disso final

MSC DISSERTATION

Did the financial crisis demonstrate the relative robustness of Islamic banks over their Conventional counterparts?

DEPARTMENT OF ECONOMICS, UNIVERSITY OF WARWICK

Raza Rehman (ID: 1463577)

Supervisor: Mr Alexander Karalis Isaac

September 2015

Word count: 5934 (excluding appendix, footnotes and references)

Abstract: The consequences of the Great Depression raised concerns regarding the reliability of the Conventional

banking system and coupled with the noteworthy growth of Islamic banking, has sparked interest in this new model.

With the literature varying on the relative rigor of the Islamic banking model, this dissertation aims to provide an

alternative perspective on the debate using a sample of 397 banks from 13 countries over a 23 year period.

Employing the Fixed Effects estimation technique, it is found that Islamic banks do not offer the extra element of

safety implicit in their theoretical model, revealing the possibility of a lack of risk management expertise and the

diversity of Islamic banking practices due to different interpretations of the Islamic guidelines

This dissertation proposal is submitted in partial fulfilment of EC 959 MSc Dissertation for the degree of MSc Economics

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Acknowledgement:

First and foremost, I thank Allah for showering me with His unconditional blessings and giving

me the opportunity to study at a renowned university alongside some of the most talented

academics. Mr Alexander Karalis Isaac has been a great inspiration and his support gave me the

confidence to compile this paper. I would also like to thank my family and friends, who have

always been my pillar of support throughout my each and every endeavour.

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Contents

1. Introduction ............................................................................................................................. 3

2. Banking theory ........................................................................................................................ 5

3. Islamic Banking framework .................................................................................................... 7

4. Literature Review .................................................................................................................... 9

5. Data ........................................................................................................................................ 11

6. Methodology .......................................................................................................................... 15

7. Empirical results .................................................................................................................... 19

8. Discussion .............................................................................................................................. 21

9. Conclusion ............................................................................................................................. 23

Appendix: ...................................................................................................................................... 24

References ..................................................................................................................................... 29

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1. Introduction

The finance-growth nexus has been explored in great detail with varying conclusions to the

direction of causality. Evidence supporting financial development as a factor of economic

growth has been widely documented together with arguments for the reverse and even

bidirectional relationship. Nevertheless, it is universally accepted that stability in the financial

sector is vital to economic growth and this was made evident during the recent financial crisis.

The 2007/08 financial crisis, or Great Recession, was one of the worst to hit the United States

since the thirties. The Bureau of Labor Statistics found that unemployment more than doubled

from 4.7% in December 2007 to a peak of 10% in October 2009, with the real Gross Domestic

Product decreasing by 3.8% in early 2009 (Swann, 2009). An upward trend in house prices,

favourable government policies and optimistic expectations attracted a surge in demand for real

estate from both first-time home buyers and aspiring realtors. Also wanting to capitalise on the

situation, banks loaded their portfolios with mortgages to prime but worryingly sub-prime

borrowers. In 2002, less than 10% of U.S. mortgages were subprime but accounted for almost

25% in 2005 (Goodwin et al., 2013).

Under the Basel accords, banks were required to maintain at least an 8 percent capital buffer

against their assets after adjusting for risk however, the activity in the housing market meant

complying with such requirements was going to be costly. To avoid such losses, banks took part

in Securitization and introduced the capital market into its transactions. A period of unorthodox

lending of mortgages, complex financial instrument engineering and lenient regulation brought

about the rapid expansion of the housing bubble. Once house prices stopped rising and the sub-

prime mortgage borrowers started to default, the value of these AAA-rated financial instruments

(i.e. Collateralized Debt Obligations) completely diminished and panic spurred. The ultimate

bust of the housing bubble led to bank runs and the collapse of giants such as Lehman Brothers

and Freddie Mac, triggering the failure of a series of financial institutions that prompted a global

economic downturn.

The trustworthiness of the central culprit of the crisis - the banking system – has since been

under question. The distinctive principles that shape its model coupled with its unprecedented

growth has sparked interest in Islamic Banking and Finance. Pre-crisis, global Islamic banking

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assets increased by almost 20% per annum till 2013 (Islamic Financial Services Board, 2015)

and according to the World Islamic Banking Competitiveness Report 2013/14 by EY, continued

such patterns following the crisis, growing 16% since 2011. With an increasing Muslim

population, relatively untapped market and repeated episodes of financial market failures1, this

new mode of Islamic Banking and Finance may be of great benefit to the global economy. The

objective of this dissertation is to shed light to the argument of whether the alternative banking

system based on Islamic teachings is the way forward towards a more stable global community.

By analysing the performance of Islamic Banks and their conventional peers across the financial

crisis, the dissertation aims to answer the following question:

“Did the financial crisis demonstrate the relative robustness of Islamic banks over their

Conventional counterparts?”

The paper is organized as follows. Section 2 outlines the theory behind financial

intermediation, with a description of the Islamic banking model and the principles underlying its

framework in Section 3. Section 4 reviews the literature that has inspired the dissertation. Section

5 discusses the data and the methodology adopted is discussed in Section 6. Section 7 follows

with the results of the analysis. A discussion of the results is given in Section 8 and finally,

Section 9 encompasses a conclusion along with potential areas of future research.

1 “…100 crises in the past 35 years” Stiglitz (2003), Dealing with Debt: How to Reform the Global Financial System, page 54.

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2. Banking theory

In an Arrow-Debreu setting with a complete set of state-contingent markets and absence of

information and transaction costs, any equilibrium allocation is a Pareto optimum and financial

intermediation only creates a distortion to the economy. However, such conditions are unrealistic

and agents face costs of acquiring information and conducting transactions, which if

considerably large may discourage economic exchange. The ability to reduce such costs and in

turn, facilitate trade allows scope for financial intermediation.

This notion was introduced formally by Diamond (1984). His theory embodies the difficulty

faced by individual lenders to screen feasible projects together with minimising the costs

associated with monitoring the actions of borrowers once contracts are signed. Furthermore,

Diamond pointed out that individual lending can lead to either the duplication of information

when each lender monitors or no monitoring whatsoever as lenders will wish to free-ride, i.e.

each person presumes the other will monitor which results in an absence of monitoring. As

intermediaries, banks face cost advantages of framing and monitoring loan contracts which allow

for Pareto optimal allocations to be achieved, ceteris paribus.

Despite banks and other financial institutions being similar in their totality, banks hold a

certain feature that distinguishes them from other financial institutions. This unique function of a

bank is referred to as ‘maturity transformation’; that is, the conversion of long-term illiquid

investments into liquid deposits (Diamond and Dybvig, 1983). Returns are generally higher for

projects that offer payoffs later in the future however, many investors shy away from such

projects. Banks conversely, due to the unique feature of being able to offer depositors access to

their savings before the returns from the investments are realised, fill this void and finance such

projects. This allows depositors with different consumption preferences to smooth consumption

by withdrawing funds according to their expenditure plans. Despite demonstrating that this

creation of liquidity can boost output, Diamond & Dybvig (1983) find that this function has also

one weakness which is that it is vulnerable to ‘bank runs’, i.e. collective withdrawal of deposits

by customers in response to expectations of the bank failing. These bank runs force the bank to

liquidate the long-term investments at a loss that can lead to bankruptcy and have adverse

implications on economic activity.

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Market rumours and confidence levels play focal roles in the health of the financial sector

due to their contagious property and are the main cause behind bank runs. Lack of confidence

stemming from speculations associated to a particular bank can spread to other stable banks as

depositors will want to withdraw their savings before a run commences, ultimately resulting in a

bank run. Panics of such nature test banking systems and the dissertation aims to supplement the

literature by applying this notion to the resilience of Islamic and Conventional banks during the

Great Depression. Before doing so, the following section gives a brief description of the Islamic

banking system.

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3. Islamic Banking framework

Islam echoes modern economics in that trade should be encouraged in order to achieve

economic growth and development. Where rationality plays the central role in traditional

economic theory, the Islamic concept encompasses both moral and spiritual dimensions to the

problem on the premise that personal pursuit of welfare maximization does not necessarily

translate to an improvement in the general welfare of the society (Iqbal and Mirakhor, 2013).

Contradictory to popular belief, Adam Smith also held views mirroring those extrapolated by

Islam in his work Theory of Moral Sentiment (1979), in which Smith voices his belief of a

Supreme Power and emphasises that following the guidelines revealed by the Creator will

internalise distortions in a country and produce fair outcomes.

Despite the role of both Islamic banks and Conventional banks being parallel and the

prominent theories of banking applying to both, their underlying models are distinct. The

adherence to Shari’ah, i.e. Islamic law, distinguishes Islamic banks from their Conventional

peers. The Islamic banking model, although introduced a few decades ago, is characterised by

principles revealed during the infancy of Islam, which are defined under fiqh mualamat (rules on

transactions).

The overarching of these is the prohibition of the receipt and payment of Riba, i.e. interest.

Money serves only as a store of value and medium of exchange in Islam and treating it as a

commodity to make money is regarded as a major sin. The condemnation of interest can be

attributed to the following passage of the Quranic scripture;

“O you who have believed, do not consume usury, doubled and/or multiplied, but fear

Allah that you may be successful.”2

Trade plays an integral part of the Islamic economic system and compensation for investment

is valued however, the risk of a venture must be divided amongst the parties involved. In other

words, risk should be shared rather than transferred. Instead of interest, the returns of an

investment stem from profit and loss sharing, which encourages prudent lending, efficient

2 The Holy Quran, Chapter 3 (Al-Imran), Verses 130-131.

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entrepreneurship and more importantly, an equitable distribution of funds. The following verses

from the Quran express this;

“…Allah has permitted trade and has forbidden interest.”3

“O you who have believed. Squander not your wealth among yourselves in vanity, except it

be a [lawful] trade by mutual consent, and kill not one another. Indeed, Allah is ever Merciful

unto you.”4

Despite the promotion of profitable trade, illicit activities and forbidden sectors such as

pork, alcohol, drugs etc. cannot be extended finance irrespective of potentially high returns.

Excessive uncertainty, or Gharar, via excessive risk-taking and speculation nullify a contract as

any monetary gains obtained are through sheer luck and not effort. Lastly, financial activity must

have an association with the real sector activity such that lending can be linked to the underlying

asset.

To incorporate the principles mentioned above, two modes of financing have been structured;

these are the Mudarabah (passive partnerships) and Musharakah (active partnerships)

arrangements. The Mudarabah contract involves a capital provider (bank) and an investment

manager (borrower) in which the former party remains isolated from the venture. The returns to

each party are a proportion of the profits, if any, according to a pre-agreed ratio but the financial

losses are borne solely by the capital provider. The losses faced by the investment manager are

the time and effort expended. The Musharakah contract differs to the Mudarabah in that the

bank may not be the exclusive investor and can, but is not required to, participate in the

management decisions. Again the profits are shared in accordance to a ratio agreed at the time of

the contract and losses equate to the respective capital investments. Where profit sharing does

not apply, other modes of financing have been designed and a description of these is given in

Table 6 under the Appendix.

3 The Holy Quran, Chapter 2 (Al-Bakarah), Verse 275. 4 The Holy Quran, Chapter 4 (An-Nisa), Verse 29.

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4. Literature Review

Established in 1975, Dubai Islamic Bank became the first bank to offer Shari’ah compliant

products and services. Since then, the growth in the size and number of Islamic banks and

financial institutions has been unmatched, reaching major economies in the West5 and some

large Conventional banks opening Islamic windows6. Given its recent beginning, literature on

Islamic banking performance is relatively scarce. The majority of the work is theoretical and the

empirical papers focus on country-specific analysis before the U.S. housing bubble even started

to materialise. Nevertheless, contributions examining cross-country comparisons with its

Conventional counterparts across the financial crisis have been documented.

Hasan and Dridi (2010) evaluate Islamic and Conventional banks from eight countries under

four criteria; profitability, asset growth, credit growth and external ratings. Using non-parametric

analysis, they found that Islamic banks outperformed their counterparts in terms of the latter

three measures and matched them in terms of profitability, attributing the results to the adherence

of Shari’ah principles. However, Islamic banks were found to be adversely affected by the

second-round effects due to lack of credit diversification. Furthermore, they found that larger

Islamic banks were more profitable due to economies of scale and strong reputation; contrary to

the results found by Cihak and Hesse (2010).

Distinguishing by size, Cihak and Hesse (2010) use the Z-score on a sample ranging from

1993-2004 to conclude that Islamic banks fared better than non-Islamic banks when small but

become financially weaker as they grow, reflecting the difficulty of scaling credit risk

management systems. The differing results to Hasan and Dridi (2010) could be due to different

samples and different definition of large banks.

Beck et al. (2013) compare business orientation, efficiency, asset quality and stability in 22

countries and found that Islamic banks were less-cost effective but had higher intermediation

ratios and were better capitalized, which supported Islamic banks across the crisis. The results

obtained by Bourkhis and Nabi (2013) contrast the common finding of relative serenity of

Islamic bank performance. Also using the Z-score, they find that there was no significant

5 E.g. United Kingdom, United Sstates, Germany, etc. 6 Department in a Conventional bank that provides Islamic banking products and services.

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difference in the effect of the crisis on the soundness of both types of banks, implying a

diversion of Islamic banks from their theoretical models.

In theory, the principles underlying the Islamic banking system should act as a defence

against such financial crises and even mitigate the likelihood of such occurrences. In fact, those

that found Islamic banks to have performed better than their Conventional counterparts have

suggested that the Islamic principles have been the reason for this. That is, the sharing of risk,

extension of credit based on moral criteria and the constraint on uncertainty should armour

Islamic banks from such episodes. However, the inconsistent results have motivated my decision

to explore the argument and evaluate whether Islamic banking can be beneficial to financial, and

ultimately, economic stability. By revising the approach used in existing work, the dissertation

will hope to complement the literature and provide an alternative perspective on the Islamic vs

Non-Islamic banking debate and determine whether or not the Islamic banking framework could

prevent such episodes in the future.

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5. Data

An initial panel of 2489 banks across 32 countries was constructed however, the balance

between Islamic and Conventional banks was considerably disproportionate. To address this,

countries homing either very small and/or very few Islamic banks in relation to Conventional

banks were dropped. Specifically, countries with an Islamic bank presence in terms of total

assets of less than $5bn were excluded. Furthermore, conflict prone countries including

Palestine, Iraq, etc. where also removed from the sample in order for the financial crisis to be the

central focus. The sample was further trimmed by removing countries with a sum of total Islamic

banking assets of less than $950m along with those countries in which total assets of Islamic

banks were much larger or much smaller than the other countries. Finally, banks with data not

extending till the end of the crisis were dropped, resulting in a final dataset of 397 banks of

similar size from 13 countries across 23 years (1993-2015 inclusive). Banks with branches in

other countries were considered as separate entities. Table 1 presents the number of both types of

banks in the final sample.

Table 1: Number of banks in sample

Country Islamic Conventional Total banks

Bahrain 19 12 31

Bangladesh 7 38 45

Egypt 3 23 26

Indonesia 10 62 72

Jordan 3 11 14

Kuwait 11 5 16

Malaysia 18 33 51

Pakistan 9 22 31

Qatar 6 6 12

Saudi Arabia 5 8 13

Sudan 16 9 25

Turkey 4 30 34

United Arab Emirates 9 18 27

Total 120 277 397

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The variables used can be divided into two categories; bank-specific and macroeconomic

indicators. The first set of variables were obtained from the Bankscope database, which compiles

information financial institutions across the globe. For the macroeconomic variables, the World

Bank database was relied upon. Table 2 provides a brief description of the variables used in the

study and their relation to the dependent variable, i.e. the log transformation of the Z-score

(discussed in Section 6).

Table 2: Description of provisional variables

Variables

(Regressors) Description

Expected

effect Explanation

Equity ratio

+

Capital stock acts as buffer for banks to repay

depositors when facing losses and allows for

diversification and higher income.

Loan Loss Reserve

-

Non-performing loans cause a reduction in equity

while reducing realizable net income.

Cost ratio

-

Higher ratio implies less efficient management of

costs and so, lowers profits and less stability.

Earnings Return on (Average) Equity + Higher profits strengthen asset side of balance

sheet and are a further cushion against bad loans.

Liquidity Ratio of Net Loans to Total Assets Ambiguous Higher ratio implies higher income but increased

exposure to default risk.

Size Logarithm of Total Assets +

Size of the bank is expected to have a non-linear

effect. Positive relation due to Economies of Scale

but only till a certain threshold. Square of logarithm of Total Assets

-

Inflation Annual percentage change in

Consumer Price Index Ambiguous

Inflation will lead to higher profits and higher

costs so effect depends on the dominating effect.

Growth Real GDP growth rate + Economic upturns are associated with higher

profits and lower non-performing loans.

Real interest rate Interest rate adjusted for inflation Ambiguous Effect depends on dominating channel, i.e.

lending rate or deposit rate.

IB Market share Market share of Islamic banks +

A higher share of Islamic banks, in terms of Total

Assets should increase the stability in the financial

sector.

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The raison d'être for the explanatory variables holds roots to the commonly applied

CAMEL model which abbreviates to Capital, Asset quality, Management, Earnings and

Liquidity. These five elements are reviewed by credit rating agencies and regulatory bodies in

order to evaluate the performance of a bank and have formed the basis for the Financial

Soundness Indicators of the International Monetary Fund. The dimensions of the model are

applied as determinants of a banks Z-score.

Capital ratios attempt to quantify a bank’s financial position and assesses the ability of a

firm to withstand loss or liquidation. Maintaining sufficient capital stock allows banks to protect

themselves from unexpected losses and the Equity ratio is used to account for this. Asset, or

Loan, quality refers to the credit risk associated with a particular asset, i.e. any balance sheet

item that generates an income. A portfolio of outstanding but not received loan income is an

indication of poor asset quality and Loan Loss Reserve to Total Assets is used to represents this.

Due to its qualitative connotation, there has not been a consistent or straightforward method of

representing Management in empirical work. In attempt to capture the quality of management

decisions, the Cost to Income ratio is used as poor management will lead to unnecessarily high

costs. Earnings establish the capacity of a bank to fund its investment decisions and provide an

additional buffer for bad debts and are represented by Return on Equity. Liquidity ratios attempt

to measure the ability of a firm to pay off its short-term debts. The crisis demonstrated the

importance for banks of maintaining current assets to fulfil their immediate commitments and

Net Loans to Total Assets is used as its measure.

To account for the cost advantages from an increase in operational capacity, Size is also

included in the list of regressors and is represented by the logarithm (henceforth, log) of Total

Assets. The square of the log of Total Assets is used to reflect that these Economies of Scale tend

to reverse after a certain level. The state of the economy impacts the environment of the financial

market and the macroeconomic variables reflect the key economic indicators. These include the

growth rate of Real Gross Domestic Product, Inflation rate and Real interest rate. Table 3.1

below displays the descriptive statistics for the bank-specific variables employed for both the

Islamic banks and Conventional banks used in the sample, with Table 3.2 reporting the overall

summary statistics.

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Table 3.1: Summary Statistics for Islamic and Conventional samples

Variables

Islamic banks Conventional banks

Mean Std. dev Mean Std. dev

Z-score 3.019 8.598 3.565 8.475

Log Z-score 0.486 1.104 0.734 1.087

Equity ratio 9.374 20.142 6.057 10.372

Loan Loss Reserve 1.827 7.276 2.798 7.661

Cost ratio 22.217 51.758 21.999 37.109

Earnings 3.438 11.202 5.158 23.099

Liquidity 15.248 26.105 20.367 27.188

Ln (Assets) 4.853 6.739 5.999 7.036

Market share 0.008 0.039 0.016 0.051 Note: The mean and standard deviations of the internal characteristics of each type of bank is reported in this table. On average, both types of

banks have a similar Z-score in both log and levels, with Conventional banks having a relative better score. The mean of the Size variable shows

that Conventional banks are much larger than Islamic banks, which is further implied by the average Market share. Islamic banks have higher costs, are less liquid and hold less capital stock than their Conventional peers.

Table 3.2: Summary statistics

Variable Observations Mean Std. Dev. Min Max

Log Z-score 9131 0.659 1.098 -4.653 5.699

Equity ratio 9131 7.059 14.142 -97.27 100.00

Loan Loss Reserve 9131 2.505 7.561 0.00 100.00

Cost Ratio 9131 22.065 42.075 0.00 950.00

Earnings 9131 4.638 20.269 -650.26 741.32

Liquidity 9131 18.820 26.966 0.00 109.14

Ln (Assets) 9131 5.653 7.158 0.00 19.01

(Ln (Assets))2 9131 83.193 109.318 0.00 361.32

Growth 9131 3.237 3.684 -13.127 30.012

Inflation 9131 4.8203 8.9959 -4.8633 132.824

Real Interest rate 9131 1.6301 4.8529 -24.6002 41.254

Market share 9131 0.0138 0.0479 0.00 0.9212 Note: This table reports the first and second moments, the minimum and maximum values for the bank-specific, macroeconomic and market share variables. Data sources and definitions of the variables are mentioned in the main body of the dissertation.

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6. Methodology

The European Central Bank defines financial stability as “a condition in which the financial

system – intermediaries, markets and market infrastructures – can withstand shocks without

major disruption in financial intermediation and in the effective allocation of savings to

productive investment.” One such shock can be a bank run. Bank runs have the characteristic of

being contagious in that, speculations regarding the health of a particular bank or financial

institution can readily hinder the stability of another. Such was the case across the financial

crisis, whereby news regarding entities such as Lehman Brothers, Freddie Mac and Northern

Rock mushroomed not only across the United States but also abroad. The dissertation will use

this notion in an attempt to achieve its objective and captures this by using the Z-score.

The Z-score has become a popular measure of bank soundness. Traditionally, the statistical

Z-score measures the distance of an observation from the mean in terms of standard deviation,

allowing for a meaningful comparison of different samples of data. Its application has recently

been extended to finance literature, where the Z-score is interpreted as the number of standard

deviations below the mean the Return on Assets has to fall before equity is depleted. A financial

institution is classed as insolvent if its losses exceed the value of equity. The Z-score is

calculated as follows,

where, ROA – Return on (average) Assets

CAR – Capital Asset Ratio

sd (ROA) – Standard deviation of ROA (proxy for return volatility)

Its popularity has stemmed from its simplicity in terms of its calculation, where accounting

metrics are used rather than market data, and in terms of its relation with the probability of

insolvency; a higher Z-score implies lower probability of bank insolvency and in turn, a more

stable bank, vice-versa. Being composed of accounting data expands its application to various

groups of financial institutions including unlisted financial institutions but more importantly

Islamic banks, allowing for an objective analysis of the question at hand.

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However, Lepetit and Strobel (2015) amongst others extrapolate that the simple Z-score,

amidst its advantages, has an important flaw especially in regards to drawing inferences; it

suffers from a skewed distribution. The relationship between insolvency risk and the Z-score is

only valid based on the assumption that profits, or Return on Assets, are normally distributed.

This may not be the case and as a result, the Z-score has a skewed distribution. To address this,

the dissertation will modify the econometric model used in the literature by applying a

logarithmic transformation to the Z-score based on the practical work of Laeven and Levine

(2009) and findings of Lepetit and Strobel (2015). As robustness checks, the identical model will

be regressed but using the simple Z-score.

The framework advanced by Cihak and Hesse (2010) and Bourkhis and Nabi (2013) is

followed in which bank stability is modelled controlling for bank-specific and macroeconomic

characteristics. But instead of the simple Z-score, the logarithmic transformation is employed in

order to overcome any distributional issues attached to the measure. The variables of interest are

the interaction terms between the Islamic bank dummy and the Crisis period dummy (here, 2007-

2009) along with the market share of Islamic banks, which are both expected to have positive

coefficients.

Given the panel structure of the data, Pooled Ordinary Least Squares (POLS), Fixed Effect

and Random Effect estimation techniques will be considered. Past values of financial metrics

have been found to impact the current position of a bank (Athanasoglou et al., 2008) and to

account for this variation in the dependent variable, the bank-specific and macroeconomic

variables are lagged one period giving the following log-level model,

where, – Z-score for bank in country at time

- vector of bank-specific variables

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- vector of macroeconomic variables

- Islamic bank dummy (1 if Islamic, 0 otherwise)

- Crisis dummy (1 if 2007-2009, 0 otherwise)

– Market share of bank at time

- vector of year dummies

- vector of country dummies

The POLS estimation technique minimizes the squared residuals of the model in order to

provide the line of best fit and is adopted as a baseline regression in this analysis. However, there

are two key assumption underlying the technique which must be considered when interpreting

the results obtained. These are that the independent variables are uncorrelated with the

disturbance term, i.e. Е ( ' ) = 0, and the observations are homogenous, i.e. = . Given the

fact that different banks specialise in different business areas, concentrating on those that

generate higher profits given the needs of the customers and cost of raising finance, the

assumption of homogeneity is not a sensible one. An attempt to capture these differing

characteristics is made however, not all the individual effects are measurable and this

heterogeneity provides the foundation for exploiting other techniques.

Least Square Dummy Variable (LSDV), Fixed Effects and Random Effects are the most

common approaches when dealing with panel data. The LSDV approach is most effective when

the number of observations are not considerably larger than the period under consideration but

with the dataset comprising of 397 banks across 23 years, the technique is disregarded. Both the

Fixed Effect and Random Effect techniques acknowledge the heterogeneity amongst

observations but differ in its treatment; the former introduces the diversity through dummy

variables for each observation whereas the latter includes it in the innovation term. That is, Fixed

Effects assumes the heterogeneity is correlated with other regressors whereas Random Effects

assumes heterogeneity is uncorrelated with other independent variables. Given both methods are

applicable to most work using panel data, the Hausman test is conducted in order to determine

the most appropriate. The results are given in Table 4.1 and infer that the individual effects do

not meet the exogeneity requirement needed for the Random Effects, so the Fixed Effects

estimation should be adopted. The Fixed Effects technique removes the heterogeneity by

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demeaning the data, i.e. deducts the mean from each observation. Given the time-invariant nature

of the country dummies, they are automatically dropped in the regressions.

Table 4.1: Hausman test results

Hypothesis Prob>chi2 FE or RE?

: [ ′ ]=0 or no correlation 0.0000 FE

To be able to draw correct inferences from the results of the regressions, it is fundamental

that the variance of the errors is time-invariant. In order to test whether the errors are

heteroskedastic, the Bruesh-Pagan test is run. The results, given in Table 4.2, show the variance

suffers from heteroskedasticity. The presence of this invalidates the inferences drawn from the

hypothesis tests however, by using the command robust in STATA, this can be corrected for.

Table 4.2: Breusch-Pagan test for heteroskedasticity

Hypothesis Prob>chi2 Heteroscedasticity?

: 0.0000 Yes

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7. Empirical results

Four specifications of the model are considered; the first containing only bank-specific

characteristics, the second including macroeconomic variables and the third introducing the

variables of interest, i.e. the Islamic bank-Crisis interaction term and Islamic bank market share.

In order to dwell deeper into the issue at hand, the interaction term is divided amongst the three

central years of the financial crisis, giving the fourth specification of the model. The results from

the Fixed Effects estimation are displayed in Table 5.2 and those drawn from the final two

specifications are analysed in the following.

From the penultimate model, the coefficient on Cost to Income ratio is expected to have a

negative sign as higher costs relative to the bank earnings puts the bank, Islamic or

Conventional, in a financial predicament and in turn, reduces its robustness. The results for the

variable also suggest such a relation however, are statistically insignificant at the 10%

significance level. Increments to the Loan Loss Reserves, as expected, reduce the log Z-score but

the opposite is indicated for Net Loans to Total Assets. That is, a more liquid position is

beneficial to the stability of a bank as the revenues generated exceed the default risk from the

increase in lending. The coefficients on both variables are insignificant. A higher capital stock

improves the log Z-score of a bank and similarly, so does Earnings, both being statistically

significant. The results on the Size variables demonstrate the existence of Economies of Scale,

i.e. larger banks have higher profits and are more stable but only till a certain threshold. The sign

of the coefficients on log of Total Assets and the Square of the log of Total Assets are positive

and negative respectively, but only the coefficient on the former is statistically significant.

A more prosperous economy is commonly viewed as an important factor for the prosperity of

the financial market and using Real GDP Growth and Inflation as measures of the outlook of a

country, we find this notion holds. Real GDP Growth and the log Z-score have a positive and

significant relation according to the data and conversely, Inflation has an inverse but statistically

insignificant relation. The Real Interest rate impacts the log Z-score via two channels; the

lending rate and the deposit rate. Some countries in the sample7 follow a pegged system and so

interest rates in those countries will follow the rates set by the Federal Reserve. Nevertheless, the

7 Saudi Arabia, UAE, Bahrain and Qatar

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results suggest the second channel is dominant such that increases in the real interest rate lowers

the stability of a bank, with the coefficient being statistically significant at the 10% level.

Despite the theoretical vigour of the Islamic banking model, the results obtained from the

third specification of the model fail to support this structural advantage across the financial

crisis. The interaction between the Islamic bank dummy and Crisis dummy has a negative

coefficient, implying that Islamic banks were also prone to the effects of the crisis but the result

is statistically insignificant.

Separating the interaction term to account for each of the three years of the crisis in the final

specification, the results suggest the opposite for the variables of interest. Islamic banks are

found to have been adversely impacted in the run up to the crisis but withstood the shocks when

the crisis was in full effect, with the results being statistically significant for 2007 but

insignificant for 2008 and 2009. Considering the implications of the market shares of Islamic

banks on the stability of individual banks, the results suggest that a financial sector with a higher

proportion of Islamic banks is less likely to create vulnerability in the performance of individual

banks. The results however, are insignificant even at the 10% level. In respect to the bank-

specific and macroeconomic variables, the same results and inferences are obtained from the

final specification.

In order to test the robustness of the results, a contemporaneous version of all specifications

of the model are analysed and the results are tabulated in Table 5.3. Other than the coefficient on

Cost-to-Income variable becoming positive but still insignificant, the remaining results hold.

Furthermore, the model is estimated using the simple Z-score, the results of which are reported

in Table 5.4. The R-squared of the model is considerably less than the model considered and

contrary to the results of the final specification, using the simple Z-score generates a positive and

insignificant coefficient on the Islamic bank-Crisis interaction dummy. When distinguishing

between each of the years of the crisis, the results mirror those produced using the log

transformation of the Z-score.

21

8. Discussion

According to the final specification of the model, the results obtained for the bank-specific

and macroeconomic variables are in accordance to the expectations in place and it may be argued

that Islamic banks endured the shockwaves originated in the Western economies.

The hypothesis of whether being an Islamic bank during the financial crisis enhanced the

stability of a bank or not is tested along with whether a larger share of Islamic banks in the

financial market improves stability. The statistically significant negative coefficient on the 2007

Islamic bank interaction term indicates an initial impact of the crisis on the sample group.

However, the insignificant results on the 2008 and 2009 Islamic bank interaction terms compels

one to draw the inference that the stability of Islamic banks was not affected by the financial

crisis.

Being the first such episode encountered by Islamic banks, the lack of experience coupled

with the complexity of the new products could be attributed to the negative coefficient on the

2007 Islamic bank interaction term. The uniqueness of the Islamic banking model opens it up to

risks other than those faced by its Conventional peers, which in a mixed industry, have been a

challenge to identify let alone manage. This inexperience has hindered Islamic banks in

implementing suitable risk management strategies and many have applied either conventional

risk management techniques or variations of these strategies in attempt to manage these unique

risks (Salem, 2013), providing the basis for such results.

A larger presence of Islamic banks in the financial market is expected to create a more

tranquil financial environment but the statistically insignificant results indicate that a financial

market concentrated with Islamic banks does not improve, nor worsen, stability. This could be

due to an absence of relevant and effective regulation or a divergence of Islamic bank practises

from its model.

Chapra and Khan (2000) note that for a continuation of the expansion of the Islamic banking

sector, reforms to the regulatory and supervisory framework are key along with entities

supporting the functions of the bank; in particular, private credit rating agencies. These agencies

will facilitate the management of risks in the more risky modes of financing, i.e. Mudarabah and

Musharaka, improving profitability and curtailing moral hazard. Since their report, multiple

22

rating agencies in Muslim countries have commenced operations8 which could be the root to the

rapid recovery noted in the World Islamic Banking Competitiveness Report 2013/14 by EY.

The debate concerning the harmony between the theoretical model and the practises of

Islamic banks has been an on-going one. Many have critiqued the Islamic banking model and

argued that apart from a change in terminology, they are not any different to their Conventional

counterparts (e.g. Kuran, 1993, 2004) whereas supporters of the model contend that such a

scenario was only possible in the transition away from the conventional system (Ahmed, 1993).

With the verses regarding trade revealed over 4000 years ago, applying them to the current

system has not been a simple task and this has been the main reason for inconsistencies in the

practises among Islamic banks. The rulings on Riba (interest) and gharar (uncertainty) have been

subject to varying interpretations, with some viewing the relevant verses as a prohibition of

interest in its entirety whereas for others they signify a restriction from “excessive” interest. Due

to this, the results may not have reflected the expectations of the variables of interest.

It has been argued that a majority of the Islamic banking contracts were collateralised by real

estate in accordance to the materiality principle and this exposed Islamic banks to the “second-

round effects” of the crisis (Hasan and Dridi, 2010). This concentration of credit exposed Islamic

banks to the economic downturn in the real economy. Issues with data availability, particularly

with Islamic banking income, restricted the calculation of an Income Diversification variable

which would have tested whether a more diversified portfolio improves the stability of a bank or

not.

Even with the efforts to control for any shortcomings, the results must be reviewed with

caution as the study has its limitations. The major weakness, as is the case with the majority of

empirical work, is in regards to the quality and quantity of the data. Despite being a reputable

and frequently sourced database, the data compiled by Bankscope is drawn from the financial

statements published by banks (and other financial institutions) which may suffer from reporting

bias. With differences in auditing and reporting regulations across countries, the accounts may

not reflect the actual stance of the bank. In terms of the quantity of data, Bankscope offers the

general user data for only the most recent 16 years available which varies for every bank. Data

for some banks range from 2015 back but for others start from 2007, resulting in discrepancies in

8 Mainly International Islamic Rating Agency (IIRA).

23

the results. This mis-match in available data was addressed by expanding the timeframe from 16

to 23 years however, this resulted in additional missing values, augmenting the potential omitted

variable bias.

9. Conclusion

This dissertation analyses the premise that Islamic banks play a key role in the stability of the

financial sector. In particular, the extra element of safety drawn from the principles underlying

the Islamic banking model are questioned using the Great Depression as the period of distress.

Additionally, whether or not a larger presence of Islamic banks in the financial market translates

to a more stable environment is also examined.

Employing the Fixed Effects estimation technique, the results suggest that the benefits of the

Islamic banking model are not present in Islamic financial institutions. According to the results,

there was no definite improvement in the log Z-score of Islamic banks across the crisis nor was

there a positive influence from a more concentrated Islamic financial market. Contrary to the

general view, the dissertation concludes that the Islamic banking model is not any more robust

than the Conventional model and attributes this to the deviation of Islamic financial institutions

from their theoretical model.

Given the various interpretations of the word Riba, further study in the area could focus on

classifying groups of Islamic banks that specify their practises according to a particular

definition in order to determine the robustness of the Islamic banking model. Alternatively,

attempts at conducting a meta-analysis between a country with a fully Islamic financial system

and one without an Islamic Banking and Finance presence may also provide a useful perspective

to the argument. All things considered, the transition away from a financial system with a deep-

rooted history may prove to be too large of a step for the international community nevertheless,

the growth of Islamic Banking and Finance cannot be overlooked and embracing some of its key

features may prove to be pivotal for the state of the global financial system.

24

Appendix:

Note: This table presents the coefficient and t-statistics for the fourth specification of the model using POLS, Fixed Effect and Random Effects techniques. A significance level of <1% is

denoted by 3 stars, 1-5% by 2 stars and 5-10% by 1 star. POLS assumes homogeneity and so the results must not be taken at face value. The fixed effects and random effects addresses

the heterogeneity amongst the banks in their respective treatment methods.

Table 5.1: Regressions Results

(Dependent variable: Log Z-score) Pooled OLS Fixed effect Random effect

Variable Coefficient t Coefficient t Coefficient Z

Equity (-1) 0.0076*** 6.09 0.0081*** 4.39 0.0079*** 4.00

LLR (-1) -0.0017 -0.74 -0.0026 -0.89 -0.0023 -0.78

Cost-Income Ratio (-1) -0.0009* -1.91 -0.0004 -1.02 -0.001 -1.20

Earnings (-1) 0.0009 1.46 0.001* 1.72 0.001* 1.71

Liquidity (-1) 0.0014 1.11 0.0004 0.20 0.0007 0.33

Ln (Assets) (-1) 0.0997*** 6.47 0.1053*** 4.27 0.1022*** 4.22

(Ln (Assets))2 (-1) -0.0008 -0.99 -0.0016 -1.07 -0.0013 -0.88

Growth (-1) 0.0172*** 4.43 0.0178*** 4.27 0.0176*** 4.81

Inflation (-1) -0.0049 -0.83 -0.0004 -0.32 -0.0005 -0.41

Real Interest rate (-1) -0.0049** -2.37 -0.0041* -1.89 -0.0043** -2.00

Islamic bank Crisis1 -0.3157** -2.69 -0.2137* -1.94 -0.2466** -2.22

Islamic bank Crisis2 -0.0017 -0.01 0.0916 0.82 0.0617 0.55

Islamic bank Crisis3 -0.1017 -0.88 -0.0094 -0.09 -0.0389 -0.37

Islamic bank share -1.4361** -2.22 0.322 0.87 -0.2626 -0.60

Constant 0.1179 1.80 0.1625 2.62 0.2027 2.33

Observations 8733 8733 8733

R-squared 0.5477 0.5172 0.6973

F 343.74 103.48 6403.12

Prob > F 0.0000 0.0000 0.0000

25

Note: This table presents the results from the Fixed Effects estimation on the four specifications of the model. Given the log-level structure of the model, the coefficients are interpreted as

the percentage change in the dependent variable from a unit change in the regressor, after multiplying the coefficient by 100. Column 1 reports the results from the specification comprising

bank-specific variables only, Column 2 includes macroeconomic variables, Column 3 introduces the Islamic bank-Crisis interaction term along with the Islamic bank market share and

finally, Column 4 divides the Islamic bank-Crisis term across the three years of the crisis. The significance levels are * 0.05 < p-value ≤ 0.10; **: for 0.01 < p-value ≤ 0.05; ***: for p-

value ≤ 0.01.

Table 5.2: Fixed-effect Results

(Dependent variable: Log Z-score) Fixed effect [1] Fixed effect [2] Fixed effect [3] Fixed effect [4]

Variable Coefficient t Coefficient t Coefficient t Coefficient t

Equity (-1) 0.0084*** 4.54 0.0081*** 4.36 0.0081*** 4.41 0.0081*** 4.39

LLR (-1) -0.0025 -0.86 -0.0025 -0.87 -0.0026 -0.88 -0.0026 -0.89

Cost-Income Ratio (-1) -0.0005 -1.02 -0.0005 -1.00 -0.0005 -1.01 -0.0005 -1.02

Earnings (-1) 0.001* 1.84 0.0009* 1.71 0.001* 1.72 0.001* 1.72

Liquidity (-1) 0.0007 0.32 0.0004 0.19 0.0004 0.19 0.0004 0.20

Ln (Assets) (-1) 0.1015*** 4.10 0.1047*** 4.25 0.1055*** 4.28 0.1053*** 4.27

(Ln (Assets))2 (-1) -0.0014 -0.92 -0.0016 -1.04 -0.0016 -1.08 -0.0016 -1.07

Growth (-1) - - 0.0176*** 4.22 0.0177*** 4.25 0.0178*** 4.27

Inflation (-1) - - -0.0003 -0.29 -0.0003 -0.29 -0.0004 -0.32

Real Interest rate (-1) - - -0.0003 -0.29 -0.0041* -1.92 -0.0041* -1.89

Islamic bank Crisis - - - - -0.0439 -0.51 - -

Islamic bank share - - - - 0.334 0.91 0.322 0.87

Islamic bank Crisis1 - - - - - - -0.2137* -1.94

Islamic bank Crisis2 - - - - - - 0.0916 0.82

Islamic bank Crisis3 - - - - - - -0.0094 -0.09

Constant 0.4896 8.13 0.1612 2.60 0.1628 2.62 0.1625 2.62

Observations 8733 8733 8733 8733

R-squared 0.5176 0.5189 0.5167 0.5172

F 113.29 112.82 109.56 103.48

Prob > F 0.0000 0.0000 0.0000 0.0000

26

Note: This table presents the results of the same regression but using the current time period of the independent variables. The sign of the coefficients on the independent variables mirrors those

in the lagged model, apart from that on the Cost to Income ratio. Again, * for 0.05 < p-value ≤ 0.10; ** for 0.01 < p-value ≤ 0.05; *** for p-value ≤ 0.01.

Table 5.3: Robustness Checks – Contemporaneous model

(Dependent variable: Log Z-score) Fixed effect [1] Fixed effect [2] Fixed effect [3] Fixed effect [4]


Equity 0.0039** 2.90 0.0037** 2.73 0.0037** 2.81 0.0037** 2.82

LLR -0.0104** -2.80 -0.011** -2.85 -0.0107** -2.90 -0.0107** -2.89

Cost-Income Ratio 0.00002 0.04 0.00003 0.05 0.00002 0.03 0.00002 0.03

Earnings 0.00001 0.04 -0.0001 -0.18 -0.0001 -0.32 -0.0001 -0.34

Liquidity -0.0018 -0.73 -0.002 -0.82 -0.0021 -0.85 -0.0021 -0.84

Ln (Assets) 0.155*** 5.36 0.157*** 5.47 0.157*** 5.46 0.157*** 5.46

(Ln (Assets))2 -0.0027 -1.64 -0.0028* -1.73 -0.0037* -1.67 -0.0027* -1.67

Growth - - 0.012** 2.84 0.012** 2.85 0.0121** 2.90

Inflation - - -0.0017 -1.41 -0.0017 -1.39 -0.0017 -1.38

Real Interest rate - - -0.0022 -0.94 -0.0021 -0.93 -0.0019 -0.88

Islamic bank Crisis - - - - -0.0571 -0.67 - -

Islamic bank share - - - - -1.1073 -1.47 -1.115 -1.47

Islamic bank Crisis1 - - - - - - -0.1949* -1.82


Islamic bank Crisis3 - - - - - - -0.0815 -0.75

Constant -0.0003 -0.02 -0.0003 -0.02 -0.0003 -0.02 -0.0003 -0.02

Observations 9130 9130 9130 9130

R-squared 0.5385 0.5419 0.5537 0.5539

F 148.03 138.09 131.64 125.52

Prob > F 0.0000 0.0000 0.0000 0.0000

Table 5.4: Robustness Checks

(Dependent variable: Simple Z-score)

27

Note: This table presents the results of the same regression but using the simple Z-score as the dependent variable. A considerable difference in the R-squared can be observed. Again, * for 0.05 < p-value ≤

0.10; ** for 0.01 < p-value ≤ 0.05; *** for p-value ≤ 0.01.

Fixed effect [1] Fixed effect [2] Fixed effect [3] Fixed effect [4]


Equity (-1) 0.113*** 4.98 0.111*** 4.83 0.109*** 4.82 0.109*** 4.82

LLR (-1) 0.0065 0.16 0.0057 0.14 0.0072 0.18 0.0071 0.17

Cost-Income Ratio (-1) -0.0057 -1.17 -0.0057 -1.16 -0.0054 -1.11 -0.0054 -1.11

Earnings (-1) 0.0018 0.60 0.001 0.34 0.0012 0.39 0.0012 0.39

Liquidity (-1) -0.0209 -0.85 -0.0231 -0.93 -0.0228 -0.91 -0.0226 -0.90

Ln (Assets) (-1) 1.358*** 5.03 1.385*** 5.13 1.378*** 5.11 1.376*** 5.11

(Ln (Assets))2 (-1) -0.064*** -3.71 -0.066*** -3.80 -0.066*** -3.80 -0.066*** -3.80

Growth (-1) - - 0.125*** 3.93 0.121*** 3.76 0.121*** 3.79

Inflation (-1) - - -0.0099 -1.29 -0.0098 -1.26 -0.0099 -1.29

Real Interest rate (-1) - - -0.0285 -1.63 -0.0278 -1.58 -0.0274 -1.55

Islamic bank Crisis - - - - 1.2089 1.46 - -

Islamic bank share - - - - 4.426** 2.06 4.334** 2.06




Constant 2.6173 4.98 1.7808 1.80 1.7781 1.79 1.7763 1.79

Observations 8733 8733 8733 8733

R-squared 0.2764 0.2790 0.2694 0.2698

F 50.99 49.76 46.85 44.71

Prob > F 0.0000 0.0000 0.0000 0.0000

28

Table 6: Islamic banking products and services

Term Meaning Explanation

Amana Safekeeping Money deposited for safekeeping which earn no return

Bay’ al-salam Advance cash purchase Buyer makes a payment in advance but the delivery of

the good is made at a later date

Bay’ bithaman ajil Deferred payment sale Sale contract in which buyer makes a payment, either

lump-sum or instalments, after the sale together with a

mark-up

Ijara Leasing One party purchases an asset and another is given the

right to use it but the lessor retains ownership

Istina Deferred payment and delivery Sale contract in which a manufacturer/contractor

agrees to produce/build a certain agreed

product/building at a given price in the future, where

the price can be paid at a later date and in instalments,

depending on preferences of the parties

Ju’ala Service charge Contract to perform a specific task in a given time for

a fee

Muradabah Mark-up financing Agreement in which one party purchases a good

desired by another and sells it to them at a price which

includes a profit agreed to by both parties

Wakalah Agency Contract Contract in which one party (Muwakil) appoints

another (Wakeel) to conduct a certain task on behalf of

the principal, for a fixed commission

Qard Hassana Beneficence loans Loans without interest and profit-sharing

29

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