Liquidity Risk of Islamic Banking in Islamic and Non Islamic

Countries

Kharisya Ayu Effendi

and Shelfi Malinda

Department of Management, Faculty Business and Management, Widyatama University, Cikutra no 204 A, Bandung,

Indonesia

Department of Management, Sriwijaya University, Palembang, Indonesia

Keywords: Liquidity Risk, Systematic Factors, Islamic Banking.

Abstract: This study aims to analyze systematic factors on the risk of Islamic banking liquidity in Islamic and non

Islamic countries. Data used in this study were 105 banks from 24 countries with 18 Islamic countries and 6

non Islamic countries from 2007 to 2016. The result of this study is that there are different factors on the

risk of Islamic banking liquidity in Islamic and non Islamic countries, i.e. Islamic countries are not affected

by the systematic factors, while non Islamic, interest rate affected. This is because Islamic banking in non

Islamic countries still following the rules of the economy using interest rates. This result has a different

impact when the economic system imposed on a country is different, especially in the application of interest

rate policies. The impact can make Islamic banking in non Islamic countries more stable than systematic

factors that cannot be diversified.

1 INTRODUCTION

Liquidity problems have been recognized as a major

obstacle to the growth of Islamic banking (Vogel

and Hayes, 1998). In fact, in principle Islamic

banking business aims to provide good liquidity

management is done on all real business transactions

(Antonio, 2001). This is a serious problem for

Islamic banking in maintaining the sustainability of

its business. Liquidity problem in banking is called

as liquidity risk. Liquidity risk makes the financial

crisis worse (Brunnermeier and Yogo, 2009). As

Cetorelli and Goldberg (2012) argued that improper

bank liquidity management can increase the spread

of global liquidity shocks globally.

In theory, Islamic banking has a higher liquidity

risk than conventional banks. Liquidity risks faced

by Islamic banking occur due to many causes, the

lack of liquidity funding sources is a fundamental

problem faced by Islamic banks due to the absence

of secondary markets and money markets available

for Islamic finance, thus complicating the maturity

problem of unlawful liabilities in Islamic banking,

consequently Islamic banks cannot produce or

provide sufficient returns to depositors (Ray, 1999).

Other causes are the limited number of financial

instruments and the lack of harmony between central

banks and Islamic banks, when the central bank

refused to provide loans to Islamic banks without

interest payments (How et al., 2005 and Tiby, 2010).

In addition to the problems above, liquidity risk in

Islamic banking is higher than that of conventional

banking because Islamic banking is related to

matters such as real assets, business cycles,

cooperation among business partners and the

behavior of policymakers. This lack of harmony

between business partners is a clear decline in

business conditions (Iqbal, 2012).

However, different opinions about the risk of

Islamic banking liquidity are put forward by some

researchers. Zineldin (1990) found that Islamic

banking has superior liquidity risk management than

conventional banking because Islamic banking has

abundant funds. The study covers Egypt and

Malaysia as Islamic countries and found that Islamic

banking is an alternative to conventional banking

today.

In addition, Ahmed (2001) revealed that most

Islamic banking in the Middle East is currently

experiencing an abundance of liquidity. As well,

Kazarian and Koko (1987) argue that Islamic banks

in Egypt have a positive role in mobilizing small

Ayu Effendi, K. and Malinda, S.

Liquidity Risk of Islamic Banking in Islamic and Non Islamic Countries.

DOI: 10.5220/0008439402810288

In Proceedings of the 4th Sriwijaya Economics, Accounting, and Business Conference (SEABC 2018), pages 281-288

ISBN: 978-989-758-387-2

281

deposits from less wealthy people. The opinion of

the researchers above is a study of Islamic countries,

so as to support the Islamic banking capital in the

country well.

According to the previous studies such as How et

al. (2005), Waemustafa and Sukri (2016) and

Megeid (2017) Islamic banking in Islamic countries

(which adheres to law, economics and politics

according to Islamic law, "Every country controlled

by law- Islamic law is an Islamic state whereas

every state controlled by different people's laws is a

non-Islamic state and no third country type (Al-

Adab Asy-Syar'iah 212) ") has a more robust

liquidity risk management system than any other

country. This is evidenced by the results of his study

that systematic factors which are not diversifiable

factors have no effect on liquidity risk and that affect

liquidity risk in Islamic banking in Islamic countries

is only the expansion of financing. The above

findings provide information that Islamic banking in

Islamic countries (in terms of liquidity risk

management) is more resilient than other countries,

as it is not affected by systematic circumstances.

The difference between theory and empirical

about liquidity risk in Islamic banking above

provides an opportunity for further analysis.

Therefore, this study aims to analyze systematic

factors on the risk of Islamic banking liquidity in

Islamic countries and non-Islamic countries (legally,

economically and politically according to Islamic

law). So it can be seen whether the different legal,

economic and political systems can have an impact

on the risk of liquidity in Islamic banking.

2 LITERATURE REVIEW

2.1 Historical of Islamic Banking

The formation of Islamic banking was initially

doubtful due to several reasons. The first reason is

because the system of free interest is impossible.

There are many opinions that say that an interest-

free banking system is something that is impossible

to do and not as common as banking in general

(Rivai et al., 2007). This opinion is naturally stated

because the banking business lives on interest. The

second reason is because of doubts about Islamic

banking financing its operations. This second reason

relates to the first reason, businesses in banking

grow and develop and finance their operations from

interest. If Islamic banking makes an interest-free

system in the banking system, then the income and

operational costs are doubtful. So that the

sustainability of the establishment of the banking is

questioned. Although there is a lot of evidence that

shows that Islamic banking is running and began its

establishment since the time of Prophet Muhammad

S.A.W and a friend of the Umayyads and Banu

Abassiyah, also in Europe (Rivai et al., 2007).

Islamic banking according to Antonio (2001) is a

banking system whose implementation is based on

Islamic law. The beginning of the establishment of

this system was based on the prohibition of usury in

Islam, namely the prohibition to lend or raise funds

by charging interest on loans / deposits. In addition,

this system was formed also due to the prohibition to

invest in illicit (prohibited) businesses and ways,

namely investing in liquor businesses and investing

with speculation. Meanwhile, the conventional

banking system cannot guarantee the absence of

these things.

For the first time, the establishment of an Islamic

bank was established in Egypt in 1963 under the

name of the Islamic bank Myt-Ghamr, whose capital

was assisted by King Faisal of Saudi Arabia. The

establishment of the Myt-Ghamr bank was

spearheaded by the Muslim Brotherhood, but did not

last long because it was immediately disbanded by

Gamal Abdul Nashr. However, the experiment of the

establishment of the Islamic banking Myt-Ghamr

(1963-1967) has been able to stimulate the thought

of the possibility of the establishment of Islamic

institutions engaged in finance and investment with

decent profits.

Then in 1970, Thalut Harb Pasha established an

Islamic bank under the name Bank Egypt. The bank

was re-established in Egypt and began operations in

1972 which is a private bank that has its own

autonomous rights. However, it is different from

Myt-Ghamr whose main activity is a profitable,

decent and lawful investment. The Egyptian Bank

has its main activities in the social field, such as

helping small businesses and helping the poor.

After that, Islamic banking began to appear in

various Islamic countries beginning with the events

of The Third Islamic Conference on February 29,

1972 in Jeddah. In a meeting attended by foreign

ministers of Islamic countries, an agreement was

reached to form a finance and economic department

under the secretary general assigned to explain the

Islamic banking system and gather opinions from

Islamic countries.

The results of the department's review were

discussed at the first meeting of the finance

ministers of the Organization of Islamic Cooperation

(OIC) in December 1973. The meeting produced a

statement to establish an Islamic banking. The rapid

development of Islamic banking turned out to be

inseparable from the contribution played by the

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Organization of Islamic Cooperation (OIC), which

since the 1970s has issued many recommendations

and encouraged its member countries to improve the

economy of the people in their respective countries.

Until finally the Islamic Development Bank (IDB)

was founded in 1975 in Jeddah which built a

milestone in the Islamic banking system, followed

by the establishment of the first private Islamic bank

in Dubai in the same year (Warde, 2000).

2.2 Islamic and Non Islamic Countries

The scholars divided the State into two parts above

based on the Qur'an and Sunnah combined with the

development of reality. Among the scholars who

assert thus are contemporary scholars, such as Ibn

Qudamah in al-Mughni (9/293), ath-Thobary in his

interpretation (6/53) and also al-Qurthubi in his

interpretation(8/57). Researchers from contemporary

scholars also concluded that because the division of

the state into two statuses is Al-Qur'an and Sunnah.

As expressed by Shaykh DR. al-Ahmadiy (ikhtilaf

ad-Darain, 1/203), Shaykh DR ‘Abid Sufyani (Daarl

Harb, p. 60), and DR Isma'il Fathoniy, (ikhtilafu ad-

Darain, 72).

Among the arguments that show the division of

shares has been implied in the Qur'an is, “Indeed,

those who have believed and emigrated and fought

with their wealth and lives in the cause of Allah and

those who gave shelter and aided – they are allies of

one another. But those who believed and did not

emigrate – for you there is no guardianship of them

until they emigrate” (Al-Anfal – 72).

In determining the rules of a country; whether it

is an Islamic or a non-Islamic country, there are

minor differences between the scholars, both salaf

and khalaf. The principal law (golabatul ahkam) that

applies is supported by the Islamic status of the ruler

(siyadah). If the applicable law is the Islamic Shari'a

in a country then it is an Islamic State.

Imam Abu Yusuf, the Hanafiyah cleric, said,

“The basis of a country is said to be an Islamic state

is the establishment of Islamic laws in it, even

though the majority of the population is non-Muslim.

And the basis of a country is said to be a non-

Muslim country is the establishment of laws other

than Islam in it, even though the majority of the

population is Muslim. The purpose of the Law here

includes the regulation of state and economic

management of a country” (al-Mabsuth Imam As-

Sarakhsi, 10/144).

The above basis can be used as a strong

foundation for the division of whether a country can

be called an Islamic state or not. If the economic

system applied is different from Islamic law, the

country is called a non-Islamic state. Thus, the

economic climate of a country will be different from

an Islamic country that does not set a benchmark

interest rate or 0% while a non-Islamic country sets

a reference interest rate.

3 DATA AND METHOD

3.1 Data Collection

The unit of analysis in this study is Islamic banking

in the World. Of the 395 Islamic banks in the world,

several issuers were taken as samples through a

purposive sampling technique. The criteria used in

sampling are:

 Islamic banking that has been established for

more than 10 years.

 Islamic banking that publishes its financial

statements on the website of each bank.

 Islamic banking that published data on the

website for 10 years from 2007 - 2016.

From the results of the random sampling criteria

above, there are 105 Islamic banks from 24 countries

in the World, 18 islamic countries (Bahrain,

Bangladesh, Egyp, Iraq, Iran, Jordan, Kuwait,

Malaysia, Maldives, Oman, Pakistan, Qatar, Saudi

Arabia, Sudan, Unit Emirat Arab, and Yamen) and 6

non islamic countries (Albania, Bosnia, Indonesia,

Philipine, South Africa, Srilanka, Thailand, Turkey).

Financial statement data obtained from the website

of bank, macroeconomic, and monetary policy data

from the world banks.

3.2 Measure And Scale of Variables

The liquidity risk variable is calculated using the

formula liquid assets / total assets. The GDP

variable is calculated using the formula 𝐶 + 𝐼 + 𝐺 +

(𝑋 - 𝑀), where C is consumption, I is the investment,

G is the state expenditure, X is the export and M is

the Import. Inflation variable is calculated using the

formula (CPI

- CPI

) / CPI

) where CPI is the

consumer price index. The Unemployment variable

is calculated using the formula of the number of

unemployed / number of labor force. The Interest

rate central bank from world bank.

3.3 Model Specification

The model specified in equation 1 is used to express

the relationship between variables:

LR = β

+β

GDP+β

CPI+β

UNEM+β

IRATE+ε

Liquidity Risk of Islamic Banking in Islamic and Non Islamic Countries

283

Where,

LR : Liquidity Risk

GDP : Gross Domestic Product

CPI : Inflation

UNEM : Unemployment

IRATE : Interest rate bank central

3.4 Method of Data Analyisis

This study uses an explanatory analysis. The test is a

panel data regression testing using Eviews 9. In the

panel data regression testing requires 3 steps,

namely: Correlation test, Model Test and

Regression. In the correlation test, the value between

variables should be <0.8 to be free from

multicollinearity. Next is the model test, this is done

to determine the best regression model. There are

four regression models of panel data namely:

Common effect, fixed effect, fixed effect with cross

section weight and random effect. There are three

test models named chow test, Hausman test and

Lagrange multiplier test. Chow test to choose the

common effect or fixed effect model, Hausman test

to choose the random effect or fixed effect model

and Lagrange multiplier test to choose common

effect or random effect. The last test used when the

result of chow test and Hausman test is not aligned.

Below is a hypothesis for model test:

 The first model is a chow test that is ho:

Common effect and ha: Fixed effect.

 The second model is the test of that ho:

Random effect and ha: Fixed effect.

 The third model is Lagrange multiplier test

that is ho: Common effect and ha: Random

effect.

 If p-value> 0.05 then accept ho and if p-

value <0.05 then reject ho.

The next step is to read the results of the panel

data regression which is the best model, whether it is

common effect, fixed effect, fixed effect with cross

section weight and random effect.

4 RESULTS AND DISCUSSION

4.1 Islamic Countries

4.1.1 Correlation Analysis

Table 1 summarizes the correlation values for all the

variables used. This test is performed to identify

some variables that have high correlation with

correlation value above 0.8. If there is a correlation

value above 0.8, then inter variables occur

multicollinearity.

Table 1: Correlation Matrix of Study Variables in Islamic

Countries.

GDP

INFL

UNEMP

IRATE

1.000

GDP

-0.002

1.000

INFL

0.129

-0.132

1.000

UNEM

-0.045

-0.171

0.228

1.000

IRATE

-0.018

-0.029

-0.290

-0.186

1.000

Test results in table 1 show that all variables

have a correlation value below 0.8. This means that

all variables are independent of multicollinearity. If

all variables are freed from multicollinearity, then

the study can be processed.

4.1.2 Model Testing

Chow Test

Testing the first model is a test using Chow test.

Table 2 below is the result of chow testing.

Table 2: Chow Test in Islamic Countries.

Redundant Fixed Effects Tests

Equation: Untitled

Test cross-section fixed effects

Effects Test

Statistic

d.f.

Prob.

Cross-section F

10.885203

-88.797

0.0000

Table 2 shows the probability result is 0.0000.

This explains that ho is rejected so that the result

obtained is a fixed effect model better than the

common effect model. Therefore, according to the

results of Chow testing, the model used is a fixed

effect model.

Hausman Test

The next model test uses the Hausman test. Table 3

below is the result of the Hausman test.

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Table 3: Hausman Test in Islamic Countries.

Correlated Random Effects - Hausman Test

Equation: Untitled

Test cross-section random effects

Test Summary

Chi-Sq.

Statistic

Chi-Sq.

d.f.

Prob.

Cross-section random

4.23449

0.3752

The results in table 3 indicate incompatibility

with the previous test, i.e. ho accepted then the

resulting result is a random effect model is better

than the fixed effect model. If in the chow and

Hausman test is not in line, then the next required

test is Lagrange multiplier test.

Lagrange Multiplier Test

The third test is the test performed if the first and

second model test results are not aligned.

Table 4: Lagrange Multiplier Test in Islamic Countries.

Lagrange Multiplier Tests for Random Effects

Test Hypothesis

Cross-section

Time

Both

Breusch-Pagan

163.5780

0.004195

163.5822

(0.0000)

(0.9484)

(0.0000)

In testing Lagrange multiplier obtained result

that ho is rejected, then best model is random effect.

In conclusion, in the selection of the best models in

chow, Hausman and Lagrange multiplier testing, the

random effect model is the best model.

4.1.3 Estimation Results

The estimation result in table 5 below is the result of

panel data regression with best choice model that is

random effect.

The estimation results in table 5 above show that the

variable of GDP, CPI, Unemployment and central

bank benchmark interest rate have no influence to

liquidity risk in Islamic banking in Islamic countries

in accordance with previous study which states that

systematic variable does not affect liquidity risk in

Islamic banking.

Table 5: Estimation Results from Random Effect in

Islamic Countries.

Variable

Independ

ent

VARIABLE DEPENDENT : LIQUIDITY RISK

Coefficie

Std. Error

Statistic

Prob.

GDP

0.15446

0.30161

0.51211

0.6087

CPI

0.21877

0.24401

0.89653

0.3702

UNEM

-0.78531

0.37631

-2.08683

0.3720

IRATE

0.02073

0.14476

0.14325

0.8861

R-

Square

0.302549

These findings are in line with previous studies

such as How et al. (2005), Waemustafa and Sukri

(2016), Haryono et al. (2016) and Megeid (2017)

that resulted in systematic factors such as

macroeconomics (GDP, CPI and Unemployment)

and monetary policy (the benchmark rate) have no

significant effect on liquidity risk in Islamic banking

in Malaysia, Egypt and Pakistan belonging to

Islamic countries.

Even Megeid (2017) found out that the risk

management of liquidity in Islamic banking in Egypt

is tougher than conventional banking. This is

evidenced from the results of his study that affect the

liquidity risk in Islamic banking in Egypt is only a

financing expansion that affects the risk of liquidity.

Therefore, Islamic banking in Egypt only needs to

maintain the quantity of financing so as not to risk

liquidity.

These findings answer previous findings so as to

strengthen evidence that Islamic banking in Islamic

countries can avoid systematic factors.

4.2 Non-Islamic Countries

4.2.1 Correlation Analysis

Table 6 summarizes the correlation values for all the

variables used. This test is performed to identify

some variables that have high correlation with

correlation value above 0.8. If there is a correlation

value above 0.8, then inter variables occur

multicollinearity.

Test results in table 6 show that all variables

have a correlation value below 0.8. This means that

all variables are independent of multicolinearity. If

all variables are freed from multicollinearity, then

the research can be proceeded.

Liquidity Risk of Islamic Banking in Islamic and Non Islamic Countries

285

Table 6: Correlation Matrix of Study Variables in Non-

Islamic Countries.

GDP

INFL

UNEMP

IRATE

1.000

GDP

0.159

1.000

INFL

0.027

0.276

1.000

UNEM

-0.266

-0.177

-0.154

1.000

IRATE

0.063

-0.146

-0.422

-0.025

1.000

4.2.2 Model Testing

Chow Test

Testing the first model is a test using Chow test.

Table 7 below is the result of chow testing.

Table 7: Chow Test in Non-Islamic Countries.

Redundant Fixed Effects Tests

Equation: Untitled

Test cross-section fixed effects

Effects Test

Statistic

d.f.

Prob.

Cross-section F

256.45670

-15,140

0.0000

Table 7 shows the probability result is 0.0000.

This explains that ho is rejected so that the result

obtained is a fixed effect model better than the

common effect model. Therefore, according to the

results of Chow testing, the model used is a fixed

effect model.

Hausman Test

The next model test uses the Hausman test. Table 8

below is the result of the Hausman test.

Table 8: Hausman Test in Non-Islamic Countries.

Correlated Random Effects - Hausman Test

Equation: Untitled

Test cross-section random effects

Test Summary

Chi-Sq.

Statistic

Chi-Sq.

d.f.

Prob.

Cross-section

random

7.135711

0.128

The results in table 8 indicate incompatibility

with the previous test, i.e. ho accepted then the

resulting result is a random effect model is better

than the fixed effect model. If in the chow and

Hausman test is not in line, then the next required

test is Lagrange multiplier test.

Lagrange Multiplier Test

The third test is the test performed if the first and

second model test results are not aligned.

Table 9: Lagrange Multiplier Test in Non-Islamic

Countries.

Lagrange Multiplier Tests for Random Effects

Null hypotheses:

No Effects

Alternative hyphotheses: Two-sided (Breusch-Pagan)

and one-sided (all others) alternatives

Test Hypothesis

Cross-section

Time

Both

Breusch-Pagan

412.2132

2.661326

414.8746

(0.0000)

(0.9484)

(0.0000)

In testing Lagrange multiplier obtained result

that ho is rejected, then best model is random effect.

In conclusion, in the selection of the best models in

chow, Hausman and Lagrange multiplier testing, the

random effect model is the best model.

4.2.3 Estimation Results

The result of estimation in table 10 below is a

regression result of data panel with best choice

model that is random effect.

Table 10.: Estimation results from Random Effect in Non-

Islamic Countries.

Variable

Independent

VARIABLE DEPENDENT : LIQUIDITY

RISK

Coeff

Std. Error

t-Statistic

Prob.

GDP

0.0048

1.0045

0.3168

CPI

-0.0098

0.0052

-1.8674

0.0639

UNEM

0.0065

0.0085

0.7659

0.4450

IRATE

-0.0091

0.0026

-3.4292

0.0008

R-Square

0.846316

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The estimation results in table 10 above show

that the factors of GDP, CPI and Unemployment

have no significant effect on Islamic banking in non-

Islamic countries. This resulted in an appropriate

outcome to a previous study of Effendi et al. (2017)

that Islamic banking in non-Islamic countries has no

impact on macroeconomic shocks. But interest rates

have a significant and negative impact on Islamic

banking in countries that do not implement Islamic

economic system, so that Islamic banking is still

highly dependent on the central bank's benchmark

interest rate. These significant and negative

influences mean that if interest rates set by a

country's central bank are high, then liquidity risk in

Islamic banking is low.

This is in line with previous researches

Bordeleau and Graham (2010) who said that rising

central bank interest rates could penalize banks for

holding liquid assets in the long term. This means

that high interest rates can lower the risk of liquidity

because banks are forced to keep their liquid assets

in the long term when interest rates are rising to

minimize the risks that can occur.

5 IMPACT

This study into the new findings that are useful as

information for non-Islamic countries should adopt

the Islamic economic system that forbids interest to

be free from various systemic problems that can lead

to economic crisis.

The impact can make Islamic banking in non-

Islamic countries more stable than systematic factors

that cannot be diversified. Thus, the Islamic banking

system can minimize liquidity risk and can focus

more on managing liquidity risk from unsystematic

factors.

6 CONCLUSION & SUGGESTION

Conclusion

This study aims to analyze liquidity risk of Islamic

banking in Islamic countries and compare it with

non-Islamic countries. The results of this study

found that macroeconomic conditions and monetary

policy not affect liquidity risk of Islamic banking in

Islamic countries, but in non-Islamic countries

monetary policy affect the liquidity risk in Islamic

banking. This is because Islamic banking in non-

Islamic countries still following the economic

system of the country that still applying interest

rates. Surely the results of this study into the new

findings that are useful as information for non-

Islamic countries should adopt the Islamic economic

system that forbids interest to be free from various

systemic problems that can lead to economic crisis.

Suggestion

The results of this study provide evidence that

countries with Islamic economic systems are more

resistant to systematic factor shocks. So the author

gives advice to apply the Islamic economic system

to all countries that practice Islamic banking and

eliminate the interest system.

LIMITATIONS

This research is only limited to systematic factors,

namely macroeconomic variables and monetary

policy on liquidity risk in Islamic banking. For

future research, can expand studied variables such as

non-systematic factors and not only on liquidity risk,

but also can assess other banking risks.

ACKNOWLEDGEMENTS

This article is funded by the Indonesian government

fund management agency (LPDP), ministry of

finance and ministry of research, technology, and

education (BUDI-DN).

REFERENCES

Ahmed, Osman Babikir. 2001. Islamic Financial

Instruments to Manage Short-Term Excess Liquidity,

2nd edition. Islamic Research and Training Institute,

Jeddah: Islamic Development Bank.

Antonio, M.S. (2001). Bank Syariah Dari Teori Dan

Praktek. Gema Insani, Jakarta

Bordeleau, É., Graham, C. (2010), The Impact of

Liquidity on Bank Profitability (No. 2010, 38). Bank

of Canada Working Paper.

Brunnermeier, M.K. and Yogo, M. (2009), “A note on

liquidity risk management”, AEA Session on

Liquidity, Macroeconomics, and Asset Prices, p. 12.

Cetorelli, N. & Goldberg, L.S. (2012). Banking

Globalization and Monetary Transmission, The

Journal of Finance, LXVII (5), 1811-1843.

https://doi.org/10.1111/j.1540-6261.2012.01773.x

Effendi, K.A, Disman & Nugraha (2017). Liquidity Risk

and Macroeconomic Analysis of Islamic Banking in

Liquidity Risk of Islamic Banking in Islamic and Non Islamic Countries

287

Indonesia, International Conference Islamic

Economics, Business and Philanthropy, 1.

Haryono, Y., Mohd, N., & Hamat, M. (2016). Factors

Affecting Credit Risk in Indonesian Islamic Banks.

Journal of Islamic Finance, 5(1), 12–25.

How, J. C. Y., Karim, M. A., & Verhoeven, P. (2005).

Islamic financing and bank risks: The case of

Malaysia. Thunderbird International Business Review,

47(1), 75–94. https://doi.org/10.1002/tie.20041

Iqbal, A. (2012). Liquidity Risk Management: A

Comparative Study between Conventional and Islamic

Banks of Pakistan. Global Journal of Management

and Business Research, 12(5).

Kazarian, E. G., & Koko, A. (1987). Islamic banking and

development. Minor Field Study Series, (no 4), iii, 62

Megeid, N.S.A (2017). Liquidity Risk Management:

Conventional versus Islamic Banking System in Egyp,

Journal of Islamic Accounting and Business Research,

Vol 8, Issue 1.

Ray, N. D. (1999). Arab Islamic Banking and the Renewal

of islamic law. J.KAU: Islamic Econ, 11(0), 63–69.

Rivai, H.V., Veithzal, A.P. & Idroes, F.N. (2007), Bank

and financial institution management. Raja Grafindo

Persada.

Tiby, A.M.E.T. (2010). Islamic Banking: How to Manage

Risk and Improve Profitability. Published by Wiley

Waemustafa, W., & Sukri, S. (2016). International Journal

of Economics and Financial Issues Systematic and

Unsystematic Risk Determinants of Liquidity Risk

Between Islamic and Conventional Banks.

International Journal of Economics and Financial

Issues, 6(4), 1321–1327. https://doi.org/10.15408/

aiq.v8i2.2871

Warde, I. (2000), Islamic Finance in the Global Economy,

Edinburgh University Press, Edinburgh.

Vogel, Frank E. and Sauel L. Hayes (1998), Islamic Law

and Finance: Religion, Risk, and Return, The Hague,

The Netherlands: Kluwer Law International

Zineldin, M (1990). The Economy of Money and Banking:

A Theoretical and Empirical Study of Islamic Interest-

free Banking, Stockholm: Almqvist & Wiksell

International

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