Specific risks and profitability of Islamic Banks in Mena region Текст научной статьи по специальности «Экономика и бизнес»

Economics, Management and Sustainability

journal home page: https://jems.sciview.net

Zrelli, N., Berguiga, I., & Abdallah, A. (2018). Specific risks and profitability of Islamic Banks in MENA region. Economics, Management and Sustainability, 5(1), 44-57. doi:10.14254/jems.2018.3-1.4.

ISSN 2520-6303

Specific risks and profitability of Islamic Banks in MENA region

Nadia Zrelli Ben Hamida* , Imene Berguiga**, Ali Abdallah**

* University of Tunis,

4, Rue Abou Zakaria El Hafsi, 1089 Montfleury, Tunis Associate Professor, Research Unit DEFI, ESSEC Tunis ** Erudite, Universite de Sousse, Ihec, Sousse, Tunis

Article history:

Received: February 19, 2018

1st Revision: February 28, 2018

Accepted: March 21, 2018

JEL classification:

G21 C67 G14 C23 C14

DOI:

10.14254/jems.2018.3-1.4

Abstract Islamic banks offer Sharia-compliant financial products. In addition to conventional risks, these banks face specific risks which will have negative effects. We investigate the effects of specific risks on the performance and the stability of a sample of 53 Islamic banks located in 11 Middle East North Africa countries including five oil-rich monarchies. Data are drawn from the Bankscope database and the annual reports of these banks over the period of 19982014. Using Data Envelopment Analysis, first of all, we designed a series of composite indicators to quantify specific risks of non-Sharia-compliance. In the second step, we applied correlation analysis, Spearman and Kendall correlation tests and a panel analysis in order to address the relationship between the performance and specific risks upon a subsample. According to the results, the relationship between performance and specific risks is significantly positive whereas the impact of each partial indicator remains ambiguous.

Keywords: composite indicators, DEA, income-efficiency, Islamic banks, performance, risks, z-score, Panel analysis, Ranking tests.

1. Introduction

The birth of the Islamic finance is due to the creation of the first Egyptian Islamic Bank, the Mit Ghamr Saving Bank, in 1963. In the year 2013, the Islamic Banks occupied near half of the market share of oil-rich monarchies (Saudi Arabia, Kuwait, Bahrain, Qatar and United Arab Emirates). They had gained less than 5 percent of the market in Egypt, Iran, Jordan, Palestine and Yemen; and represent less than 1 percent of the world market (Ernst and Young, on 2015). The Islamic Banks offer products conforming to the Islamic economic laws which forbid the uncertainty, speculation and interest-bearing loans; and oblige banks to forge transactions with only Islamic Banks assets as well as profits and losses sharing (PLS) contracts. The Sharia board ensures the

Corresponding author: Nadia Zrelli Ben Hamida

E-mail: zrellinadia@yahoo.fr

This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license

lawfulness and the validity of activities. The status of investor and co-owner justifies the part of an Islamic Banks in the results of the Profit Loss Sharing projects, venture capital (Mudarabah) or joint-venture (Mucharakah).They offer other specific contracts of purchase-resale (Murabaha) and leasing (Ijara). Those contracts correspond to a short-term finance and represent 90 percent of the intermediation activities of the Islamic Banks in 2008 (Ali, 2012). The subprime mortgage crisis demonstrated the instability of the conventional banking system (Minsky, 1986), while it increased the interest and the profitability of the Islamic Banks (Hassan & Kayed, 2009). Nevertheless, the Islamic Banks presents problems of risk management. Other than the conventional risks, the Islamic Banks are front of specific risks. The nature of contracts strengthens their specificities by the entanglement of the risks. These specificities underscore the problem of an effective management of the risks, the vulnerability of the Islamic Banks and, consequently, the impact on their profitability. The objective of this study is to measure and analyze the effects of the specific risks on the performance and stability of the Islamic Banks. Section 1 is dedicated to the literature review. Section 2 presents the methodology as well as the data used to calculate the efficiency indicators and specific risk. In Section 3 analysis and interprets of correlations are discussed.

2. Literature review

2.1. Performance and resiliency

The majority of the studies use the Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods to analyze the efficiency of Islamic Banks and to establish a comparative analysis with the conventional banks (CB). According to Al-Muharrami (2008), Alam ( 2012), Amal and Mohamed ( 2015) and Regaieg and Abidi (2015), Islamic Banks are more profitable, liquid, stable, competitive, inclined and can better capitalize on the risks. Abdul-Majid and al. (2010), Srairi (2010), Johnes and al. (2014), and Ferhi and Chkoundali (2015) show that the CB are performing better than the Islamic Banks. In contrast, Bader and al. (2008), Hassan and al. (2009), Said (2013) and Sillah and al. (2015) show that there are no differences of performance between Islamic Banks and CB. The regional analyses of Yudistira (2004) and Kablan and Yousfi (2013) as well as Wahidudin and al. (2014), and contrary to Sulfian and Noor (2009), show that the Islamic Banks of the MENA region would be performing worse than their counterparts, in particular Asian ones. According to Ahmad and al. (2010), the Islamic Banks that operate in countries with high income are more efficient. The constraints of Sharia-compliance forbid the subprime, the practices with leverage effect, and the risky structured products and assets with a lack of traceability (Hassoune, 2008). Nevertheless, the resiliency during the recession of 2008 varies according to the countries of the MENA region, with a better resistance of the large banks (Said, 2012). Compared to 2007, the profitability of Islamic Banks turns out better in 2008-2009 than that of the CB with the exception of Bahrain, Qatar and the UAE (Hasan & Dridi, 2010). Some stylized facts are obtained through the comparative analysis of these three categories of works: Islamic Banks are not more successful than the CB, the Islamic Banks is better capitalized and more inclined to risks; but suffer from higher costs of intermediation and do not reach the necessary optimal size to benefit from economies of scale. Methodologies used in these works present certain limits: DEA does not measure the random error contrary to the SFA which imposes a probably badly specified functional form. The obtained results depend on the studied period, the size of the sample and on its composition. An important size allows considering a variety of countries with various levels of wealth on the condition of identifying the country effects and eliminating the extreme values (Beck et al., 2013).

2.2. Risks

Does the risk-performance correlation of Islamic Banks is comparable to that of a CB? The objective of a bank remains a better performance given its environment and risks of managing. The Islamic Banks does not make an exception to it and meets common risks to the CB but in a different exposure level (Ariffin et al., 2009, Al-Tamimi & Al-Mazrooei, 2007). The Islamic Banks present important liquidity, credit, and operational risks (Hussain & Al-Ajmi, 2012). The liquidity risk is the most present in the BI and arises from the incapacity to cover the commitments to increase the assets (Idries, 2012). The Islamic Banks present difficulties to manage liquidities in particular the investment of liquid assets, refinancing and conversion of the banking assets (El-Gamal, 2006). The Islamic Banks seems to master the maturity mismatch, which can generate a problem of liquidity when the bank gives loans in the long term and borrows in the short term. According to Bourakba and Belouafi (2015), over the period 2000-2012, the Islamic Banks of the Gulf practice the positive maturity of transformation and create more money than they destroy. The credit risk is a source of

instability in the banking system (McNeil et al. 2005). Ferhi and Chkoundali (2015) suggests that the CB has an important credit risk with regard to the BI. According to Shepherd and al. (1997) a mismanagement of the internal costs intensifies the credit risk. Abedifar et al. (2012) show that, with regard to the CB over the period 1999-2009, the risk of insolvency is lesser in the small ISLAMIC BANKS. Due to the insufficient or unconvincing internal practices the operational risk influences the decision-making by various ways, in particular the lack of experience and the absence of familiarity with the financial instruments (Ray & Cashman, 1999; Srairi, 2010). The bank soundness depends on the financing of profitable investments and on its reputation. Other than the conventional risks, BI face specific risks: the translated commercial risk, the non-compliance risk, the risk of specific investments and the entanglement of the risks. The non-compliance risk is due to the difference of interpretation between Schools of thought and, on the rebound, between the members of Sharia Board on the launching of new products. This risk affects particularly the reputation of an Islamic Banks which leads to a massive retreat of the deposits and the non-acceptance of its products by the other Islamic financial institutions. The risks are specific to some activities. Ijara contract obliges the management and the maintenance of the goods. PLS Contract requires an expensive follow-up, a negotiation on the division rates of the profits or the losses, added to the volatility of the return to the financed underlying projects.

What are the impacts of the risks on the performance of the Islamic Banks? Rare are the studies which have considered the problem of scale and risk management on performance indicators. According to Alam (2012) the banking inefficiency and the risk are positively correlated in the case of the CB and alternatively for the ISLAMIC BANKS.

The risk is positively correlated to the efficiency for 235 banks among which 70 BI distributed in 11 countries and 6 from the MENA region. Said (2013) shows that the operational risk and the credit risk are negatively correlated to the performance of 32 BI of which 11 from the MENA region and 18 of the countries of the Gulf. However, the correlation is not significant between the performance and the risk of liquidity. Sillah and al. (2015) present the same conclusion by emphasizing the idiosyncratic risk for 52 banks, belonging exclusively to the GCC.

3. Data and methodology

Our objective is to measure the specific risk and its impact on efficiency of the banks. The efficiency scores are calculated by the DEA method (Table 1) which allows determining an efficiency- border assembling the most efficient Islamic Banks. The more the dual variable Ai, with i=l,..., n, is close to 1, the more the Islamic Banks is efficient relatively to the other BI considered in the study. Indeed, to realize the level of output 3Vo the closest to the outputs situated on the border

n

of efficiency y^A^ , the bank has to use a minimum of inputs equivalent to 1. This implies that 6

7=1

is the smallest proportion of inputs used by the bank which establishes a score of efficiency. The obtained scores are indicators of efficiency with variable return to scale, which allows us to compare banks while considering their heterogeneousness in terms of size. To measure the specific risks, we opted for the construction of composite indicator by using the radial model without inputs; a variant of the DEA model directed to the outputs (Table 1) (Lovell & Pastor, 2002) making

y = urYrj

all the partial indicators converge on their maximal values (Zrelli, 2013). 'J "i-=i"r!V; represents the partial indicators of specific risks (CISR) of composite indicators construction in Islamic Banks. It is important that none of these dimensions explains on its own the composite indicator, or does not participate in an identical way in its construction. The respect for these conditions means imposing restrictions to the weights.

Table 1: DEA model

DEA Model for the efficiency scores

DEA model for the composite indicator

Min SS

1=1 r=1

Subject to :

n

yro -£ y<ixj + S — 0 j—1

n

Oxo -Ë XjXj - S = 0 j=1

tt * 0

j=1

With j=1,...,n Sr > 0 r= 1, ...,s

S > 0 i=1.....m

MinSi ViXi0 — IC0 Subject to :

I

With N the number of the considered DMU (Banks)

Restriction A: VrJ * 00001 soitu>- ^ 00001 Restriction B:Vi + V2+V3 + Va = 1

Source: Author's conception

Figure 1: Determinants of the calculated indicators

Performance Indicators

Risks

ROAA : return on avg assets as Net Income/Average Total Assets

ROEA : return on avg equity as Net Income/ Average Total Equity

Efficiency Score :

Inputs :

* Deposits and short term loans

* Corporal immobilisations

* Total payed interests Outputs :

* Gross loans

* Other productive assets productifs

* Total receives interests

loudness:

Zscore=(E(ROA)+CAR)/cr ROA

ROA: Return on Assets

CAR (Total capital ratio) Equity/Total assets

standard deviation of the ROA is calculated for each bank for the ^period 2007-2014_^

Specific risks ( SRCI):

• LLP: Loan Loss Porvisions)

• The inverse of present members of Sharia Board

• Part of specific contracts of the total assests

Source: Author's conception

The initial sample represents a panel of 53 Islamic Banks from 11 Countries of the MENA region and for a period from 1998 to 2014. This panel represents three types of countries; five Petro-monarchies which are Arabia Saudi, Bahrain, Kuwait, Qatar and, EAU. For_Arabia Saudi, Iran and the Yemen, Sharia is the source of law. Mauritania is not an oil-producer, but it adopts the Sharia. The non-oil producers constitutionally independent from the Sharia are Egypt, Jordan, and Syria.

The obtained efficiency scores (Available at the authors) show that Islamic Banks of Petro-monarchies countries are more efficient with an average score of 0.8 points. Ian makes an exception with an average score of 0.64 points. Although some Islamic Banks remain constantly on the border of efficiency, the general trend is a loss of efficiency in 2004 except the Islamic Banks of UAE with an improvement of the efficiency to an average of 0.89 points from 2011. The Islamic Banks of Saudi Arabia and Qatar present high and steady levels of efficiency. The most efficient ISLAMIC BANKS, with respective average scores of 0.87points and 0.76 points are from Egypt and Bahrain. The Islamic Banks of Tunisia, Jordan, Syria and Yemen are not efficient.

The partial indicators used in the construction of the SRCI (Figure 1) show that the more the SRCI is close to one, the more the BI deal with specific risk. The Sharia board guarantees the "Halal"

of the financial products. The analysis of the annual reports of Islamic Banks shows that for the countries, which apply Sharia, Islamic Banks do not give information on the Sharia board represented generally by a single person which is the case of Iran. Yet, according to the theory of agency as well as the asymmetry of information, a board represented by a single person cannot guarantee the conformity with Sharia. Therefore, with a large number of people who sit on the board, the bank tries to send a positive signal for its conformity. The question that arises is: does Islamic Banks need a numerous board to ensure to the clients that it respects the Sharia law? Along with the criterion of application of Sharia as a source of law, the distinction of Petro- monarchies shows a strong disparity in terms of specific risk. Iran is the most exposed to the specific risk with an SRCI equal to one. Other countries of Petro-monarchies are less exposed with indicators not exceeding the average of 0.54 points. The lowest SRCI are observed in the non-oil producing countries with a maximum of 0.33 points in the case of Tunisia and an average of 0.15 points in the case of the Yemen.

4. The analysis of the correlations

According to the correlation matrix by country (Appendix 1), the SRCI has no significant effect on the efficiency scores. The SRCI affects negatively and significantly the ROEA of the Qatar and the UAE and the ROAA of Saudi Arabia. The correlation between ROAA and SRCI is positive for the Kuwait. Therefore, the specific risk affects negatively the indicators of performance of the Islamic Banks of the Qatar, the UAE and Saudi Arabia; and effects positively those of the Islamic Banks of the Kuwait. The LLP presents a negative and significant effect on the ROAA and the ROEA of the Islamic Banks of the Yemen, the Kuwait and the UAE. The size of the Sharia board affects negatively and significantly the efficiency of the Egyptian Islamic Banks of the ROEA and ROAA of the Islamic Banks of Kuwait, the UEA, the Bahrain and Syria. An important size of the Sharia board (6 members) is a guarantee of the Sharia-compliance and consequently a good reputation of the bank which affects positively the indicators of performance. The results bring us to ask if there is a bank endeavour to guarantee this conformity.

Table 2: Spearman and Kendall correlation tests : Efficiency, ROAA, ROEA, SRCI

2007 2008 2009 2010 2011 2012 2013 2014

Correlation SRCI- Efficiency

Spearman's rho 0.6719 0.4335 0.3631 0.1504 0.3537 0.0714 -0.1056 0.2040

Prob > |t| 0.0016 0.0212 0.0529 0.4035 0.0343 0.6532 0.4847 0.2128

Kendall's tau-a 0.5322 0.3386 0.3005 0.1515 0.2952 0.0848 -0.0783 0.1903

Kendall's tau-b 0.5322 0.3386 0.3005 0.1515 0.2952 0.0848 -0.0783 0.1903

Kendall's score 91 128 122 80 186 73 -81 141

SE of score 28.583 50.616 53.310 64.539 73.417 92.273 105.617 82.666

Prob > |z| 0.0016 0.0121 0.0232 0.2209 0.0117 0.4352 0.4488 0.0903

Correlation ROAA-SRCI

Spearman's rho 0.2754 0.0739 0.3089 0.2326 0.3454 0.1093 0.1887 0.2350

Prob > |t| 0.2537 0.7086 0.1030 0.1927 0.0391 0.4908 0.2093 0.1498

Kendall's tau-a 0.1696 0.0370 0.1970 0.1667 0.2063 0.0848 0.1343 0.1633

Kendall's tau-b 0.1696 0.0370 0.1970 0.1667 0.2063 0.0848 0.1343 0.1633

Kendall's score 29 14 80 88 130 73 139 121

SE of score 28.583 50.616 53.310 64.539 73.417 92.273 105.617 82.666

Prob > |z| 0.3273 0.7973 0.1384 0.1777 0.0789 0.4352 0.1913 0.1466

Correlation ROEA-SRCI

Spearman's rho 0.5526 0.3114 0.2892 0.2276 0.3156 0.2101 0.1304 0.2328

Prob > |t| 0.0141 0.1067 0.1282 0.2027 0.0608 0.1817 0.3876 0.1538

Kendall's tau-a 0.3801 0.2275 0.1823 0.1591 0.2286 0.1475 0.0763 0.1768

Kendall's tau-b 0.3801 0.2275 0.1823 0.1591 0.2286 0.1475 0.0763 0.1768

Kendall's score 65 86 74 84 144 127 79 131

SE of score 28.583 50.616 53.310 64.539 73.417 92.273 105.617 82.666

Prob > |z| 0.0252 0.0931 0.1709 0.1984 0.0514 0.1721 0.4602 0.1158

N 19 28 29 33 36 42 46 39

Source : Author's calculation based on Bankscope et bank's reports

The part of the specific contracts (PLS and Ijara) in the whole portfolio of the bank affects significantly and negatively the performance indicators of the Islamic Banks of Iran and Egypt (score of efficiency), UAE and Syria (ROEA and ROAA) and of Jordan (ROAA). The specific contracts are the most intense in every type of risk. The correlation matrix allows us to analyse the impact of

the partial indicators of the specific risk on the default risk (Z-score). For Egypt, there is a significant and positive correlation between the LLP and the Z-score. The Islamic Banks with a Sharia Board counting numerous members send a negative signal, which increases the fragility of banks. In fact, results shows a negative coefficient of correlation between the variable Sharia and the Z-score in the cases of Iran, Jordan, Saudi Arabia, Qatar and UAE.

According to the obtained coefficients of correlation of Spearman and Kendall, there is a positive and significant correlation between the specific risk and the efficiency in 2007-2009, 2012 and 2014 (Table 2). This result is confirmed by the statistically significant coefficients of rho and tau.

The weights allocated to the various partial indicators in the calculation of the SRIC show that the size of the Sharia Board and the part of the specific contracts are the main determinants of the specific risk. The more the size of the Sharia Board is low, the more important is the probability not to be Sharia Compliant.

4. The Panel analysis

4.1. Methodology

We use a panel analysis to define the effects of the Composite indicator and the partial indicators on the performance of the Islamic banks measured by a calculated efficiency score the average returns on equity and the average returns on assets. With the specific risks, we consider the credit risk, the liquidity risk and the soundness risk. We estimate first the model with whole sample and then we consider a subsample without Islamic banks from Iran.

There are differences between Islamic banks of the MENA region. These differences can be related to specific characteristics which can be fixed and appropriate to the banks of the sample (model with fixed effects) or random characteristics model with random (unpredictable) effects FGLS). The specification test of Hausman capture the individual characteristics of the banks and helps to decide which model we have to use (Fixed or randomized model). The Hausman-Taylor test based on the differences between the Hausman-Taylors and FGLS estimators permit to identify the best method to use (Hausman-Taylor if the obtained probability is less than 5 percent).

4.2. Results and interpretation

The Hausman-Taylor test shows that we have to use the FGLS method. Models with the partial indicators of specific risks are more significant than the models with the composite indicator of specific risks (Table 3).

The Composite indicator of the specific risks presents a positive and significant effect only on the performance indicators of Average Returns on Assets and the Average Returns on Equity which confirms the results obtained by the correlation matrix. The More banks are exposed to the religious risk, the more she is performing. Partial indicators can explain this positive impact. In fact, related coefficients to the Sharia Board and the Loss Loans Provisions are negatively significant, which is not the case of the part of specific contracts of the total assets. Furthermore, the obtained weights of each partial indicator show that the part of the specific contracts contributes the more in the construction of the composite indicator of the specific risks. The size of the Sharia Comity is supposed to send a positive signal on the respect of the Islamic economic laws. Nevertheless, it can affect negatively the performance of the banks, especially the returns on equity, if there is an agency problem. An important Loss loans provision to prevent losses of the profit loss sharing contracts reduce the performance of the Islamic banks. Inversely, an important part of specific contracts of the total assets present enhance all the performance indicators, especially when these contracts are dealing with non-risked sectors (lack of speculation, and transactions based on tangles).

The long-term and short-term liquidity risks present a positive and significant impact on respectively the efficiency and the average return on equity. The credit portfolio represents its main sources of the revenue. This performance is explained by the absence of the risks in these types of product (Olson & Zoubi, 2011).

The short-term liquidity risk highlights the capacity of the bank to tackle the liquidity perturbations on the short-term. The more this liquidity risk ratio is important, the more the bank is not able to face the short-term maturity. According to the norms of the Islamic finance, Banks can use equities to tackle the volatility of its liquidities.

The Z-score, used as a proxy of the soundness risk, present a positive impact on the efficiency score and the ROAA. Srairi (2013) shows that there are no differences between conventional and Islamic banks in terms of insolvency risk.

Table 3 : Panel Analysis results

Sample Global sample Sub-sample (without Iran )

Composit Partial Composit Partial Composit Partial Composit Partial Composit Partial Composit Partial

Indicators e indicator e indicator e indicator e indicator e indicator e indicator

indicator s indicator s indicator s indicator s indicator s indicator s

Endogenous variable Efficiency Efficienc Score_ y score ROAA ROAA ROEA ROEA Efficiency Efficiency score score ROA ROA ROE ROE

CISR -0.0193 (-0.4879) 1.6132** (2.2212) 7.1223*** (2.6568) -0.0212 (-0.5048) 1.6923** (2.1110) 6.6129** (2.2507)

LLP -0.0000 (-0.1811) -0.0003** (-2.3172) -0.0015*** (-2.9387) -0.0000 (-0.9214) -0.0003* (-1.8405) -0.0017*** (-2.8382)

Sharia Comity -0.0060 (-0.3356) -0.6846 (-1.5567) -2.5609** (-2.1216) -0.0160 (-0.9575) -0.9655** (-2.2403) -2.4887** (-2.1360)

Part of

specific 0.0033*** 0.0414*** 0.2577*** 0.0019 0.0329 0.1449

contracts (3.2395) (2.8741) (4.4815) (1.5156) (1.3235) (1.3872)

Credit risk -0.0000 0.0006 0.0147 0.0125 -0.1007 -0.1144 -0.0003 0.0004 0.0198 0.0127 -0.0404 -0.0069

(-0.0058) (0.1236) (0.2322) (0.1826) (-0.4229) (-0.4409) (-0.0692) (0.0742) (0.2937) (0.1955) (-0.1766) (-0.0301)

Long term Liquidity risk 0.0026** 0.0026** 0.0168 0.0209 0.0938 0.1177 0.0021* 0.0022* 0.0183 0.0257 0.1000 0.1400*

(2.0432) (2.2262) (0.7361) (0.9259) (1.3045) (1.5420) (1.7010) (1.9517) (0.8315) (1.1999) (1.4312) (1.9533)

Short term liquid risk -0.0159 0.0016 4.5189 3.5176 26.1023** 22.6222 0.0174 0.0279 5.1486* 3.3956 25.0178** 18.2464

(-0.0686) (0.0066) (1.6053) (1.1416) (2.0123) (1.5611) (0.0821) (0.1297) (1.8268) (1.0789) (2.1565) (1.3025)

lnZscore 0.0606 0.0615* 0.4731* 0.3505* 2.5135 2.1218 0.0551 0.0537 0.5558* 0.3702* 2.8415* 2.1756*

(1.6371) (1.6948) (1.6826) (1.6690) (1.5210) (1.4711) (1.5777) (1.5735) (1.8369) (1.8056) (1.8792) (1.8321)

Age -0.0001 -0.0001 -0.0008* -0.0009*** 0.0012 -0.0003 0.0030 0.0032 0.0212 0.0464** 0.2386** 0.2517**

(-1.0439) (-1.2227) (-1.8844) (-2.5844) (0.4958) (-0.1298) (1.5758) (1.6142) (1.0421) (1.9994) (2.3569) (2.2900)

Ownership 0.0259 0.0126 0.2781 -0.5363 4.5497 1.4208 0.0122 -0.0126 0.2044 -0.9038 3.2773 -0.4802

(0.3970) (0.1952) (0.3118) (-0.4481) (1.3735) (0.3721) (0.1935) (-0.1998) (0.2051) (-0.7681) (0.8737) (-0.1303)

Size 0.0344** 0.0373*** -0.0659 0.1940 -0.5422 0.4022 0.0490*** 0.0523*** -0.0965 -0.0486 -0.2835 0.5378

(2.3208) (2.7243) (-0.5566) (0.7518) (-0.8003) (0.4543) (3.5226) (3.2938) (-0.3128) (-0.1603) (-0.2541) (0.4582)

Concentratio n -0.0119 -0.0105 2.0011*** 2.5407*** 10.2696*** 13.3826** * -0.0262 -0.0255 1.9644** 2.6981** 9.4582*** 12.8851** *

(-0.2607) (-0.2346) (3.2063) (3.3038) (3.8356) (4.0014) (-0.5462) (-0.5359) (2.4328) (2.5286) (2.7519) (3.0674)

GDPgrowth 0.0006 0.0007 0.1373*** 0.1281*** 0.4754*** 0.4323** 0.0007 0.0009 0.1788*** 0.1471*** 0.7425*** 0.6479***

(0.2937) (0.3687) (3.2834) (3.2255) (2.6628) (2.4812) (0.3064) (0.3627) (3.2743) (3.2087) (3.6096) (3.4412)

Inflation 0.0001 -0.0000 0.0815 0.0887* 0.3498* 0.3973** 0.0010 0.0009 0.0611 0.0850 0.1822 0.2643

(0.0306) (-0.0075) (1.5311) (1.8481) (1.7768) (2.1374) (0.3123) (0.3041) (1.0396) (1.5779) (0.8380) (1.2598)

Petromonach ic country 0.2237*** 0.2163*** 0.8848* 1.5544** 0.5215 3.4634 0.2032*** 0.2058** 0.8421 2.4690*** -0.9255 2.7576

(3.0405) (2.9520) (1.8329) (2.5371) (0.1968) (1.1316) (2.7346) (2.4625) (1.0349) (2.9317) (-0.2805) (0.7899)

Constant -0.0027 -0.0081 -4.1472* -2.8140 -14.4776 -10.2372 -0.1113 -0.0782 -4.6121 -1.1430 -20.2229* -15.5087

(-0.0159) (-0.0470) (-1.6963) (-1.4635) (-1.3853) (-1.0427) (-0.7472) (-0.4768) (-1.3035) (-0.4363) (-1.6915) (-1.4314)

Observations 229 227 229 227 229 227 200 199 200 199 200 199

Number of Banks 48 48 48 48 48 48 41 41 41 41 41 41

R-squared 0.3037 0.3099 0.2222 0.2590 0.3085 0.3171 0.4150 0.4281 0.2328 0.2951 0.3562 0.3880

Breusch Pagan Wald 0.0000 0.0000 0.0002 0.0074 0.0000 0.0000 0.0000 0.0000 0.0013 0.0540 0.0013 0.0360

140.15 500.86 63.35 104.96 219.09 134.41 209.85 540.49 93.72 150.64 99.44 183.23

Sargan 0.9241 0.2266 0.2266 0.3337

Hausman 0.2538 0.4023 0.4454 0.6371 0.0651 0.3359 0.1624 0.2451 0.0979 0.5935 0.4369 0.0998

Source: author's calculations

It seems logical to think that the more a bank matures, the more she acquires some experience and adopts sophisticated products to succeed in managing her costs and risks. Accordingly, the age should have a positive effect on the banking performance. However, the coefficient associated with this variable is negative and it is significant with ROAA. It is possible that

a selection bias exists in our sample. The regression of our econometric model on a sub- sample allows identifying this limit.

The size of the Islamic banks, measured by the logarithm of the total of assets, presents a positive and significant impact only on the efficiency score. The Islamic banks of the MENA region are more efficient in terms of income when they are bigger. Indeed, they present important profitable assets, economies of scale and product diversification (Huges et al, 2001). This positive relation between the performance and the size was confirmed by Akhavein et al, (1997), Sufian and Habibullah (2009), Olson and Zoubi (2011).

To establish a proxy of the competitiveness, we use as Concentration ratio of the deposits. Its coefficient presents a positive and significant effect only on the ROAA and the ROEA. If an Islamic Bank benefit from a monopoly situation, especially in the MENA region, it will be more profitable. This result is also confirmed by the work of Kamarudin et al, (2014).

GDP growth, inflation and Petro-Monarchies are used as macroeconomics variables and present positive and affect positively and significantly the performance of the Islamic banks.

The economic growth enhances banking activities and performance through the increase of the demand of the deposits. Although, Islamic Banks offer higher lending than the conventional banks, the inflation can influence positively the efficiency of the Islamic banks if a large part of their results comes from direct investments because of participation in other commercial activities (Murabaha). Petria and al (2015), Kamarudin and al. (2014) And Olson and zoubi (2011) also observed this positive correlation while Wahidudin and al (2014) observed a negative impact on the profitability of the Islamic Banks of the MENA region.

The more countries are oil exporters the more their Islamic banks are successful. Generally, these countries have a gross domestic product by capita higher which allows reducing the risk supported by the bank, especially when it grants the loans (Angkinand and Wihlborg, on 2007; Laeven and Levine, on 2009; Srairi, on 2013).

After having dropped the banks of Iran of our global sample, the results of the estimation confirm the robustness of the results obtained previously in the global sample (Table 4). Both partial indicators of the religious risk (Loans Loss provisions and the members in Sharia), the risks of short and long-term liquidity, the risk of insolvency, the market Concentration and the macroeconomic variables the determiners of the performance of the Islamic banks. In addition, the results identify a selection bias: the variable Age becomes positive and significant while the coefficient of "specific contracts" is not significant.

The existence of the banks of Iran in our global sample risk is to bias our results: although they are the most mature banks who adopt the finance Islamic, they did not develop their products and did not diversify their assets to avoid the risk of not respecting the standards of the Sharia. Iran is a country which applies the Sharia as a source of law and law. The Iranian banks are thus unanimously Islamic banks. What distinguishes Iran from Malaysia, which is also an Islamic country, is the delay of development of the financial products always because of the concern of respect for the Islamic laws.

5. Conclusion

The Islamic Banks are distinguished from CB in terms of contracts and risks. Until now, the analysis of the specific risks for the Islamic Banks has not been handled. Our study represents an attempt to assess this type of risks and its impact on the performance of the Islamic Banks. The use of the efficiency scores, ROEA, ROAA and the SRCI in the analysis of the correlations and the non-parametric tests of ranks lead us to formulate three main conclusions.

First, the results show a positive correlation between the efficiency scores and the specific risk. Second, divergent impacts of each partial indicator of the specific risks composite indicator on the performance indicators is demonstrated according to the studied country. Finally, the vulnerability of the Islamic banks is based on their capacity to manage the specific risk through the Sharia Board and the allocation of assets in the specific contracts.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in the online version, at https://dx.doi.org/10.14254/jems.2018.3-1.4

Funding

The authors received no direct funding for this research.

Citation information

Zrelli, N., Berguiga, I., & Abdallah, A.(2018). Specific risks and profitability of Islamic Banks in

MENA region. Economics, Management and Sustainability, 3(1), 44-57. doi:10.14254/jems.2018.3-

1.4.

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Appendix 1: Correlations matrix by country

Egypt Efficiency score ! ROEA ROAA SRCI LLP Sharia boardSpecific contracts Z-score

Efficiency Score 1.0000

ROEA -0.0247 1.0000

(0.9459)

ROAA 0.0813 0.9918*** 1.0000

(0.8232) (0.0000)

SRCI -0.4135 0.2255 0.1483 1.0000

(0.2349) (0.5310) (0.6826)

LLP 0.3883 -0.1413 -0.1022 0.3402 1.0000

(0.2675) (0.6970) (0.7787) (0.3362)

Sharia Board -0.7188*** 0.3700 0.2552 0.5528 * -0.3513 1.0000

(0.0192) (0.2926) (0.4767) (0.0974) (0.3195)

Specific Contracts -0.7350** -0.4095 -0.5153 0.3886 -0.1915 0.6813 ** 1.0000

(0.0154) (0.2399) (0.1275) (0.2671) (0.5961) (0.0301)

Z-score 0.2982 (0.4027) 0.0921 (0.8002) 0.0911 (0.8024) 0.3909 (0.2640) 0.6717 ** (0.0334) -0.1213 (0.7386) -0.1791 (0.6205) 1.0000

Iran

Efficiency Score 1.0000

ROEA 0.0554 (0.7375) 1.0000

ROAA -0.0633 0.9106*** 1.0000

(0.7018) (0.0000)

SRCI (0.0000) (0.0000) (0.0000)

LLP 0.2353 0.0002 0.0149 1.0000

(0.1551) (0.9990) (0.9292) (0.0000)

Sharia Board 0.1992 0.0845 -0.0719 -0.0421 1.0000

(0.2241) (0.6090) (0.6634) (0.0000) (0.8021)

Specific Contracts -0.5269 *** -0.1932 -0.1296 -0.0989 -0.4578 *** 1.0000

(0.0006) (0.2386) (0.4317) (0.0000) (0.5546) (0.0034)

Z-score 0.1281 (0.4371) 0.1031 (0.5321) 0.1313 (0.4255) (0.0000) 0.0640 (0.7027) -0.3124 * (0.0529) -0.2505 (0.1240) 1.0000

Jordan

Efficiency Score 1.0000

ROEA 0.1457 1.0000

(0.6044)

ROAA 0.2057 0.9274 *** 1.0000

(0.4621) (0.0000)

SRCI -0.2982 0.1664 0.2079 1.0000

(0.2803) (0.5533) (0.4572)

LLP 0.2205 0.1263 0.1896 -0.0337 1.0000

(0.4297) (0.6539) (0.4986) (0.9050)

Sharia Board -0.0578 -0.1782 0.0508 -0.4856 * 0.0441 1.0000

(0.8378) (0.5252) (0.8574) (0.0665) (0.8761)

Specific Contracts -0.1675 -0.4382 -0.5833 ** 0.3168 -0.4460* -0.6226 ** 1.0000

(0.5507) (0.1023) (0.0224) (0.2500) (0.0957) (0.0132)

Z-score -0.1062 (0.7064) -0.1420 (0.6137) -0.4050 (0.1342) 0.3769 (0.1661) -0.2680 (0.3343) -0.9069 *** (0.0000) 0.8330 *** (0.0001) 1.0000

Arabia Saudi

Efficiency Score 1.0000

ROEA -0.3670 1.0000

(0.1115)

ROAA -0.4208 * 0.8919 *** 1.0000

(0.0646) (0.0000)

SRCI 0.3432 -0.3092 -0.5095 ** 1.0000

(0.1385) (0.1847) (0.0218)

LLP 0.2247 -0.0772 -0.1728 0.6571 *** 1.0000

(0.3408) (0.7464) (0.4663) (0.0016)

Sharia Board -0.1123 0.2588 0.2936 0.4341** 0.4659 ** 1.0000

(0.6374) (0.2705) (0.2091) (0.0558) (0.0384)

Specific Contracts 0.0766 0.0898 0.1957 -0.0515 -0.1777 -0.0520 1.0000

(0.7483) (0.7066) (0.4083) (0.8294) (0.4536) (0.8276)

Z-score -0.0996 (0.6760) -0.0930 (0.6966) 0.2328 (0.3234) -0.7743 *** (0.0001) -0.6274 *** (0.0031) ' -0.4369 * (0.0541) 0.2543 (0.2793) 1.0000

Kuwait

Efficiency Score 1.0000

ROEA 0.1462 (0.3010) 1.0000

ROAA 0.1718 0.9324 *** 1.0000

(0.2234) (0.0000)

SRCI 0.0382 0.3081 0.25 00 * 1.0000

(0.7880) (0.0263) (0.0738)

LLP -0.0614 -0.2323 * -0.2652 -0.0932 1.0000

(0.6653) (0.0975) (0.0575) (0.5111)

Sharia Board 0.1174 -0.3003 ** ■ -0.3613 *** -0.1942 0.6642*** 1.0000

(0.4071) (0.0305) (0.0085) (0.1678) (0.0000)

Specific Contracts -0.0774 -0.0972 -0.0946 -0.1384 0.6114*** 0.3255** 1.0000

(0.5853) (0.4929) (0.5048) (0.3278) (0.0000) (0.0185)

Z-score 0.1922 (0.1722) -0.1824 (0.1957) -0.2185 (0.1197) -0.1813 (0.1984) -0.1783 (0.2059) 0.1677 (0.2346) -0.1000 (0.4805) 1.0000

Qatar

Efficiency Score 1.0000

ROEA -0.0192 1.0000

(0.9377)

ROAA 0.0198 0.8663*** 1.0000

(0.9358) (0.0000)

SRCI 0.3271 -0.4003 * -0.1790 1.0000

(0.1716) (0.0895) (0.4634)

LLP -0.3125 0.0274 0.1014 -0.0099 1.0000

(0.1927) (0.9113) (0.6796) (0.9680)

Sharia Board 0.0578 -0.2608 -0.3191 0.0031 -0.0429 1.0000

(0.8141) (0.2809) (0.1830) (0.9899) (0.8617)

Specific Contracts -0.0001 0.0148 0.0485 -0.3011 -0.2679 0.3657 1.0000

(0.9996) (0.9520) (0.8436) (0.2104) (0.2675) (0.1236)

Z-score -0.0497 (0.8400) 0.2369 (0.3289) 0.2602 (0.2821) -0.2334 (0.3362) -0.1934 (0.4276) -0.6520 *** (0.0025) -0.4502 * (0.0531) 1.0000

UAE

Efficiency Score 1.0000

ROEA 0.0129 (0.9377) 1.0000

ROAA 0.1112 0.7426*** 1.0000

(0.5002) (0.0000)

SRCI -0.0800 0.4044*** 0.0748 1.0000

(0.6282) (0.0107) (0.6510)

LLP 0.0800 -0.2322 -0.3350 ** 0.2792* 1.0000

(0.6281) (0.1549) (0.0371) (0.0852)

Sharia Board 0.0330 -0.3514 ** -0.6698 *** 0.3996 ** 0.5024*** 1.0000

(0.8419) (0.0283) (0.0000) (0.0117) (0.0011)

Specific Contracts -0.0311 -0.2910 * -0.6475 *** 0.4771*** 0.4424*** 0.9662*** 1.0000

(0.8529) (0.0764) (0.0000) (0.0025) (0.0054) (0.0000)

Z-score -0.0085 (0.9591) -0.1664 (0.3114) -0.3483 ** (0.0298) 0.3231** (0.0448) 0.0568 (0.7311) 0.3936** (0.0132) 0.5072 *** (0.0012) 1.0000

Bahrain

Efficiency Score 1.0000

ROEA -0.2528 * 1.0000

(0.0678)

ROAA -0.1147 0.8572*** 1.0000

(0.4133) (0.0000)

SRCI 0.1643 0.1093 0.0326 1.0000

(0.2397) (0.4313) (0.8149)

LLP -0.0597 -0.0209 -0.0193 -0.1142 1.0000

(0.6711) (0.8805) (0.8899) (0.4110)

Sharia Board -0.0820 0.3506*** 0.3654*** 0.0587 0.0519 1.0000

(0.5595) (0.0094) (0.0066) (0.6732) (0.7095)

Specific Contracts 0.0399 0.0322 -0.0056 0.0809 0.2416* 0.2202 1.0000

(0.7765) (0.8169) (0.9679) (0.5611) (0.0784) (0.1095)

Z-score 0.0102 (0.9423) -0.0945 (0.4965) -0.1228 (0.3763) -0.0333 (0.8109) -0.1299 (0.3493) 0.0630 (0.6511) 0.0816 (0.5572) 1.0000

Syria

Efficiency Score 1.0000

ROEA 0.1774 1.0000

(0.4812)

ROAA 0.3097 0.8578*** 1.0000

(0.2111) (0.0000)

SRCI -0.1656 0.0009 0.1229 1.0000

(0.5113) (0.9971) (0.6270)

LLP 0.0913 0.3822 0.1865 -0.0963 1.0000

(0.7185) (0.1175) (0.4587) (0.7038)

Sharia Board 0.2819 0.4552* 0.5978*** 0.0562 0.4865** 1.0000

(0.2572) (0.0577) (0.0088) (0.8248) (0.0406)

Specific Contracts 0.3261 0.6059*** 0.5882*** -0.2816 0.5960*** 0.6549*** 1.0000

(0.1866) (0.0077) (0.0102) (0.2576) (0.0090) (0.0032)

Z-score 0.1272 (0.6151) 0.3271 (0.1852) 0.2269 (0.3652) -0.4651* (0.0518) 0.3175 (0.1992) 0.1143 (0.6517) 0.6105 *** (0.0071) 1.0000

Yemen

Efficiency Score 1.0000

ROEA 0.2839 (0.2251) 1.0000

ROAA 0.2852 0.7425*** 1.0000

(0.2229) (0.0002)

SRCI LLP

Sharia Board Specific Contracts Z-score

-0.282S -0.3734 -0.3136 1.0000

(0.227S) (0.1048) (0.1781)

-0.2701 -0.4770** -0.S197** 0.1428 1.0000

(0.249S) (0.033S) (0.0188) (0.S482)

(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)

0.0742 -0.3S41 0.1323 0.2791 -0.0814 1.0000

(0.7627) (0.1369) (0.S894) (0.2472) (0.7404) (0.0000)

0.3246 0.S294** 0.3366 -0.4901** -0.2S21 -0.1479

(0.1626 (0.0164) (0.1467) (0.0283) (0.283S) (0.0000) (0.S4S6)

1.0000

Source: author's calculation

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