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Finance and Credit
 

Modelling a bank loan loss provision: a panel data aspect

Vol. 21, Iss. 21, JUNE 2015

PDF  Article PDF Version

Available online: 10 June 2015

Subject Heading: Banking

JEL Classification: 

Pages: 44-56

Kazakova K.A. Astrakhan State University, Astrakhan, Russian Federation
kristinakazakova0309@gmail.com

Importance In conditions of financial and economic instability, the issues of warning and mitigating banking risks are very popular in the banking theory and practice. Creating a bank loan loss provision is one of the methods to prevent banking risks arising from borrowers' default.
     Objectives The purpose of the study is to assess the efficiency of methods for creating a bank loan loss provision and to define a rational approach to the system of allocation to the provision.
     Methods
The paper offers an alternative approach to estimating a bank loan loss provision. The econometric analysis of panel data on past due loans of fifty Russian banks depending on certain characteristics of the banks' operations was a basis for the considered approach. The research demonstrates building panel regressions and calculating projected values of past due loans.
     Results Practical realization of the econometric model of past due debt on loans of banking institutions presented the results of retrospective forecasting, which exceed the real values of past due debt. In most cases, the values of the generated forecasts appeared to be much less than the real values of allocations to the provision for corresponding losses.
     Conclusions and Relevance The comparative analysis of creating a loan loss provision revealed the inefficiency of the standard methodology for allocations to the reserve and defined the importance of quantitative methods for bank risk assessment based on mathematical models, which may serve as a basis for evaluating the probability of crisis events occurrence for a bank.

Keywords: bank, reserve, provision, credit indebtedness, loan, past due debt, bank characteristics, panel data, modeling, forecasting

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