ECONOMETRIC MODELING OF THE BANK’S SHORT-TERM LIQUIDITY DYNAMICS BASED ON MULTI-FACTOR REGRESSION

The article develops theoretical and methodological aspects of the predicative analysis of liquidity in the banking sector in the context of diversity and divergence of factors affecting the liquidity position of modern banks. The aim of the work is to study the dependence of short-term liquidity of a bank on a number of economic indicators based on econometric modeling. We used a systematic analytical approach to the study of liquidity formation factors and methods of comparative data analysis. The key methodological instrument of the empirical part of the study is multivariate correlation and regression analysis based on the parametric choice of performance indicators of a large bank in the Ural region. This study did not confirm the dominant thesis about the negative impact of excess liquidity on the profitability of commercial banks. Thus, a comparative analysis of the liquidity ratio and return on assets of Russian banks of various sizes for the period 2015-2019 did not reveal the existence of an explicit inverse-proportional relationship between liquidity and profitability. The analysis of pair correlations also did not reveal a close relationship between the value of current liquidity ratio and the bank’s operating profit. The authors noted that small-sized banks are able to demonstrate a combination of high liquidity and high profitability, while larger banks may not achieve such results. Consequently, the scale of the bank’s activities is not the determining factor in liquidity but indirectly provides quick access to funding sources. The authors have constructed regression models of the dependence of the mandatory current liquidity ratio of a bank on a number of performance indicators, making it possible to carry out a forecast-analytical assessment of liquidity for the short term outlook. It is established that own capital, short-term liabilities and overdue loan debt have a significant impact on the dynamics of the bank’s liquidity. The proposed approach can be applied by analytical departments of commercial banks in the development of predicative models for the monitoring the liquid position.