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The role of macroeconomic determinants in credit risk measurement in transition country: Estonian example

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  • Grigori Fainstein
  • Igor Novikov
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    Abstract

    Purpose – The purpose of this article is to investigate empirically the influence of macroeconomic and real estate market variables on the level of non-performing loans. A secondary goal is to analyse the effect of constant loan portfolio growth on the level of non-performing loans. Design/methodology/approach – The Vector Error Correction Model is applied. Findings – The research indicates that the most significant reason for the growth of non-performing loans was caused by the changes in the real GDP. The increasing influence of rapid loan portfolio growth proves the assumption that banks' credit risk management policies underestimated the changes in the macroeconomic variables during the analysed periods. Rapid growth of the real estate market played an important role, but it was not as crucial as it has been previously assumed. Practical implications – Developed an innovative approach of credit risk analysis that can be used for forecasting banks' financing activity. Originality/value – There has been developed and tested empirically an innovative approach of credit risk analysis based on the combined influence of different credit risk determinants.

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    Bibliographic Info

    Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Transitions and Innovation Systems.

    Volume (Year): 1 (2011)
    Issue (Month): 2 ()
    Pages: 117-137

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    Handle: RePEc:ids:ijtisy:v:1:y:2011:i:2:p:117-137

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    Web page: http://www.inderscience.com/browse/index.php?journalID==160

    Related research

    Keywords: non-performing loans; banking system; credit risk determinants; vector error correction model; VECM; Estonia; macroeconomics; real estate; market variables; constant loan portfolios; GDP; credit risk management; risk assessment; transition economies.;

    References

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    1. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    2. Pesaran, M. H. & Shin, Y., 1997. "Generalised Impulse Response Analysis in Linear Multivariate Models," Cambridge Working Papers in Economics 9710, Faculty of Economics, University of Cambridge.
    3. Marcucci, Juri & Quagliariello, Mario, 2009. "Asymmetric effects of the business cycle on bank credit risk," Journal of Banking & Finance, Elsevier, vol. 33(9), pages 1624-1635, September.
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    18. Juri Marcucci & Mario Quagliariello, 2008. "Credit risk and business cycle over different regimes," Temi di discussione (Economic working papers) 670, Bank of Italy, Economic Research and International Relations Area.
    19. Santiago Fernández de Lis & Jorge Martínez Pagés & Jesús Saurina, 2000. "Credit Growth, Problem Loans and Credit Risk Provisioning in Spain," Banco de Espa�a Working Papers 0018, Banco de Espa�a.
    20. Peter Winker, 2000. "Optimized Multivariate Lag Structure Selection," Computational Economics, Society for Computational Economics, vol. 16(1/2), pages 87-103, October.
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    Cited by:
    1. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.

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