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

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.

Nexus between non‐performing loans and economic growth in emerging countries: Evidence from Turkey with wavelet coherence approach

“…Espinoza and Prasad (2010) study six Arab countries for 1995 and 2008 by using vector autoregressive (VAR) analysis and define that there is an inverse relationship between EG and NPLs. Similarly, Fainstein and Novikov (2011) use the vector error correction model (VECM) for the examination of Baltic countries between 1997 and 2009, and conclude that there is an increase in NPLs due to the underestimation of macroeconomic variables including EG. Also, De Bock and Demyanets (2012) perform structural panel VAR analysis to examine 25 emerging countries for the period between 1996 and 2010 and determine that a slowdown of EG is associated with higher NPLs.…”

Bank-specific and macroeconomic determinants of non-performing loans in the Republic of Macedonia: Comparative analysis of enterprise and household NPLs

“…With regard to the dependent variable, the empirical literature usually suggests usage of two indicators: the ratio of non-performing to total loans (Gasha & Morales, 2004;Jimenez & Saurina, 2006;Fain stein & Novikov, 2011;Festic, Kavkler, & Repina, 2011;Pestova & Mamonov, 2012;Castro, 2012), and the change of the status of nonperforming loans or credit losses Quagliariello, 2008 and2009). In addition, losses due to unrepaid loans are also used in exploring the credit risk, (Bikker & Hu, 2002;Pain, 2003;Pesola, 2005;Quagliariello 2007;Glogowski, 2008).…”

“…In addition, losses due to unrepaid loans are also used in exploring the credit risk, (Bikker & Hu, 2002;Pain, 2003;Pesola, 2005;Quagliariello 2007;Glogowski, 2008). This credit risk measure, however, often faces an identification problem, as a result of the different polices of managers in different banks during the credit cycle (Pestova & Mamonov, 2012) and its use is therefore more complicated (Fain stein & Novikov, 2011). Hence, the research usually focuses on the first two indicators (i.e., options that include non-performing loans).…”

What Explains Bank Risk? Evidence from An Emerging Economy

“…During recession and stagnation credit risk increases and hence banks become vulnerable. The converse is also true during periods where there is economic boom or growth (Fainstein & Novikov, 2011). Increased levels of inflation unfavorably affect the productivity of the banking sector as a result of cyclical downturns.…”