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Journal of Financial Stability 25 (2016) 4757

Contents lists available at ScienceDirect

Journal of Financial Stability

journal homepage: www.elsevier.com/locate/jfstabil

net stable funding ratio for Islamic banks and its impact on financialtability: An international investigation

awood Ashraf a,, Muhammad Suhail Rizwan b, Barbara LHuillier c

Islamic Research & Training Institute (A member of Islamic Development Bank Group), 8111 King Khalid Street, Jeddah 22332-2444, Kingdom of SaudirabiaNUST Business School, National University of Sciences and Technology (NUST), Islamabad, PakistanCollege of Business Administration, Prince Mohammad Bin Fahd University, Al Khobar 31952, Kingdom of Saudi Arabia

r t i c l e i n f o

rticle history:eceived 25 November 2015eceived in revised form 21 June 2016ccepted 29 June 2016vailable online 4 July 2016

eywords:

a b s t r a c t

The Islamic Financial Services Board (IFSB) is the standard setting body for the Islamic banking industry.The IFSB, while endorsing the Basel III accord, modified the criteria to calculate the Net Stable FundingRatio (NSFR) to cater for the unique aspects of the Islamic banking industry. In this paper, we calculatedthe modified NSFR of 136 Islamic banks from 30 jurisdictions between 2000 and 2013 and explored thepotential impact the requirements of this ratio has on the financial stability of Islamic banks after con-trolling for bank, country, and market-specific variables. The empirical findings suggest that the modified

slamic bankset stable funding ratioinancial stabilityegulatory framework

FSB

NSFR has a positive impact on the financial stability of Islamic banks during the sample period. However,the marginal impact of the NSFR on stability diminishes as the size of the bank increases. The resultsremained robust after applying an alternative measure of stability and using an alternative estimationmodel based on an instrumental variable approach. These results validate the use of the IFSBs modifiedNSFR for Islamic banks as a regulatory measure.

2016 Elsevier B.V. All rights reserved.

. Introduction

The 20072009 global financial crisis highlighted weaknesses inhe conventional banking system and drew attention to the successf the Islamic banking model (Hasan and Dridi, 2010). The Islamicanking sector grew at an exceptional compound annual growthate of 17% during the period 20082013. It now accounts for morehan a quarter of the total banking assets of 10 countries where the

ajority of the population is Muslim including five of the oil-richembers of the Gulf Cooperative Council (GCC)1 (Islamic Financial

ervices Board, 2015a).In response to weaknesses in the global financial system, the

asel Committee on Banking Supervision (BCBS) introduced twoew regulatory measures in the Basel III regulatory framework. One

s a liquidity coverage ratio (LCR) that focuses on the short-termiquidity of banks and the other is a net stable funding ratio (NSFR)hat aims to monitor the long-term funding stability of banks.

Corresponding author.E-mail addresses: [email protected] (D. Ashraf), [email protected]

M.S. Rizwan), [email protected] (B. LHuillier).1 GCC member countries include Saudi Arabia, Kuwait, Qatar, UAE, Bahrain, andman.

ttp://dx.doi.org/10.1016/j.jfs.2016.06.010572-3089/ 2016 Elsevier B.V. All rights reserved.

Although adoption of the Basel III accord is being phased in,it is expected that by 2019 all requirements will be fully imple-mented. The full impact of these new regulatory requirements onthe banking industry is still unknown. However, there is already agrowing literature assessing the potential impact these new reg-ulatory measures will have on the stability of conventional banks.This literature capitalizes on the argument that the newly intro-duced regulatory measures (NSFR and LCR) can be calculated usingexisting data and their potential impact on banks can be exploredretrospectively. Yan et al. (2012), using data from a sample of 11 UKbanks for the period 19972010, found that higher regulatory capi-tal requirements not only reduce the probability of a banking crisisbut also reduce the economic loss from a banking crisis. SimilarlyJiraporn et al. (2014), using data from a sample of 68 banks from 11East Asian countries for the period 20052009, reported an inverserelationship between the NSFR and risk-taking behavior of banks.King (2013), using data from a sample of banks from 15 countries,suggested that the implementation of the NSFR has adverse con-sequences for the economy due to the shrinking of banks balancesheets, changes in the composition of assets or maturity thereof.

The business model for Islamic banks is quite different fromthat of conventional banks in terms of their asset-liability struc-ture and product offering. The International Monetary Fund (IMF)(2011) suggested that the business model on which banks base their

dx.doi.org/10.1016/j.jfs.2016.06.010http://www.sciencedirect.com/science/journal/15723089http://www.elsevier.com/locate/jfstabilhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.jfs.2016.06.010&domain=pdfmailto:[email protected]:[email protected]:[email protected]/10.1016/j.jfs.2016.06.010

48 D. Ashraf et al. / Journal of Financial Stability 25 (2016) 4757

Table 1Islamic bank assets and liabilities and their conventional counterparts. Comparative haircuts given by Basel III for conventional banks and IFSB for Islamic banks are given inthe last two columns respectively. These haircuts are based on the authors understanding of quantitative guidelines for the calculation of the NSFR published by the IFSB(for Islamic banks) and Basel III for conventional banks.

Islamic product Conventionalcounterpart

Nature of thecontract forIslamic banks

Key features Haircut underBasel III

Haircut underIFSB

Qard-al-Hassan orwadiah

Current account Debt Resembles conventional deposits, althoughnon-interest/return bearing. May receive a gift(wadiah) from bank capital.

50% 50%

Qard-al-Hassan orwadiah

Saving deposits Debt Safekeeping and profit sharing of Islamic bank(deposit) contracts.

50% 50%

Profit-sharinginvestment accounts(PSIAs)

Saving and termdeposits

Equity Structured as profit/loss sharing partnerships(mudarbah or musharaka) or agency (wakalah)contracts.

95%

PSIA (Restricted) Saving and termdeposits

Quasi equity Funds provided by investors are invested per accountholders instructions and not comingled with banksown assets and so easier to trace and transfer toaccount holders. However, assets of all such accountholders may be pooled together, so traceability maystill be a challenge.

95% 0%

PSIA (Unrestricted) Saving and termdeposits

Hybrid Account holders give banks full discretion to invest inany Sharah compliant assets. May be comingled withbank assets or those of other account holders.Traceability to specific account holders may be achallenge.

95% 9095%

Sukuk Bonds and securitizedloans

Hybrids Islamic equivalent of conventional bonds. Structuredas certificates of participation through securitization ofspecific assets/pool of assets.

100%

Murabahah Loans and advances Debt A sales contract whereby the institution offeringIslamic financial services sells to a customer a specifiedkind of asset that is already in its possession. Sellingprice is the sum of the original price and an agreedprofit margin.

85% 85%

Musharaka Loans and advances Equity A contract between the institution offering Islamicfinancial services and a customer. Both wouldcontribute capital to an enterprise. Profits generatedby that enterprise or real estate assets are shared bythe terms of the Musharaka agreement. Losses areshared in proportion to each partners share of capital.

85% 50%

Ijarah Mortgages and leases Equity An agreement made by an institution offering Islamicfinancial services to lease an asset to a customer for anagreed period for a specified rental. An Ijarah contractcommences with a promise to lease that is binding onthe part of the potential lessee before entering theIjarah contract.

50%65% 50%

Qard-al-Hassan Loans and advances Debt An interest-free loan is given by a lender to a borrowerwith the stipulation that the latter pays back theprinciple only.

85% 0%

Salam and istisnaa Hybrid Hybrid Salam: Agreement to purchase, at a predeterminedprice a specified kind of commodity not currentlyavailable to the seller, to be delivered on a specifiedfuture date as per agreed specifications and specifiedquality.

85%

istisnaa: A contract of sale of specified objects to becturet of th

to the

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Islamic banking system risk is shared between the two (Hasan andDridi, 2010). Regulatory requirements under the BCBSs frameworkare based upon the underlying riskiness of banks and are designed

manufathe parobjects

perations has serious consequences for the stability of banks andhat prior to 2008 the NSFR of investment banks declined moreharply as compared to commercial banks. Furthermore, Mergaertsnd Vennet (2016) while examining the impact of bank businessodels on performance and risk of European banks found that

etail banks perform better in terms of profitability and stability anduggested that business model considerations should be more fun-amentally integrated in the regulatory and supervisory practices.eck et al. (2013) observed that Islamic banks are generally betterapitalized compared to conventional banks. The equity-based andisk-sharing nature of Islamic contracts helps reduce the maturity

ismatch of assets and liabilities and enhances financial stability.

The Islamic Financial Services Board (IFSB) is the standard-

etting body for the Islamic banking industry. The IFSB endorsed theasel III regulatory framework after making some adjustments for

d or constructed, with an obligation one manufacturer or builder to deliver the

customer upon completion.

the difference in the nature of assets and liabilities of Islamic banks.The IFSB issued Guidance Note No. 12 which provides guidelines forthe calculation of the NSFR for Islamic banks.2

Why is there a need for a modified NSFR for Islamic banks?Response to this very critical question centers on the treatment ofrisk under both banking systems. Under the conventional bankingsystem risk transfers from lenders to borrowers while under the

2 Guidance Note 12 on quantitative measures for liquidity risk management ininstitutions offering Islamic financial services [excluding Islamic insurance (Takaful)institutions and Islamic collective investment schemes] issued by the IslamicFinancial Services Board in 2014. Online: www.ifsb.org.

http://www.ifsb.orghttp://www.ifsb.orghttp://www.ifsb.org

nancial Stability 25 (2016) 4757 49

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D. Ashraf et al. / Journal of Fi

o adequately buffer levels of risk. However, this regulatory frame-ork cannot remain efficient and effective if its application does

ot take into account the risk-sharing nature of Islamic banks.The IFSBs modified NSFR takes into account the risk-sharing

ature of the underlying contracts of Islamic banks and modi-es regulatory requirements for the NSFR accordingly. Table 1rovides a detailed comparison of the BCBSs proposed NSFR foronventional banks and the IFSBs proposed modified NSFR forslamic banks. Major differences can be seen in regulatory require-

ents related to Profit-Sharing Investment Accounts (PSIA) androfit-Sharing Investment Accounts-Restricted (PSIA-R), Sukuk,usharaka, Salam, Istisnaa, and Qard-al-Hassan.3

Despite the apparent differences in the business models, therere no studies that have investigated the impact of the NSFR onhe stability of Islamic banks. From the sparse literature related tohe stability of Islamic banks Cihk and Hesse (2010) compared thenancial stability of Islamic banks with that of conventional bankssing a data set of 19 countries and found that small Islamic banksre financially more stable than large Islamic banks. However, theylso noted the limitations of their research findings regarding lim-ted availability of product-specific data on Islamic banks financialtatements. The challenge when using standard harmonized dataor empirical analysis is that it not only assumes that Islamic andonventional banks share the same traits but also that their stabil-ty is affected by similar factors. However, there are clearly someifferences in the financial indicators of Islamic banks. Examplesay include the nature of contracts underlying Islamic financial

roducts, recognition of income from intermediation activities, andomputation of capital.

This paper explores the potential impact of the IFSBs new reg-latory measures by calculating the NSFR using existing data and

inking it to the financial stability of Islamic banks. It extends theork of Cihk and Hesse (2010) on stability and explores whether

he IFSBs proposed NSFR has any potential impact on the stabilityf Islamic banks.

For empirical estimations, this study utilizes a unique datasetollected from the financial statements of 136 Islamic banks from0 jurisdictions for the period 20002013. This dataset enabless to capture the financial position of Islamic banks based on thenderlying contracts as per the IFSBs guidelines. To the best of theuthors knowledge there are no published studies that have used aataset based on bank-specific variables, or have utilized the IFSBsuidelines, to compute the NSFR for Islamic banks.

The empirical findings suggest that the NSFR measure intro-uced by the IFSB for Islamic banks has a positive impact on thenancial stability of Islamic banks during the sample period. How-ver, the marginal impact of the NSFR on stability diminishes ashe size of the bank increases. These findings remained robust aftersing an alternative measure of financial stability and using anlternative estimation model based on an instrumental variablepproach.

The findings of this study have significant policy implicationsncluding the validation of the new regulatory framework. If Islamicanks adopt the IFSBs recommended NSFR their stability will benhanced. However, our findings also indicate that banks operat-ng under a fully Islamic banking system are less stable comparedo banks in a mixed banking system. We ask that these findingse considered cautiously as countries with fully Islamic bankingystems (Iran and Sudan) were subject to political and economic

ifficulties during the time period under examination that mightave biased the estimation results.

3 A detailed discussion on the difference between the BCBS and the IFSBs pro-osed NSFR measures is provided in Section 2 of this paper.

Fig. 1. Islamic banks share of total banking assets by jurisdiction.Source: Islamic Financial Services Industry Stability Report (2015b)

The rest of this paper is organized as follows. The next sectiondescribes the background, methodology and calculation of the NSFRfor Islamic banks. Section three describes the hypothesis, modeland variable development utilized in this study. Section four pro-vides a rationale and discussion on the sample, data and univariateanalysis used in this research with section five providing regressionresults and section six provides robustness checks of the empiricalfindings. Section seven will provide concluding comments.

2. Net stable funding ratio for Islamic banks

In 2014, almost 80 percent (USD 1.63 trillion) of the globalIslamic finance industrys assets were Islamic banking assets(Thomson Reuters, 2015). The Islamic banking industry grew atabout 17 percent annually between 2008 and 2013. In compari-son, the top 1000 global banks grew by only 4.9 percent in 2012and 0.6 percent in 2013. With such rapid growth, several Islamicbanks have become systemically important banks especially inthose economies where Islamic banks account for over 10 percentof total bank assets.4

Fig. 1 depicts the share of Islamic banking assets as compared tototal banking assets in countries where the Islamic banking sectorhas a sizeable presence. Iran and Sudan have a fully Islamic bank-ing sector and thus 100 percent of bank assets are with Islamicbanks. Other countries with a significant Islamic banking sectorinclude: Saudi Arabia with 51.3%, Brunei with 41%, Kuwait with38%, Yemen with 27.4%, Qatar with 25.1%, Malaysia with 24%, andthe UAE with 17.4% of total domestic banking assets invested withIslamic banks. Bangladesh, Jordan, and Pakistan are also in dou-ble figures in terms of the percentage of total domestic bank assetsinvested with Islamic banks. These figures highlight the importanceof the Islamic banking sector in countries where the majority of thepopulation is faith-adhering Muslims.

To support the Islamic banking sector legal and regulatoryframeworks specifically designed to cater the needs of Islamicbanks have been created. Examples include the Malaysian Islamic

Financial Services Act 2013 which provides a legal foundation forthe Islamic banking system to shift towards a regulatory frame-work that caters to the needs of specific types of Sharah contracts.

4 Islamic Financial Services Industry Stability Report (2015b).

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ikewise, the State Bank of Pakistan launched a five-year strate-ic plan and is finalizing details on an Islamic liquidity frameworkonsisting of an Islamic inter-bank money market (IIMM) and audarbah-based placement facility run by the central bank. Oman

nd Qatar recently set up a separate banking system for Islamicanks that does not allow conventional banks to offer Islamic finan-ial products through Islamic windows. Turkey is also in the processf developing legal and regulatory frameworks to enhance the

slamic banking industry.One of the major differences between conventional banks and

slamic banks is the way assets and liabilities are structured. Thessets and liabilities of conventional banks are structured as debtnstruments while the assets and liabilities of Islamic banks aretructured in more equity-like instruments. Aside from benevolentoans (Qard-al-Hassan) the assets of Islamic banks can be dividednto three broad categories:

Equity-like assets including partnership instruments (Musharakaand Mudarbah),Leases (Ijarah) and,Debt-like instruments including deferred delivery-of-products(salam for basic products and istisnaa for manufactured or con-struction projects) and sale plus markup (Murabahah).

The major difference between conventional and Islamic banksn terms of liabilities is in the nature of deposits. In comparison

ith conventional banks, deposits of Islamic banks have guar-nteed Sharah safekeeping deposit contracts (Qard-al-Hassannd Wadiah), non-guaranteed Sharah contracts for investmentMudarbah and Wakalah), profit sharing investment accountsrestricted and unrestricted), and sukuk (Islamic equivalent of con-entional bonds). The nature of profit sharing investment accountsPSIAs) also differs from that of conventional bank deposits. PSIAsre more like mutual fund investments where investors bear theoss when there is a deterioration in the value of an investment foreasons other than management negligence. This equity-like struc-ure of liabilities provides an extra layer of protection to Islamicanks especially during market down turns.

To cater for the specific regulatory needs of Islamic banks,he IFSB provides a regulatory framework for the Islamic bank-ng industry. To remain aligned with the global banking industryhe IFSB endorsed the Basel III regulatory framework after mak-ng adjustments for the different nature of assets and liabilities ofslamic banks. The IFSB issued several standards including the Cap-tal Adequacy Framework (IFSB-15), and the Guiding Principles oniquidity Risk Management (IFSB-12) for Islamic banks.

Like its conventional counterpart, the NSFR requirement underhe IFSBs guidelines is the ratio of available stable funding (ASF)o required stable funding (RSF). However, there are clear differ-nces in the computation of ASF and RSF between Islamic andonventional banks. Table 1 presents the major differences in thereatment of assets and liabilities under both approaches and theirespective haircuts for the calculation of the NSFR.

The difference in treatment of various categories of assets andiabilities under the IFSB-NSFR and the BCBS-NSFR is due to theistinctive nature of assets and liabilities of Islamic banks. On the

iabilities side of an Islamic bank, current account deposits andeposits under Qardal-Hassan or wadiah are debt in nature similaro their conventional counterparts and hence need similar RSF to

eet both the IFSB and the BCBSs requirements. However, depositsnder profit sharing arrangements have clear differences underach banking systems.

Islamic banks Profit Sharing Investment Accounts (PSIA) havewo categories. One is restricted under which funds are provided bynvestors and are invested per the account holders instructions andence are quasi equity. The second category of PSIA is unresticted

l Stability 25 (2016) 4757

in which account holders give banks full discretion to invest in anySharah-compliant assets and these accounts may be comingledwith shareholders capital. The IFSB requires a haircut of 90%95%for unrestricted PSIA. The BCBS for conventional banks however,does not differentiate savings deposits and requires a flat haircutof 95% when calculating the NSFR.

A major source of difference between the BCBS and the IFSBsNSFR is in regard to the treatment of assets. There are productsoffered by Islamic banks which are so unique that there are nocounterparts offered by conventional banks. One such example isMusharaka products which are based on partnership principles.These are not the same as conventional banks loans and advances.Under the BCBS conventional banks are required to provide 85%stable funding against such loans and advances but due to thepartnership nature of the Musharaka Islamic banks are requiredto provide just 50% stable funding as it partially qualifies as equity(risk-sharing feature).

Another major difference under loans and advances is Qard-al-Hassan. It is rare for conventional banks to offer such loans andadvances to customers. However, if it does then the BCBS requires85% stable funding. Islamic banks offer Qard-al-Hassan more fre-quently and these loans are based upon Hassanah and the IFSBrequires no stable funding against these loans. Sukuk, Salam andistisnaa are unique products provided by Islamic banks. The BCBSdoes not have any rules regarding these products but the IFSB pro-vides for an 85% haircut for these products. Ijarah contracts are likemortgages and leases offered in the conventional banking system.The BCBS allows for a 50%65% haircut while the IFSB requires 50%stable funding for all Ijarah contracts.

For the calculation of RSF utilizing the IFSB guidelines, assets andliabilities are categorized into buckets depending on their liquid-ity. Every bucket has a haircut depending on the relative liquidityof the asset. These haircuts range from 0% for cash (highly liquid) to100% for fixed assets (highly illiquid). On the other hand, haircutsalso apply to funding sources to calculate ASF. These haircuts rangefrom 100% for regulatory capital (non-returnable equity financing)to 0% for Sharah -compliant hedging instruments.

Although quantitative guidelines issued by the IFSB are compre-hensive, there are clear limitations in calculating the NSFR usingpublically available data. The International Monetary Fund, in itsApril 2011 Global Financial Stability Report, highlights data issuesthat are challenging when calculating the NSFR. Most of the stud-ies analyzing the impact of NSFRs usually apply an approximationsapproach on the application of haircuts to various components ofthe balance sheet when calculating the NSFR under Basel III (King,2013; Distinguin et al., 2013; Yan et al., 2012). These assumptionsare generally in line with the broader interpretation of various bal-ance sheet items and are based on the liquidity and maturity ofassets and liabilities. Similar to the conventional approach, we usea modified IFSB approach for the computation of the NSFR thattakes into consideration several assumptions about maturity andliquidity in assigning haircuts as shown below:

NSFRit =ASFitRSFit

(1)

where ASFit is the sum of

100% values of total shareholders capital (tcapit) and mudarbahinvestment accounts (mud invit), and

50% of Mudarbah savings (mud savit), current savings (cnsacit)

and other accounts (oth depit) that are not profit and loss sharing.

RSFit is the sum of

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50% values of outstanding financing contracts based onMusharaka and diminishing Musharaka (mshit), leasing (ijarah),hire purchase, and ijarah muntahia bittamleek (ijait),65% of investment in companies, funds, shares (secit) and invest-ment in Islamic bonds (invit),85% in Murabahah, deferred sales and murabahah for purchaseorders (mrbit), Istisnaa and parallel istisnaa (istit) and all otherfinancings (othit), and100% of the fixed assets (net of depreciation) (fxdit) and balanceswith banks and other institutions (ofiit).

Mathematically the above is shown as:

NSFRit =[(tcapit + mud invit) + 0.5(mud savit + oth depit + cnsavit)]

[0.5(mshit + ijait) + 0.65(secit + invit) + 0.85(mrbit + istit + othit) + (fxdit + ofiit(

We hypothesize that a higher NSFRit contributes positively tohe overall stability of Islamic banks since this ensures that theyave more available funds than is required. The following sec-ion develops the empirical model used to test this hypothesis andevelops control variables for empirical analysis.

. Determinants of bank stability

.1. Stability measure

Most of the empirical literature on financial stability of banksses Z-score as a tool for the assessment of individual bank insol-ency risk and financial stability.5 Mathematically, it measures theumber of standard deviations of a banks return-on-assets it wouldave to fall to deplete the sum of its equity and income. Z-score hasdvantages over other accounting-based financial stability mea-ures due to its capability to capture both interest and fee-basedncome streams. Following Lepetit and Strobel (2013)6, Z-score isalculated as:

TBLit =E(ROA)it + CARit

(ROA)i(3)

E(ROA) it is the expected return on assets, CAR it is equity capital-o-asset ratio and (ROA) i is the volatility of return-on-assets,ubscript i and t refers to bank and time respectively. As it is widelyrgued in the literature that Z-score is highly skewed we usedts logarithmic transformation in all empirical estimations (Laevennd Levine, 2009; Schaeck and Cihk 2012).

.2. Other control variables influencing the stability of banks andtable funding adjustments, and covariate definitions

Existing empirical literature provides a number of explanatoryariables as to why banks may adjust their portfolio risk to meetegulatory requirements. The explanatory variables are dividednto two broad categories of bank-specific and industry-specific

ovariates. In the next sub sections, the rationale for each covariatencluded in the empirical model is considered in detail.

5 See for example Boyd and Runkle (1993), De Nicolo (2001), Stiroh (2004), Stirohnd Rumble (2006), Laeven and Levine (2009), Demirgc -Kunt and Huizinga (2010),arrell et al. (2010), and De Haan and Poghosyan (2012).6 Lepetit and Strobel (2013) compared various methods used for calculating

-score. They suggest that an alternative measure that uses mean and standardeviation of the return-on-assets calculated over the full sample period and currentalues of the CAR ratio is more robust.

l Stability 25 (2016) 4757 51

3.2.1. Bank specific variablesAmong bank-specific variables, the size of the bank (too big

to fail) significantly influences the composition of assets and ulti-mately the risk-taking behavior of the bank (Schwerter, 2011).Furthermore, larger banks can maintain higher liquidity levels dueto easier access to the lender of last resort and would be the first tobenefit from this safety net (Distinguin et al., 2013). Similarly, largerbanks enjoy better franchise value and can use diversification as atool for risk management (Demsetz and Strahan, 1997). We mea-sure SIZEit as the natural log of total assets. A negative coefficientwith SIZEit indicates the too big to fail phenomena while a positivecoefficient with SIZEit reflects the impact of higher franchise value,better risk management systems, and easier access to the lenderof last resort.

A banks stability is also a function of its income sources. Incomesources for banks have changed considerably over the past coupleof decades. Busch and Kick (2009) concluded that fee income ismore stable for commercial banks in Germany from 1995 to 2007.However, income diversification has been identified as one of themajor factors that may contribute to the fragility of banks (Ashrafet al., 2016; Khler, 2015; Ashraf and Goddard, 2012; Demirgc -Kunt and Huizinga, 2010). NONIIit is the ratio of income from fee-based activities to total assets. A positive relationship with STBLitimplies diversification benefits for banks.

Bank profitability is another important driver of bank stability.Financial institutions with strong operational profitability enjoystable income streams (King, 2013; Jiraporn et al., 2014; Hong et al.,2014). We use the ratio of net income to total assets (NITAit) asour measure of profitability. We anticipate a positive coefficient ofprofitability with bank stability.

Beck et al. (2013) reported that Islamic banks are generally lessefficient in terms of cost efficiency compared to conventional banks.They attributed this inefficiency to the level of maturity, sophistica-tion, and competitive behavior of Islamic banks. We use the inverseof the cost-to-income ratio (EFFit) to control for efficiency. A pos-itive coefficient would imply that higher efficiency helps Islamicbanks to become more stable.

3.2.2. Country-specific control variablesThe economic outlook of a country plays an important role in

the stability of its financial institutions. Credit demand by corpo-rations and credit supply by the financial sector shrinks during adown turn in the economy resulting in a poor performance in thefinancial sector (Lowe and Rohling, 1993). Literature suggests thatthe financial performance of a bank is influenced by business cycles(Laker, 1999; St. Clair, 2004; Jokipii and Monnin, 2013). To controlfor the impact of business cycles we use GDP growth (GDPjt) as amacroeconomic control variable.

The banking industry has been transformed considerably overthe past couple of decades due to the deregulation of banking activ-ities, financial innovation, and technical advancements. This hasled to higher merger and acquisition activities and hence competi-tion within both the domestic and international banking industries(Goddard et al., 2007). Higher competition within the banking sec-tor may lead to higher risk-taking (Boyd et al., 2006; Uhde andHeimeshoff, 2009). To control for the impact of competition on bankstability, we used 5-banks asset concentration (CONCjt) as a proxyfor competition.

Religiosity in a society could affect the stability of financial insti-tutions. We added a new country-specific variable to determine ifthere is any potential impact of religiosity on the financial stability

of Islamic banks. Ahmed and Gouda (2014) developed a consti-tutional religiosity index that measures the extent to which theconstitution of a country is developed under Islamic fundamen-tal principles. We used the constitutional religiosity index (RELGjt)


Table 2Descriptive statistics of all the non-dummy variables used in this study. Definitions are in column 2. Data is sourced from the Islamic banking information system (IBIS). Thetime period is from 2000 to 2013.

Variable Definition Obs Mean Std. dev. Min Max

STBLit Financial stability 1226 2.32 0.96 0.16 3.82NSFRit Net stable funding ratio 1234 1.01 0.34 0.20 1.78SIZEit Log of total assets 1234 14.02 1.95 2.00 18.48NITAit Net income/total assets 1234 0.01 0.02 0.11 0.05NONIIit Non-interest income/total assets 1234 0.01 0.01 0.00 0.04EFFit Bank efficiency 1182 3.94 4.48 1.02 19.14

0.05 0.04 0.15 0.2669.42 31.04 0.81 100.00

9.88 8.02 0.00 26.00

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Table 3Pairwise correlation matrix of all the non-dummy variables used in this study.Definitions are in column 2 of Table 2. Data is sourced from the Islamic bankinginformation system (IBIS). The time period is from 2000 to 2013.

STBLit NSFRit SIZEit NITAit NONIIit EFFit GDPjt CONCjt RELGjt

NSFRit 0.060** 1SIZEit 0.096** 0.197***1NITAit 0.116*** 0.134***0.137*** 1NONIIit0.237***0.046*** 0.310***0.180***1EFFit 0.023 0.159***0.179***0.178***0.246***1GDPjt 0.008 0.068** 0.008 0.190***0.039 0.064** 1CONCjt0.082** 0.122*** 0.369***0.001 0.232***0.122***0.032 1RELGjt 0.047* 0.163***0.319*** 0.184***0.0010.122***0.0110.618***1* p < 0.1.

GDPjt Gross domestic product-growth rate 1219 CONCjt 5-Banks assets/bank industry assets 1203 RELGjt Index of constitutional religiosity 1234

reated by Ahmed and Gouda (2014) in our model to control foreligiosity.

To control for the impact of the 2007-2009 global financial crisise included a dummy variable that takes the value of one dur-

ng 2007-2009, zero otherwise. We also use another dichotomousariable (FULLjt) to control if conventional and Islamic banks areperating side-by-side. FULLjt is equal to one if the banking system

s fully Islamic or zero otherwise.

. Sample, data and univariate analysis

This section describes the sources of data used for our empiricalnvestigation and its univariate analysis. We obtained our financialtatement data from the Islamic Banks and Financial Institutionsnformation usually referred as Islamic Banking Information Sys-em(IBIS) database for all Islamic banks with at least three yearsonsecutive data.7 IBIS collects and provides financial statementata on Islamic banks based on underlying Islamic financial con-racts for their assets and liabilities. Existing studies that analyzedhe stability of Islamic banks have used the Bankscope database.8

Although the Bankscope database is considered to be the mostomprehensive database for banking it has some limitations whent comes to Islamic banking (Cihk and Hesse, 2010). Limitationso Bankscope data include differences in variable definitions byslamic and conventional banks. For example, what is or is notncluded in capital for Islamic banks or how to measure (the equiv-lent of) interest income? Second, for the calculation of the NSFR ofslamic banks, we need data on Islamic products based on under-ying Islamic financial contracts and Bankscope does not have suchata on these accounts in a long time series. For these reasons wesed the dataset from IBIS as it is a database specifically designed for

slamic banks and have data on Islamic financial statements since000. The use of the IBIS data set makes this study more valuables it utilizes Islamic bank data measured and reported through theslamic banks reporting framework and is thus more reliable.

The initial data set for this study consisted of 173 Islamicanks for which the financial data was available from IBIS duringhe period 20002013. We then dropped all observations whereslamic banks showed no deposits or financing activities. We alsoropped all banks with less than three years of continuous data.e lost some observations due to missing data or obviously incor-

ect data. In addition, we dropped data for banks from Iraq dueo war and political instability during this time. After this filteringrocess, we were left with an unbalanced panel data of 136 Islamic

anks from 30 countries with a total of 1226 bank-year observa-ions. The data for macroeconomic variables was downloaded fromhe World Bank website. Finally, due to the presence of large out-

7 The data is available online at http://www.ibisonline.net.8 See for example Cihk and Hesse (2010).

** p < 0.05.*** p < 0.01.

liers, we winsorized all continuous variables at the 1 st and 99thpercentile.

Table 2 reports the descriptive statistics of the sample. The meanof STBLit is 2.32 suggesting that an additional 43% of the volatility ofreturn-on-assets is required to deplete the equity of Islamic banks.Among explanatory variables Islamic banks have, on average, NSFRof 1.01 suggesting that Islamic banks are sufficiently funded overthe sample period. On average, the return-on-asset (NITAit) forIslamic banks and income from fee-based activities (NONIit) is 1%.The low ROA and income from fee-based activities can be attributedto the fact that most Islamic banks started their operations dur-ing the sample period and were investing heavily to compete withconventional banks.

Generally, country-specific variables are within the normalrange but do exhibit some differences. The growth in Gross Domes-tic Product (GDPjt) shows a mean value of 5% however, countriesdo reflect some degree of dispersion with a minimum negative of15% to a maximum of 26%. The concentration of the banking sectoris measured by 5-banks asset concentration (CONSjt). This showsthat, on average, the five biggest banks hold 78% of the bankingsectors total assets in those countries where Islamic banks oper-ate. On average constitutional religiosity stands at 9.88% suggestingthat countries that offer Islamic finance are more likely to follow asecular constitution.

The pairwise correlation matrix for the main variables is pre-sented in Table 3. Generally, the correlation is in line with ourexpectations. Factors that can adversely affect the stability of bankson a stand-alone basis include the NSFR, income from fee-basedactivities, and the concentration of banking assets in the five biggestbanks. Among the covariates that enhance the resilience of banksare the size of banks and profitability.

4.1. Empirical methodology

The theoretical literature investigating the impact of regula-tory requirements on the stability of banks use dynamic models

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Table 4Estimation results based on a dynamic panel autoregressive model as specified in Eq.(4). Definitions are in column 2 of Table 2. Data is sourced from the Islamic bankinginformation system (IBIS). The time period is from 2000 to 2013. Standard errors inparentheses.

(1) (2) (3)Variables STBLit STBLit STBLit

NSFRit 0.1304*** 0.1468*** 1.0869***

(0.0439) (0.0442) (0.2526)SIZEit 0.0448*** 0.0546*** 0.0053

(0.0098) (0.0102) (0.0191)NITAit 0.6666*** 0.6143** 0.4567*

(0.2544) (0.2533) (0.2558)NONIIit 3.3897** 4.2146*** 4.0676***

(1.4690) (1.4801) (1.4797)EFFit 0.7226*** 0.7260*** 0.6494***

(0.0810) (0.0826) (0.1897)Global financial crisis (dummy) 0.0019 0.0016

(0.0229) (0.0229)GDPjt 0.0805 0.0482

(0.2502) (0.2498)CONCjt 0.0066*** 0.0067***

(0.0019) (0.0019)RELG jt 0.0178 0.0186

(0.0124) (0.0122)Fully Islamic banking country (dummy) 0.7149*** 0.7286***

(0.2238) (0.2202)NSFR SIZE 0.0674***

(0.0184)NSFR EFF 0.0480

(0.1536)Constant 3.0961*** 3.6964*** 2.8430***

(0.1630) (0.2664) (0.3482)Observations 1226 1180 1180Number of banks 136 133 133

Wooldridge test for autocorrelation 250.031*** 242.726*** 207.705***

Modified Bhargava et al. Durbin-Watson 0.740*** 0.774*** 0.783***

Baltagi-Wu LBI test for autocorrelation 1.121*** 1.147*** 1.151***

*


o control for adjustment costs that banks may face when movingoward target regulatory requirements (Ayadi et al., 2009; Dahert al., 2013; Elizalde and Repullo, 2007; Naceur and Omran, 2011).he economic reasoning for using the dynamic model is that bankehavior displays persistence. Bank management change their poli-ies based on the current financial and regulatory environment thatould potentially affect their future stability implying that past sta-ility does affect present and future stability (Jahn and Kick, 2012).he persistence in banking stability can be incorporated, econo-etrically, by using a dynamic panel model.

To test the effect of the NSFR on the financial stability of Islamicanks, we modeled the stability of Islamic banks using a dynamicanel model assuming that lagged stability values may partiallyxplain the subsequent behavior of variables over time. We assumehat the stability of bank i is a function of its funding stability, itsundamentals, and a host of country-level control variables includ-ng economic, constitution, and competition condition in country. The basic econometric specification is:

TBLit = + oSTBLit1 + 1NSFRit + Bit + Cjt + Dt + i + it(4)here STBLit is a measure of financial stability of an Islamic bank

s measured by Z-score, NSFRit is the net stable funding ratio cal-ulated using Eq. (2), Vector Bit, Cjt and Dt are observable bank,ountry-specific, and dummy control variables respectively andi+itis the error (idiosyncratic) terms so that:

it = it1 + it (5)here || < 1 and it is independent and identically distributed

i.i.d.) with mean 0 and variance2 . Due to the presence of time-nvariant covariates i is assumed to be a realization of an i.i.d.rocess with mean 0 and variance 2 .

The dynamic panel data allows for possible endogeneityetween the dependent and the explanatory variables character-

zed by autocorrelation due to the presence of a lagged dependentariable. Bond (2002) suggests that a dynamic model is preferredor panel data even if the coefficient of the lagged dependent vari-ble is not of direct interest; allowing for dynamic in the underlyingrocess may be crucial for recovering consistent estimates of otherarameters. The endogeneity problem associated with dynamicodels is dealt with in this paper using the Baltagi and Wus (1999)

ynamic panel model following an AR(1) distribution. Further-ore, standard errors are clustered at bank level to account for

eteroscedasticity and serial correlation of error terms.

. Regression results and discussion

Table 4 reports the results of Eq. (4) using the dynamicanel model following an AR(1) disturbance explained earlier. Theynamic panel model is suitable if the data series exhibit a serialorrelation to the AR(1) order. To test for serial correlation in thediosyncratic errors of a linear panel-data model we performedhree different tests:

The Wooldridge (2002) test for serial correlation with the nullhypothesis of no autocorrelation was conducted.

A modified Bhargava et al. (1982) test that provides the upper andlower bounds for the Durbin Watson statistic. For a large numberof cross sections, if it is less than 2 then it would indicate a positiveserial correlation.

Baltagi and Wu (1999)s recommended autocorrelation test forunequally spaced panel data.

Test results are reported in the final row of Table 4. All threeests indicate the presence of first order autocorrelation or in otherords, the existence of persistence in financial stability. Under such

p < 0.1.** p < 0.05.

*** p < 0.01.

circumstances OLS estimates are not only inefficient but also under-estimate the error variance resulting in inflated t-statistics that maycause the erroneous rejection of null hypothesis.

For empirical estimations, we used two different specificationsof the model in Eq. (4). The first specification includes bank-specificvariables and for the second specification we added country-specific variables to the first specification. The estimation results,reported in Table 4, are in line with expectations. The null hypoth-esis of no association between NSFRit and Z SOREit is rejected at5% significance level suggesting that the maintenance of the NSFRrequirements has a positive impact on the stability of Islamic banks.These results are in line with the findings of Jiraporn et al. (2014)who argued that if the NSFR requirements were implemented dur-ing 20052009 it would have positively affected the stability of thebanking sector.

The coefficient of SIZEit is significantly negative which suggeststhat Islamic banks smaller in size are more stable compared tolarger Islamic banks. This finding is in line with Cihk and Hesse(2010) who suggested that small Islamic banks were stronger thanlarge Islamic banks in 19 banking jurisdictions from 1993 to 2004.This result may also reflect the challenge of risk-management thatIslamic banks may face with the growth in bank size. From theperspective of conventional banking this result is also in line withthe findings of Demirgc -Kunt and Huizinga (2010) who foundthat larger banks exhibited lower risk aversion over the period

19952007. Similarly Maudos and De Guevara (2011), after exam-ining a large sample of EU, American, and Japanese banks from20012008, concluded that size may have a negative, but not linear,relationship with stability. This implies that for very large banks an


terms

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Fig. 2. The relationship of interaction

ncrease in bank size decreases the probability of bankruptcy. How-ver, Hakenes and Schnabel (2011) argued that larger banks areore stable due to their access to sophisticated risk-management

ools and access to the lender of last resort. Our results are in lineith the literature; larger banks demonstrate lower risk aversion.

Profitability, as measured by the return-on-assets (ROA), is neg-tive and significant indicating that Islamic banks, while seekingigher profit margins, engage in high risk-taking operations. How-ver, Hong et al. (2014) who linked profitability with failure hazardsing call report data from US banks from 2001 to 2011, concludedhat banks with higher profitability are more resilient to short-termhocks and have less failure hazard. The differences in our resultsan be attributed to the different nature of banks (traditional versusslamic) and a different sample.

The measure of diversification, non-interest income to totalssets, was significantly positive providing robust evidence thatanks with diverse income sources (other than profit or returnsarned from traditional intermediation activities) become moretable. These results are in line with Busch and Kick (2009) who con-luded that banks enjoy stability benefits with fee income as thisype of income is more stable when compared to interest income.ank efficiency, measured by taking payments to depositors as aatio of total income from loans, positively affects bank stability.his suggests that Islamic banks that pay higher returns from their

ncome to their depositors are more stable.

The global financial crisis did not have any significant impact on

slamic banks. Also, GDP growth has no significant impact on thenancial stability of Islamic banks. Market competition, measuredy 5-banks assets to total banking assets of a country, is signifi-

of NSFR with SIZE and EFF with STB.

cantly negative in affecting the financial stability of banks. Uhdeand Heimeshoff (2009), using aggregate data from the EuropeanUnion banking sector, highlights the negative impact of marketconcentration (a proxy for market competition) on financial sta-bility. These results are in line with Vives (2011) who argues thatthere are two possible ways in which higher levels of competitioncan lead to banking instability. Firstly, by aggravating the coordi-nation problem of depositors/investors on the liability side andfostering runs/panics. Second, by increasing incentives to engagein high risk activity ultimately results in an increased probabil-ity of failure. Boyd and De Nicolo (2005) concur suggesting thathigher competition within the banking sector may lead to higherrisk-taking.

The coefficient of religiosity is not statistically significant albeitpositive. The insignificance of the RELG is a little surprising. How-ever, this can be explained by the fact that countries with higherlevels of religiosity in their constitution are still working under aconventional banking system and regulating Islamic banks underthe same regulatory framework. Such an environment does not sig-nificantly channel the potential positive impacts of constitutionalreligiosity into the Islamic banking system.

The dummy used to measure the impact of a fully Islamicfinancial system is significantly negative. This result suggests thatIslamic banks operating in a fully Islamic financial system are unsta-ble; this may be because of limited investment avenues and Sharah

restrictions on doing business. We ask that these findings be con-sidered cautiously as countries with fully Islamic banking systems(Iran and Sudan) were subject to political and economic difficulties


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Table 5Estimation results based on modified z-scores. Definitions are in column 2 of Table 2.Data is sourced from the Islamic banking information system (IBIS). The time periodis from 2000 to 2013. Standard errors in parentheses.


NSFRit 0.1962*** 0.2395*** 1.5982***

(0.0713) (0.0732) (0.3921)SIZEit 0.0466*** 0.0565*** 0.0313

(0.0156) (0.0158) (0.0299)NITAit 1.1368*** 1.1765*** 0.9358***

(0.3233) (0.3249) (0.3286)NONIIit 8.0813*** 9.2564*** 8.7601***

(2.2991) (2.3265) (2.3130)EFFit 1.2356*** 1.2624*** 1.2277***

(0.1299) (0.1340) (0.3085)Global financial crisis (dummy) 0.0220 0.0185

(0.0410) (0.0407)GDPjt 0.7951 0.7218

(0.5157) (0.5112)CONCjt 0.0073** 0.0074**

(0.0032) (0.0032)RELG jt 0.0782*** 0.0795***


(0.3521) (0.3519)NSFR SIZE 0.1056***

(0.0308)NSFR EFF 0.0220

(0.2410)Constant 2.7940*** 2.8927*** 1.7373***


Wooldridge (2002) test for autocorrelation 157.870*** 136.620*** 95.545***

Modified Bhargava et al. Durbin-Watson 0.881*** 0.945*** 0.957***

Baltagi-Wu LBI test for autocorrelation 1.252*** 1.1303*** 1.307***


uring the time period under examination that might have biasedhe estimation results.

The interaction between covariates may pose a challenge to thendings of this study. Royston and Sauerbrei (2008) recommend

he use of a multivariable fractional polynomials interaction (MFPI)echnique to cater for the interaction between pairs of covariatesn the model. The MFPI is designed to investigate the interactionnd statistical significance between each pair of covariates in theodel whether continuous, binary or categorical. Specifically, we

mployed Royston and Sauerbrei (2012) and identified the interac-ion of the NSFR with SIZE and the NSFR with EFF as significant at 1%.owever, the relationship of interaction pairs with the dependentariable is not linear as depicted in Fig. 2. For example, when theSFR is below the required 100% level smaller banks show higher

tability as compared to larger banks. However, this relationship iseversed once the NSFR reached the 100% levellarger banks show

greater level of stability as compared to smaller banks. The non-inear relationship suggests that there is a possibility of a negative

oderating role of SIZE with the NSFR-stability relationship. Erro-eously assuming that the effect of SIZE is linear on the stabilityf Islamic banks while estimated slopes of SIZE and NSFR indicatestrong interaction between SIZE and the NSFR, and may lead to arong inference.

To incorporate the impact of identified interaction termsSFR SIZE and NSFR EFF we re-estimated the last empirical esti-ation by incorporating these two additional interaction terms.

he empirical results are reported in column 3 of Table 4. The coef-cient of NSFR SIZE is negative and significant suggesting that

he marginal impact of the NSFR on stability diminishes as the sizef the bank increases. This clearly indicates a cautious approach isequired for cost-benefit analysis purposes when moving above thehreshold level.

The coefficient of the second interaction term, NSFR EFF, isnsignificant suggesting that the effect of the NSFR on financial sta-ility is not influenced by a change in bank efficiency levels. The

evel of significance and direction generally remain the same amongther covariates.

. Robustness checks

.1. Alternative stability measure

Cihk and Hesse (2010), when comparing the stability of Islamicnd conventional banks, suggested that more interesting informa-ion lies in the downward spikes of ROAs (and Z-scores) than inhe overall sample. However, the standard deviation underlyinghe standard Z-score based on the overall sample gives only partialnformation about the behavior of Z-scores as a measure of stability.o take into account the downward spikes in ROAs (and Z-scores),

ihk and Hesse (2010) suggested an alternative method for theomputation of Z-score wherein capitalization and ROA are dividedy the absolute value of the downward volatility of ROA.9

To further confirm our results, we calculated the modified Z-core as suggested by Cihk and Hesse (2010) using the downwardolatility of ROA. We estimated the dynamic panel model andesults are reported in Table 5. Results remained robust in termsf direction and significance. In fact, the magnitude of the coeffi-

ient increased which suggests that the stabilizing function of theSFR is more pronounced when considering downside risk.

9 Hesse and Cihk (2007) also used a modified z-score.

*p < 0.1.** p < 0.05.

*** p < 0.01.

6.2. Endogeneity

To address for endogeneity, we turn to instrumental variabletechniques using a two stage least square (2SLS) estimator. Weemploy the deposit-to-loan ratio, leverage ratio, and variability inincome (standard deviation of return-on-assets) as instrumentsto explain the NSFR in the first stage. All of these variables areimportant measures for the funding stability of banks. A higherdeposit-to-loan ratio determines the funding gaps and could harmthe funding stability of banks in the case of bank runs. A higherleverage ratio (long term liabilities to total assets) determines howmuch funding is provided by long term investors. Similarly, theprofitability of banks can provide a buffer to the equity of banks.

The empirical results based on the 2SLS estimator are reportedin Tables 6 and 7. The dependent variable in the case of Table 6is the Z-score while in the case of Table 7, the dependent variableis modified Z-score as discussed above. The main results remainunchanged even after controlling for endogeneity. The NSFR ispositive and significant in both tables while the interaction term(NSFR SIZE) is negative and significant suggesting that the NSFR,on average, enhances the stability of Islamic banks. However, thestability enhancement function of the NSFR is more pronounced insmaller banks.

Our results offer empirical support for the new regulatory mea-sure of a NSFR introduced by the IFSB for Islamic banks as it has apositive effect on bank stability. Our findings support the view that

funding stability positively affects the soundness of Islamic banks.However, the positive stability impact of the NSFR is not uniform.Islamic banks that are smaller in size benefit more from the newfunding requirements than larger banks. We ask that a cautious

56 D. Ashraf et al. / Journal of Financia

Table 6Estimation results based on an Instrumental variable model. Definitions are in col-umn 2 of Table 2. Data is sourced from the Islamic banking information system (IBIS).The time period is from 2000 to 2013. Standard errors in parentheses.


NSFRit 0.7677*** 0.9265*** 1.3887***

(0.1495) (0.1541) (0.2871)SIZEit 0.0514*** 0.0700*** 0.0054

(0.0107) (0.0117) (0.0216)NITAit 0.4806 0.4793 0.3537

(0.3158) (0.3185) (0.2851)NONIIit 3.3079* 5.1771*** 3.9422**

(1.8253) (1.8909) (1.6947)EFFit 1.0253*** 1.0824*** 0.8961***

(0.0976) (0.1018) (0.2046)Global financial crisis (dummy) 0.0582** 0.0432*

(0.0266) (0.0238)GDPjt 0.4759 0.0985

(0.3444) (0.2985)CONCjt 0.0088*** 0.0090***

(0.0020) (0.0018)RELG jt 0.0230* 0.0175


(0.2262) (0.2276)NSFR SIZE 0.0855***

(0.0202)NSFR EFF 0.0051

(0.1696)Constant 2.6547*** 3.3437*** 3.0280***


* p < 0.1.** p < 0.05.

*** p < 0.01.

Table 7Estimation results based on an Instrumental variable model. Dependent variable ismodified z-score. Definitions of other variables are in column 2 of Table 2. Data issourced from the Islamic banking information system (IBIS). The time period is from2000 to 2013. Standard errors in parentheses.


NSFRit 1.6352*** 1.6725*** 1.8659***

(0.3267) (0.3137) (0.4330)SIZEit 0.0070 0.0289 0.0240

(0.0234) (0.0235) (0.0336)NITAit 0.2011 0.0726 0.5004

(0.4655) (0.4561) (0.3436)NONIIit 9.5527*** 12.0512*** 12.2696***

(3.1712) (3.1981) (2.5136)EFFit 1.6989*** 1.7638*** 1.1351***

(0.1844) (0.1870) (0.3243)Global financial crisis (dummy) 0.0895* 0.0368

(0.0532) (0.0404)GDPjt 1.5729** 0.8542

(0.7424) (0.5621)CONCjt 0.0085** 0.0121***

(0.0036) (0.0030)RELG jt 0.0936*** 0.0772***


(0.3818) (0.3862)NSFR SIZE 0.1139***

(0.0331)NSFR EFF 0.1873

(0.2631)Constant 0.9250* 1.1148 2.0318***


* p < 0.1.** p < 0.05.

*** p < 0.01.

l Stability 25 (2016) 4757

approach be adopted when interpreting these results as a high NSFRmay potentially affect the size of banks balance sheets as banksmight forgo good investment opportunities trying to maintain ahigh NSFR ratio.

7. Summary and conclusion

In the aftermath of the recent financial crisis (20072009)the BCBS incorporated new changes in their regulatory frame-work and proposed new checks on funding stability namely therequirement to meet a Net Stable Funding Ratio. However, thestructural framework and product nature of Islamic banks differsfrom the traditional interest-based banking system. Therefore, itis not appropriate to calculate the NSFR of Islamic banks in thesame way as traditional banks. The Islamic Financial Services Board(IFSB), the standard setting body for the Islamic banking indus-try, while endorsing the Basel III accord modified the Net StableFunding Ratio (NSFR) to cater for the unique aspects of the Islamicbanking industry.

We calculated the NSFR of Islamic banks according to the IFSBsrequirements and investigated its potential link to the financial sta-bility of Islamic banks from 30 countries. This study used Z-score asa measure of a banks stability and the NSFR as a tool to increase andstrengthen a banks stability. Our research found robust evidenceto suggest that the IFSBs requirement of a NSFR has a significantpositive effect on the stability of the Islamic banking industry andthus adds to the growing body of literature in favor of regulatoryframeworks introduced after the global financial crisis.

Our research findings indicate that the NSFR has the capabil-ity to increase the financial stability of Islamic banks by reducingmaturity mismatch of assets and liabilities resulting in improvedfinancial stability. However, the impact of stability is not uniformamong all banks. This indicates a cautious approach is needed whenviewing the NSFR as a tool to increase and strengthen the stabil-ity of all Islamic banks. The results remained robust after usingalternative stability measures and controlling for endogeneity.

This study also opens up new avenues for future research. Thestability-enhancing function of the NSFR is welcome however, thequestion what does compliance with the NSFR mean for the bal-ance sheet and the efficiency of Islamic banks still needs to beaddressed.

Acknowledgments

We would like to thank Professor Kabir Hassan, Professor Obiy-athullah Ismath Bacha, Professor Mansor Ibrahim, Dr. AndreasJobst, the Managing Editor (Professor Iftekhar Hasan), and twoanonymous referees for their helpful comments and suggestionsthat enabled us to improve the quality of this paper considerably.Useful comments by the participants of the Islamic Finance Bankingand Business Ethics conference held in Lahore, Pakistan on March2627, 2016 are also gratefully acknowledged. We also wish tothank Mr. Zahid ur Rehman Khokher, Assistant Secretary-General,Islamic Financial Services Board (IFSB) for his valuable feedback onan earlier draft.

We also wish to thank Dr. Anis Ben Khedher, Information Solu-tions Specialist, IRTI for his efforts in collecting and ensuring theaccuracy of the data.

The authors acknowledge and are grateful for financial supportand encouragement from the Islamic Research and Training Insti-

tute, Jeddah, Saudi Arabia and the Deanship of Graduate Studies andResearch at Prince Mohammad Bin Fahd University, Saudi Arabia.

The views expressed in this paper are those of the authors and donot necessarily reflect the views of the Islamic Research and Train-

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