurally the same, but the quality of the influence of factors is different in various countries. The influence of macroeconomic factors is particularly evident in developing countries. The impact of economic factors in different countries is high in GDP of these countries. The article focuses on determining the influence of macroeconomic factors on credit risk of systematic banks in Georgia. The coefficients of individual macroeconomic indicators are calculated by using Pearson’s correlation. The credit risk ratio is taken from the bank’s overdue credits and credit portfolio ratio. Based on the correlation coefficients obtained, the expected risk of shock changes is calculated.
- Keywords
-
JEL Classification (Paper profile tab)G21, G38
-
References13
-
Tables1
-
Figures4
-
- Figure 1. Credit risk of the Bank of Georgia (overdue credit 90+)
- Figure 2. Y – Credit risk, X – Unemployment rate
- Figure 3. Y – Credit risk, X – GDP
- Figure 4. Y – Credit risk
-
- Table 1. The result of the calculation of Pearson’s correlation
-
- Aver, B. (2008). An Empirical Analysis of Credit Risk Factors of the Slovenian Banking System. Managing Global Transitions, 6(3), 317-334.
- Belas, J., & Cipovova, E. (2011). Internal Model of Commercial Bank as an Instrument for Measuring Credit Risk of the Borrower in Relation to Financial Performance (Credit Score and Bankruptcy Models). Journal of Competitiveness, 3(4), 104-120.
- Butaru, F., Chen, Q., Clark, B., Das, S., Lo, A. W., & Siddique, A. (2016). Risk and risk management in the credit card industry. Journal of Banking and Finance, 7(2), 218-239.
- Chornous, G., & Ursulenko, G. (2013). Risk management in banks: new approaches. Ekonomika, 92(1), 120-132.
- Dimitriu, M., Oprea, I. A., & Scrieciu, M. A. (2012). Credit Risk Modeling using Multiple Regressions. International Journal of Advances in Management and Economics, 1(5), 125-131.
- Economic Policy Research Center (2014). Management of Non-Performing Loans in Georgia Analysis and Recommendations.
- Jakubik, P. (2007). Macroeconomic Environment and Credit Risk. Czech Journal of Economics and Finance, 57(1-2), 60-78.
- Musau, S., Muathe, S., & Mwangi, L. (2018). Financial Inclusion, GDP and Credit Risk of Commercial Banks in Kenya. International Journal of Economics and Finance, 10(3), 181-195.
- National Bank of Georgia (2007–2017). Annual Reports of the National Bank of Georgia.
- National Bank of Georgia (2014). Regulation on Risk Management in Commercial Banks.
- Nunes, T., & Rodrigues, P. (2010). Threshold effects in credit risk and stress scenarios. International Journal of Economics and Finance, 16(4), 393-407.
- Oniani, L., & Ghoghoberidze, T. (Eds.) (2017). Modern method of banking risk assessment. Proceedings from Second international conference Economic Development of Economic, Legal and Social Problems of Modern Development, Kutaisi, GE.
- Tong, X., Feng, Y., & Li, J. (2018). Neyman-Pearson classification algorithms and NP receiver operating characteristics. Science Advances, 4(2), 1-10.