Page 139 - FREN-C2021 PROCEEDINGS

Understanding and quantifying this systemic risk is important in ensuring that our financial
institutions are adequately capitalised to withstand another financial crisis. Borio (2010) stated that
there are two classifications of the systemic risks addressed by a macroprudential policy that are time
dimension and cross-sectional dimension. The time dimension deal with how to aggregate risk in the
financial system evolves.

Systemic risk was a major contributor to the global financial crisis (GFC) from 2008 to 2009 and
companies that are facing this systemic risk problem are called “too big to fail.” To monitor systemic
risk, a wide range of macroprudential indicators is used in the previous studies such as indicators of
bank capital, bank’s performance, indicators of liquidity and indebtedness. Other than that the
indicators also cover both the domestic and international aspects of the financial system, and include
macro, micro and sectoral variables (Lim et al., 2011). The most important macroprudential policy
indicators that are used to monitor systemic risk are asset quality and liquidity indicators. According
to Ismail and Che Pa (2015), the most important sources of systemic risk in Islamic banks are credit
risk and liquidity risk. the indicators that can be used as a proxy to measure credit risk is the banks’
nonperforming loans to total loans (Lim et al., 2011).

This research aims to examine the relationship between the non-performing financing with different
macroeconomic variables and macroprudential policy tools and institutional factors for twenty (20)
selected countries from 2008 until 2017. These selected countries are Bahrain, Bangladesh, Brunei,
Egypt, Indonesia, Iran, Jordan, Kuwait, Lebanon, Malaysia, Nigeria, Oman, Pakistan, Palestine,
Qatar, Saudi Arabia, Sudan, Tunisia, Turkey and United Arab Emirates (UAE).

Literature Review
According to Nursechafia and Abduh (2014), one of the performance indicators used to measure a
bank’s stability is non-performing loans (NPL) for conventional banks or non-performing financings
(NPF) for Islamic banks. A bank’s stability can be measured by this ratio based on the bank’s
productive asset quality because it is often used as a proxy for asset quality and is intended to identify
problems with asset quality in the loan portfolio. A high level of this ratio would lead to potential
banking instability. A non-performing loan can be defined as a loan where the borrower is not making
interest payments or repaying any principal. Rulyasri, Achsani and Mulyati (2017) stated that Non-
Performing Loans (NPL) are one of the main performance ratios that are generally used by banks to
measure their ability to cover failed risks (defaults) based on debtor loan refunds. When the loan
becomes a bad debt, it is classified as non-performing by the bank depending on local regulations
(Waemustafa & Sukri, 2015). Normally, the actions taken by banks regarding this problem are to set
aside money to cover potential losses on loans (loan loss provisions) and to write off bad debt in their
profit and loss account. This macroprudential indicator is calculated by using the value of non-
performing loans (NPLs) as the numerator and the total value of the loan portfolio (including NPLs,
and before the deduction of specific loan-loss provisions) as the denominator.

Gross Domestic Product (GDP) Growth Rate and Non-Performing Financing
Numaningtyas and Puwohandoko (2018) studied the effect of gross domestic product, inflation,
interest rate, profitability and capital adequacy ratio on the non-performing loans of several banks
covering the period from 2012 to 2015 The analytical method used in their study is the linear multiple
regression analysis and The research results showed that GDP negative effect on NPLs, and that the
economy will increase the value of NPLs. According to Leka, Bajrami and Duci (2019), the GDP
growth rate hurts NPLs. When a country has stable economic growth, the economic agents have much
more potential to settle the financial obligations, affecting both the reduction of current NPLs and the
potential of creating new NPLs. Tomak (2013) used the 2003 to 2012 data on a sample of 18 Turkish
commercial banks to identify the macroeconomic variables that determine the bank’s lending
behaviour. The study found that GDP has an insignificant impact on the bank`s lending behaviour.
Similarly, Swamy (2012) examined the macroeconomic and indigenous factors that influence NPLs in

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