Factors affecting sensitivity of commercial banks to bank run in the Visegrad Countries
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Date
2017-10-02
Journal Title
Journal ISSN
Volume Title
Publisher
Technical university of Liberec, Czech Republic
Technická Univerzita v Liberci
Technická Univerzita v Liberci
Abstract
While managing liquidity, each bank should be prepared also for unexpected and exceptional events, such as bank runs. The aim of this paper is therefore to determine the maximum volume of deposits that can be withdrawn from individual banks operating in the Visegrad countries and to identify the determinants of their sensitivity to a bank run. The data cover the period from 2000 to 2014. Although bank liquidity, measured by the liquid asset ratio, decreased in all countries during the analyzed period, the level of liquidity differs among countries. We have simulated a bank run as a sudden withdrawal of 20% of client deposits. The ability of individual banks to survive this crisis scenario significantly differs. Nevertheless, as Czech and Hungarian banks were more liquid, they are better prepared for a potential bank run than Polish and Slovak banks. After that, using the panel data regression analysis, we tested seven bank-specific factors and seven macroeconomic factors. The sensitivity of commercial banks from the Visegrad countries to a possible bank run is determined mainly by different aspects of bank liquidity (not only the level of bank liquidity, but also connection to bank lending activity, the way of its financing and also activity on the interbank market). Among the other bank specific factors, profitability, capital adequacy and size of the banks are relevant in some countries. When it comes to macroeconomic factors, interest rate and unemployment rate are important. However, we can conclude that the most important factor is the level of bank liquidity: banks with a sufficient buffer of liquid assets are safer than other banks, particular during periods of financial distress.
Description
Subject(s)
bank run, liquid asset ratio, scenario analysis, panel data regression analysis
Citation
ISSN
1212-3609