Modern methodology of metrics monitoring changes in bank lending conditions and credit demand levels

Issue 56, 2024

Raya Gulimbetova, Nazira Gumar, Azhar Nurmagambetova, Gaukhar Zhanibekova, Almagul Jumabekova

Received 22.12.2023, Revised 05.03.2024, Accepted 17.05.2024

https://doi.org/10.54919/physics/56.2024.36fur1

Abstract

Relevance. The relevance of the article lies in the fact that lending, being a vital aspect of a bank's operations, plays a crucial role as a major investment source. It fosters the ongoing and expedited reproduction process, bolstering the economic capabilities of businesses, holds a prominent position as the primary revenue-generating activity for the bank.

Purpose. The purpose of this study is to develop a modern methodology for monitoring changes in bank lending conditions and credit demand levels. The study aims to provide a comparative assessment of the current practices in analyzing lending fluctuations within the banking sectors of Kazakhstan, the Russian Federation, the USA.

Methodology. To address the analysis task outlined in the study and to identify opportunities for enhancing the methodology, a variety of general scientific methods were employed. These included system-functional, statistical, structural, factorial, logical, graphical methods.

Results. It is shown that the growth of lending in Kazakhstan is supported by demand in the regions, which necessitates the use of a highly sensitive model for calculating indicators reflecting alterations in the conditions of bank lending and demand for loans. The research results can be applied in solving the primary challenges within the industry of lending in Kazakhstan and other post-Soviet countries, and will improve the work of credit institutions, which is especially important after COVID-19 crisis.

Conclusions. This study highlights the critical importance of modernizing the methodology for monitoring changes in bank lending conditions and credit demand levels. Lending is a vital aspect of a bank's operations, serving as a major investment source and a primary revenue-generating activity. Despite its significance, the full potential of bank lending has not been fully explored. This research provides a comparative assessment of current practices in analyzing lending fluctuations within the banking sectors of Kazakhstan, the Russian Federation, the USA.

Keywords: demand for credit; bank; financial resources; lending conditions; region

Suggested citation

Gulimbetova R, Gumar N, Nurmagambetova A, Zhanibekova G, Jumabekova A. Modern methodology of metrics monitoring changes in bank lending conditions and credit demand levels. Sci Herald Uzhhorod Univ Ser Phys. 2024;(56):361-370. DOI: 10.54919/physics/56.2024.36fur1

Download article

References

  1. Niepmann F, Schmidt-Eisenlohr T. Institutional investors, the dollar, and U.S. credit conditions. Int Fin Disc Pap. 2019;2019:1246.
  2. Gambacorta L, Marqués-Ibanez D. The bank lending channel: Lessons from the crisis. Frankfurt am Main: European Central Bank; 2011.
  3. Kapounek S, Zuzana K, Jarco F. Lending conditions in EU: The role of credit demand and supply. Econ Model. 2017;67(C):285-293.
  4. Adrian T, Etula E. Financial intermediaries and the cross-section of asset returns. J Fin. 2014;69(6):2557-2596.
  5. Puri M, Rocholl J, Steffen S. Global retail lending in the aftermath of the U.S. financial crisis: distinguishing between supply and demand effects. J Fin Econ. 2011;100(3):556-578.
  6. Byrne D, Holton S, Parle C. COVID-19: Bank credit conditions and monetary policy. 2020. https://www.centralbank.ie/docs/default-source/publications/economic-letters/vol-2020-no-5-covid-19-bank-credit-conditions-and-monetary-policy-(byrne-holton-and-parle).pdf.
  7. Dosmagambet Y. Financial system, and SME financing in Kazakhstan. ADBI Work Pap Ser. 2018;904. https://www.econstor.eu/handle/10419/222671.
  8. Zholamanova M. The current state of the credit market of the Republic of Kazakhstan. Revista. 2017;38(29):3. https://www.revistaespacios.com/a17v38n29/a17v38n29p33.pdf.
  9. Hagenow AV, Karamzin AS, Chekmareva EN. Analysis and monitoring conditions of bank lending. Mon Cred. 2010;10:16.
  10. Bassett WF. Changes in bank lending standards and the macroeconomy. J Mon Econ. 2014;62(C):23-40.
  11. Turk RA, Beaumont C. Negative interest rates: How big a challenge for large Danish and Swedish banks? IMF Work Pap. 2016;198:A001.
  12. Altavilla C, Boucinha M, Holton S, Ongena S. Credit supply and demand in unconventional times. ECB Work Pap Ser. 2018;2202:18.
  13. Albertazzi U, Marchetti D. Credit supply, flight to quality and evergreening: an analysis of bank-firm relationships after Lehman. Temi di disc. 2010;756. https://ideas.repec.org/p/bdi/wptemi/td_756_10.html.
  14. Weinbach G. The federal reserve’s senior loan officer opinion survey. Basel: BIS; 2009.
  15. Jimenez G. Do demand or supply factors drive bank credit, in good and crisis times? 2017. https://crei.cat/wp-content/uploads/2018/02/Peydro_Do-Demand.pdf.
  16. Berrospide JM, Edge RM. The effects of bank capital on lending: What do we know, and what does it mean? Washington: Federal Reserve Board; 2010.
  17. Niepmann F, Schmidt-Eisenlohr T. Foreign currency loans and credit risk: Evidence from U.S. banks. CESifo Work Pap Ser. 2017;6700. https://ideas.repec.org/p/ces/ceswps/_6700.html.
  18. Jimenez G, Ongena S, Peydro J, Saurina J. Hazardous times for monetary policy: what do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking? Econometrica. 2014;82(2):463-505.
  19. Askar A, Sunggat M, Maxat M, Nunzhigit S, Kanat Z, Ainur K, Arnay A, Asset O. Investigation of the Efficiency of the Ozonator in the Process of Water Purification Based on the Corona Discharge. J Eco Eng. 2023;24(2):140-151.
  20. Londono J, Zhou H. Variance risk premiums and the forward premium puzzle. J Fin Econ. 2017;124(2):415-440.
  21. Zinaida S, Igor F, Nelia C, Roman C, Oleksii K, Oleksandr T. Blockchain Technologies in the Conditions of Digitalization of International Business. Lect Not Network Syst. 2023;621:796-804.
  22. Shleifer A, Vishny RW. Unstable banking. J Fin Econ. 2010;97:306-318.
  23. Banking sector of Kazakhstan under COVID-19 crisis. 2021. Asian Development Bank. https://www.adb.org/sites/default/files/institutional-document/654971/banking-sector-kazakhstan-covid-19-crisis.pdf.
  24. Piera MA, Buil R, Ginters E. State space analysis for model plausability validation in multi agent system simulation of urban policies. In: 25th European Modeling and Simulation Symposium, EMSS 2013 (pp. 504-509). 25-27 September, Athens, Greece; 2013.
  25. Buil R, Piera MA, Ginters E. Multi-agent system simulation for urban policy design: Open space land use change problem. Int J Model Simul Sci Comp. 2016;7(2):1642002.
  26. Mikhailov P, Ualiyev Z, Kabdoldina A, Smailov N, Khikmetov A, Malikova F. Multifunctional Fiberoptic Sensors For Space Infrastructure. East-Eur J Enter Tech. 2021;5(5-113):80-89.
  27. Parkhomets M, Uniiat L, Chornyi R, Chorna N, Hradovyi V. Efficiency of production and processing of rapeseed for biodiesel in Ukraine. Agricult Res Econ. 2023;9(2):245-275.
  28. Denysiuk O, Svitlyshyn I, Tsaruk I, Vikarchuk O, Dankevych A. Diversification in the enterprises' activities for sustainable development in the agricultural sector. Riv Stud sull Sostenib. 2022;2022(2):85-102.
  29. Dankevych A, Dankevych V, Levchenko Y. EU integration and the business efficiency of the quality control system of dairy products: The dilemma of ukrainian enterprises. In: Recent Trends in Business and Entrepreneurial Ventures (pp. 61-83). Hauppauge, New York: Nova Science Publishers; 2023.
  30. Krasilenko VG, Nikolsky AI, Zaitsev AV, Voloshin VV. Optical pattern recognition algorithms on neural-logic equivalental models and demonstration of their prospectiveness and possible implementations. Proceed SPIE - The Int Soc Optic Eng. 2001;4387:247-260.
  31. Syzdykbayeva NB, Baidybekova SK, Otelbay SK, Appakova GN, Abdykalyk SYe. Improving Food Supply through Modernisation of the Agri-Food Complex in Kazakhstan - a Country Study. Int J Food Syst Dyn. 2023;14(3):345-350.
  32. Kozlovska TI, Kolisnik PF, Zlepko SM, Titova NV, Pavlov VS, Wójcik W, Omiotek Z, Kozhambardiyeva M, Zhanpeisova A. Physical-mathematical model of optical radiation interaction with biological tissues. Proceed SPIE - The Int Soc Optic Eng. 2017;10445:104453G.
  33. Andriievskyi I, Spivak S, Gogota O, Yermolenko R. Application of the regression neural network for the analysis of the results of ultrasonic testing. Mach Energ. 2024;15(1):43-55.
  34. Borio C, Gambacorta L. Monetary policy and bank lending in a low interest rate environment: diminishing effectiveness? BIS Work Pap. 2017;612. https://www.bis.org/publ/work612.pdf.
  35. Gambacorta L, Rossi C. Modelling bank lending in the euro area: A non-linear approach. SSRN Electron J. 2010;20(14):1099-1111.
Make a Submission

Indexing

info@physics.uz.ua