Abstract
Using an autoregressive distributed lag model, this paper examines the factors that influence the credit risk of the Bulgarian banking system over the decade 2001–2010, as measured by non-performing loans. Recent papers aim to identify the determinants of non-performing loans using a cross-country modelling framework. As the South East European region (SEE) is non-homogeneous, our analysis is country-specific and captures the timeline between the bank privatisation era up to the global financial crisis and the ensuing Greek crisis. The contribution of our paper is twofold: it uses the ARDL modelling framework that is scarcely employed in related studies but also investigates spillover effects from the Greek crisis in view of the material presence of Greek banks in Bulgaria. In accordance with previous studies, the findings suggest that the credit risk determinants of Bulgarian banks should be sought endogenously in macroeconomic variables and industry-specific factors but also in exogenous factors. We evidence a pronounced role of the global financial crisis and the country’s bank regulatory framework. The Greek debt crisis appears to play an immaterial role indicating that Greek banks have not been a Trojan horse in the Bulgarian banking system.
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Notes
As of end 2009, five Greek Banks together controlled 29 % of the sector’s assets (BNB 2009).
For instance, in early 2005, the Bulgarian National Bank (BNB) introduced credit ceilings whereby banks were allowed to expand credit by up to 6 % per quarter.
These models are used in the studies conducted by the Central Banks of the UK, Japan, Spain, the Netherlands, and by the European Central Bank.
The introduction of market variables such as interest rates, foreign exchange rates, equity and real estate price indices into credit risk models is a way of explicitly integrating the analysis of market and credit risks.
The VAR model is based on a transmission that includes the following nine endogenous variables: the real effective exchange rate, exports, monetary aggregate M2, imports, aggregate bank loans to clients, the unemployment rate, the consumer price index, the domestic real 3-month interest rate and the share of non-performing loans in aggregate bank loans to clients.
As long as the ARDL model is free of residual correlation, endogeneity is less of a problem. Pesaran and Shin (1999) demonstrated that the appropriate lags in the ARDL model correct for both serial correlation and endogeneity problems.
In accordance with the Bulgarian National Bank (BNB), the doubtful loans include all past-due loans - 91 to 180 days as well as these loans where the debtor’s financial standing has substantially deteriorated. Loss loans are defined as past-due loans over 181 days and credit exposures where there are valid grounds to consider that borrowers are in a permanent state of financial inability to repay.
The construction index, an indicator of construction production activity includes building construction (both residential and non-residential) and civil engineering (infrastructure construction) such as roads, telecoms and other type of construction.
Real estate surveys indicate that the property market in Bulgaria has weakened considerably after the global financial crisis. A sharp contraction in the construction sector could ignite a major economic downward spiral with adverse consequences for banks’ loan portfolios.
We refer to Type II banking crises as defined by Reinhart and Rogoff (2009). That is a milder banking crisis also known as financial distress where there are no runs, closure or large-scale government assistance of an important institution that marks the start of a string of similar outcomes for other financial institutions.
Another proxy used to capture the Greek crisis effect on the Bulgarian credit risk was the spread differential between Greek and German long-term bond yields (SPGRD) as can be viewed in Table 3 in the Appendix.
Based on data from the National Statistical Institute of Bulgaria (NSI), the influx of foreign funds in Bulgaria led to rapid construction and real-estate development, with property prices appreciating by over 50 %. Moody’s (2012) stresses that the construction and real-estate sector is the primary source of credit risk.
The results reported in this paper include only one of the two Greek-crisis proxies used, the LLPGR. Similar results were obtained when the proxy SPGRD was used instead of LLPGR, not presented here but available upon request.
The value of the F-statistic was 7.24 from the F-test that the coefficients of lagged variables are jointly zero and the lower and upper bounds at the 1 % significance level are (3.41, 4.68).
The VECM results, not presented here are available upon request.
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Acknowledgments
We are grateful to two anonymous referees of the journal as well as to the participants of the 5th International Conference ‘The Economies of Balkan and Eastern European Countries in the Changed World’, EBEEC 2013, 9–12 May 2013, Istanbul, Turkey for their constructive comments.
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Nikolaidou, E., Vogiazas, S.D. Credit Risk Determinants for the Bulgarian Banking System. Int Adv Econ Res 20, 87–102 (2014). https://doi.org/10.1007/s11294-013-9444-x
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DOI: https://doi.org/10.1007/s11294-013-9444-x