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and macroeconomic performance of NPLs in Central, Eastern, and South-Eastern Europe (CESEE)
from 1998 to 2011 of 10 banks in every 16 countries by using fixed effect and random effect model.
The independent variables that are used in the study are credit growth rate, inflation, unemployment
rate and GDP growth rate. The empirical findings revealed that the credit growth rate has a negative
significant effect on NPLs.

The result is also supported by Espinoza and Prasad (2010) that explored the factors that influence
non–performing loans (NPLs) in the Gulf Cooperation Council (GCC) banking sector. They used
several methods of analysis (fixed effect model, difference GMM, and System GMM models) based
on panel data from a sample of 80 banks in GCC countries. The result revealed that there is an inverse
relationship between credit growth and NPL where higher credit reduces the NPL ratio. Meanwhile,
the coefficient value of the unemployment rate (UNR) had a positive impact on the non-performing
financing at a 1% significance level. This result proved that there was a positive and significant
relationship between the unemployment rate and non-performing financing. This result was in line
with the previously studied by Balogh (2012) and Makri et al. (2014) that showed that the
unemployment rate has a positive impact on banks non-performing loans. The increase in the
unemployment rate captures the economic cycles leading to an increase in the nonperforming loans
rate, as well as being considered an indicator of major economic imbalance and contributing to the
rise of banking vulnerabilities.

Similarly, Ahlem and Fathi (2013) conducted a study on the factors influencing non-performing loans
among 85 banks in Italy, Greece and Spain from 2004 to 2008. The macroeconomic variables that are
used in this study are GDP growth rate, unemployment rate and real interest rate. By applying the
Fixed Effect model, the empirical findings showed significant positive relationships between the
unemployment rate and NPLs. Unemployed customers cannot meet their commitments and repay the
loans which can increase the level of non-performing loans.

In terms of macroprudential policy elements, table 3 showed that for the first macroprudential policy
tool, the coefficient value of the natural logarithm of loan to value ratio (LTV) was significantly and
uniformly negative for the NPF at 1% level Second tool is Debt to income ratio (DTI) where the result
showed DTI had no relationship with NPF, meanwhile the coefficient value of the natural logarithm
reserve requirement (RR) had a negative impact on the non-performing financing (NPF) at 1%
significance level This result is in line with some previous studies that are showed Some macro-
prudential policies tools being effective in stabilising the banking system fragility. Other than that, for
mandate there are two indexes used CBINDEX and GOVINDEX where the coefficient value of
CBINDEX was significantly and uniformly negative for non-performing financing (NPF) at 1% level,
Meanwhile, the coefficient value of the GOVINDEX had a positive impact on Non-performing
financing at 10% significance level. For the second macroprudential policy institutional factor, the
coefficient value of the natural logarithm of transparency (TRANS) was significantly and uniformly
positive for non-performing financing. This result showed the institutional factors in the
macroprudential policy framework are effective for ensuring financial stability.

Conclusion
The main lesson of the Global financial crisis is the importance of mitigating systemic financial risks
and the need to strengthen the macroprudential approach to supervision and regulation that can
identify risks throughout the system and take appropriate actions to maintain stability. Key elements
of an effective macroprudential policy framework consist of a system of early warning indicators that
signal increased vulnerabilities to financial stability, a set of policy instruments that can help to
contain risks and institutional factors that can be used to ensure the effective identification of systemic
risks. Non-performing financing is one of the important macroprudential policy indicators to measure
a bank’s stability. The empirical evidence documented that there is a statistically significant
relationship between the NPF and macroeconomic variables such as the balance of payment, domestic

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