Relationship Between Business Models of Banks and Stability of Economic Development

Depending on the chosen business model, banks can act as both shock absorbers and crisis catalysts. In this regard, the analysis of the relationship between banks’ business models and financial cycles becomes a useful tool for diagnosing and predicting crisis phenomena. The purpose of the research is to identify the relationship between the volume of debt of the banking and the debt burden of the economy. The research uses econometric methods. The key result of the research is two new econometric models, which were calibrated for the Russian economy. The models differ from each other by the types of bank liabilities used in the calculation of independent variables. The models also differ from the existing models by the calculation algorithm of independent variables. The source of information is the official statistics of the Bank of Russia for the period 2008–2019. The tests of the models confirmed the presence of a statistically significant cointegration relationship between the debt burden of the banking sector and the debt burden of the economy. Coupling coefficients in the models are identified as debt multipliers of the banking sector and characterize the multiplier effect of changes in the debt burden of banks. For the model containing banks’ balance sheet liabilities, the debt multiplier for the Russian economy was 6.7; and for the model using banks’ total liabilities was 3.1. The developed models are easy-to-use for forecasting financial cycles.

1. Roengpitya R., Tarashev N. A., Tsatsaronis K., Villegas A. Bank business models: Popularity and performance. BIS Working Papers. 2017;(682). URL: https://www.bis.org/publ/work682.pdf

2. Kok C., More C., Petrescu M. Recent trends in Euro area banks’ business models and implications for banking sector stability. Financial Stability Review. 2016;(May):130–143. URL: https:/www.ecb.europa.eu/pub/pdf/fsr/art/ecb.fsrart201605_03.en.pdf

3. Tran D. V., Hoang K., Nguyen C. How does economic policy uncertainty affect bank business models? Finance Research Letters. 2021;39:101639. DOI: 10.1016/j.frl.2020.101639

4. Cavelaars P., de Haan J., Hilbers P., Stellinga B. Challenges for financial sector supervision. DNB Occasional Studies. 2013;11(6):1–62. URL: https://www.dnb.nl/media/u2pjevcp/201312_nr-_6_-2013-_challenges_for_financial_sector_supervision.pdf

5. Tanzi P. M., Aruanno E., Suardi M. A European banking business models analysis: The investment services case. Journal of Financial Regulation and Compliance. 2018;26(1):35–57. DOI: 10.1108/JFRC-04–2016–0028

6. Karkowska R. Business model as a concept of sustainability in the banking sector. Sustainability. 2020;12(1):111. DOI: 10.3390/su12010111

7. Farnè M., Vouldis A. T. Banks’ business models in the Euro area: A cluster analysis in high dimensions. Annals of Operations Research. 2021;305(1–2):23–57. DOI: 10.1007/s10479–021–04045–9

8. Blundell-Wignall A., Atkinson P., Roulet C. Bank business models and the Basel system: complexity and interconnectedness. OECD Journal: Financial Market Trends. 2014;2013(2):43–68. DOI: 10.1787/fmt-2013–5jzb2rhkd65b

9. Abuselidze G. The impact of banking competition on economic growth and financial stability: An empirical investigation. European Journal of Sustainable Development. 2021;10(1):203. DOI: 10.14207/ejsd.2021.v10n1p203

10. Mergaerts F., Vander Vennet R. Business models and bank performance: A long-term perspective. Journal of Financial Stability. 2016;22:57–75. DOI: 10.1016/j.jfs.2015.12.002

11. Ayadi R., De Groen W., Sassi I., Mathlouthi W., Rey H., Aubry O. Banking business models monitor 2015: Europe. Montréal: International Research Centre on Cooperative Finance; 2016. 125 p. URL: https://www.ceps.eu/wp-content/uploads/2016/01/Banking-Business-Models-Monitor-Europe-2015.pdf

12. Brunnermeier M. K., Pedersen L. H. Market liquidity and funding liquidity. The Review of Financial Studies. 2009;22(6):2201–2238. DOI: 10.1093/rfs/hhn098

13. Olszak M., Roszkowska S., Kowalska I. Do macroprudential policy instruments reduce the procyclical impact of capital ratio on bank lending? Cross-country evidence. Baltic Journal of Economics. 2019;19(1):1–38. DOI: 10.1080/1406099X.2018.1547565

14. Noreen U., Alamdar F., Tariq T. Capital buffers and bank risk: Empirical study of adjustment of Pakistani banks. International Journal of Economics and Financial Issues. 2016;6(4):1798–1806. URL: https://www.econjournals.com/index.php/ijefi/article/view/3077/pdf

15. Bui C., Scheule H., Wu E. The value of bank capital buffers in maintaining financial system resilience. Journal of Financial Stability. 2017;33:23–40. DOI: 10.1016/j.jfs.2017.10.006

16. Hodbod A., Huber S. J., Vasilev K. Sectoral risk-weights and macroprudential policy. Journal of Banking & Finance. 2020;112:105336. DOI: 10.1016/j.jbankfin.2018.04.015

17. Dong F., Xu Z. Cycles of credit expansion and misallocation: The good, the bad and the ugly. Journal of Economic Theory. 2020;186:104994. DOI: 10.1016/j.jet.2020.104994

18. Virtanen T., Tölö E., Virén M., Taipalus K. Can bubble theory foresee banking crises? Journal of Financial Stability. 2018;36:66–81. DOI: 10.1016/j.jfs.2018.02.008

19. Gadzo S. G., Kportorgbi H. K.; Gatsi J. G. Credit risk and operational risk on financial performance of universal banks in Ghana: A partial least squared structural equation model (PLS SEM) approach. Cogent Economics & Finance. 2019;7(1):1589406. DOI: 10.1080/23322039.2019.1589406

20. Bressan S. The funding of subsidiaries equity, double leverage, and the risk of bank holding companies. Journal of Business Finance & Accounting. 2018;45(1–2):209–231. DOI: 10.1111/jbfa.12288

21. Cociuba S. E., Shukayev M., Ueberfeldt A. Managing risk taking with interest rate policy and macroprudential regulations. Economic Inquiry. 2019;57(2):1056–1081. DOI: 10.1111/ecin.12754

22. King M. Banking: from Bagehot to Basel, and back again. Speech by Mr. Mervyn King, Governor of the Bank of England, at the Second Bagehot Lecture, Buttonwood Gathering, New York, 25 October 2010. URL: https://www.bis.org/review/r101028a.pdf

23. Mankart J., Michaelides A., Pagratis S. Bank capital buffers in a dynamic model. Financial Management. 2020;49(2):473–502. DOI: 10.1111/fima.12253

24. de Haan J. Fang Y., Jing Z. Does the risk on banks’ balance sheets predict banking crises? New evidence for developing countries. International Review of Economics & Finance. 2020;68:254–268. DOI: 10.1016/j.iref.2020.03.013

25. Jarmuzek M., Rozenov R. Excessive private sector leverage and its drivers: Evidence from advanced economies. Applied Economics. 2019;51(34):3787–3803. DOI: 10.1080/00036846.2019.1584383

26. Schoenmaker D., Wierts P. Macroprudential supervision: From theory to policy. National Institute Economic Review. 2016;235(1):50–62. DOI: 10.1177/002795011623500115

27. Kaya E., Koksal Y. Leverage and procyclicality: An application on banking sector. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2019;14(2):331–346. (In Turk.). DOI: 10.17153/oguiibf.451134

28. Gross M., Henry J., Semmler W. Destabilizing effects of bank overleveraging on real activity — an analysis based on a threshold MCS-GVAR. Macroeconomic Dynamics. 2018;22(7):1750–1768. DOI: 10.1017/S 1365100516001024

29. Drehmann M., Juselius M. Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting. 2014;30(3):759–780. DOI: 10.1016/j.ijforecast.2013.10.002

30. Engle R. F., Granger C. W.J. Co-integration and error correction: Representation, estimation, and testing. Econometrica. 1987;55(2):251–276. DOI: 10.2307/1913236