Interest Rate and Financing of Islamic Banks: Evidence from Pakistan
Maria Hashim
Lecturer,Institute of Business and Management Sciences (IBMS),
Muhammad Nouman
Lecturer,Institute of Business and Management Sciences (IBMS)
Zahoor Khan
Assistant Professor, Institute of Management Sciences
Abstract
Islamic banks are not allowed to charge interest; however, they use interest-based benchmarks as
a pricing reference, due in part to the absence of stable and dependable alternatives. Though,
benchmarking interest rate in Islamic banking does not violate the Shariah rulings. However, it is
usually argued that benchmarking interest rate violates the basic philosophy of Islamic banking
and finance. Moreover, it exposes Islamic banks to the problems of conventional banks,
particularly the interest rate risk. The present study investigates the long term and short
association among interest rate and financing of the overall Islamic banking industry of Pakistan
via different modes. Findings of the study reveal invariably strong links between the Islamic and
the conventional banking systems in Pakistan. These findings suggest that paradoxical as it may
seem, the financing of Islamic banks operating within a dual banking system are vulnerable to
interest rate risk.
Keywords: Islamic banking, benchmarking interest, Interest rate risk, Pakistan
1. Introduction
Islamic banking is a growing rapidly in the Muslim countries and the primary world financial
hubs (Nouman, Ullah, & Gul, 2018). There are more than 300 institutions carrying out interest
free banking in 80 countries. These institutions provide interest free financial services to their
clients, including not only Muslims but also non-Muslims who are having an extreme interest in
the financial products of Islamic banks (Awan, 2009).
Despite the impressive growth of Islamic banking throughout the world, a comprehensive
Islamic financial system is still in its nascence (Nouman et al., 2018). Many issues and
challenges related to the Islamic financial system and its various aspects need to be addressed
yet. Among these is the issue of using interest rate as a benchmark by Islamic banks. Although
Islamic banks do not charge interest, however, they use interest-based benchmarks as a pricing
reference, due in part to the absence of stable and dependable alternatives (Ayub, 2007; El-
Gamal, 2006; Reuters, 2016; Usmani, 2002b).
Many advocates of Islamic banking favor benchmarking interest rate (See for example El-
Gamal, 2006; Hamoud, 1994; Jaman, 2011; Usmani, 2002a). They justify Interest as a
benchmark on the grounds that Islamic banking being a niche market has to co-exist with the
conventional banking (See for example, Hamoud, 1994, Usmani, 2002a). Islamic banks have to
use interest rate as a benchmark to remain competitive and be able to attract deposits from
customers (Jaman, 2011). Moreover, it helps in avoiding arbitrage in the dual banking
environment (Bacha, 2004; Jaman, 2011). Aznan Hasan who is a Shariah advisor to Malaysia's
stock exchange Bursa Malaysia opined in an interview that the presence of two separate
benchmarks for the Islamic and conventional banks will result into a severe turbulence in the
country. In fact, it would open ways to arbitrage i.e., if people see conventional financing
suitable in a given situation, they will switch to conventional financing and vice versa.
On the contrary, many Islamic scholars criticize benchmarking interest as not being adequately
based on real economic activity, which is a core requisite in Islamic finance (Reuters, 2016).
Moreover, it violates the basic philosophy of Islamic banking and finance (Usmani, 2002b) and
makes the transactions of Islamic banks indistinguishable from those of conventional banks
(Ayub, 2007). Therefore, though benchmarking interest rate in Islamic banking does not violate
the Shariah rulings1, it is heavily criticized and has become a subject of debate (Jaman, 2011).
Furthermore, Benchmarking interest rate is not without implications. Though Islamic banking is
interest free, benchmarking interest rate exposes Islamic banks to the problems of conventional
banks, particularly the interest rate risk2 (Bacha, 2004; Rosly, 1999). According to Bacha (2004)
1 Benchmarking interest for estimating the profit or loss of a permitted transaction does not make the transaction
Haram or invalid. Rather the validity of a transaction is determined by its mechanism/nature. (Ayub, 2007).
2In the context of banking system, interest rate risk refers to the effect of changes in interest rate on the net worth,
profits, and/or cash flows of a bank (Bacha, 2004).
Benchmarking interest rate leads to invariably strong links between the Islamic and the
conventional banking systems. This leads to several implications particularly when a large non-
Muslim customer base exists. First, due to strong association between the two banking systems,
there is a possibility of arbitrage between the systems, particularly by the non-Muslims who are
indifferent towards both banking systems. This in turn implies that deposit rates within the
Islamic banking system must change with adjustments in the prevailing interest rate in the
conventional system. Otherwise, rate differentials will prevail leading to arbitrage opportunities.
Third, due to opportunities of such risk free arbitrage through fund flows, Islamic banks become
vulnerable to the repercussions of interest rate volatility that normally pertain to the conventional
banking system. For example, the costs of funds of Islamic banks will change with changes in
the cost of funds of the conventional banking system. Thus, though the effect of interest rate
variation on the Islamic banking industry may be indirect, the repercussions would be the same.
A vast literature has stemmed from this debate. Several studies have empirically investigated the
impact of interest on different aspects of Islamic banks (See for example Haron & Ahmad, 2000;
Kolapo & Fapetu, 2015; Relasari & Soediro, 2017; Shamsuddin, 2014; Zainol & Kassim, 2010).
However, an important concentration apparent within the extant literature is the over-whelming
attention given to the effect of interest rate on the deposits, profitability, risk, and stock prices of
Islamic banks; whereas the financing side of Islamic banks remains relatively unexplored. To
bridge this gap the present study empirically investigates the effect of interest rate on the
financing of Islamic banks operating in Pakistan. Using quarterly data for the time period March-
2003 to September-2018, this paper argues that paradoxical as it may seem, the financing of
Islamic banks operating within a dual banking system may also be vulnerable to the interest rate
risk.
The reset of the paper is structured as follows: section 2 elaborates an integrated review of the
literature, followed by research methodology in section 3. Empirical results and findings are
presented and contextually discussed in section 4, while section 5 concludes the paper.
2. Literature Review
Islamic banking, being based on Islamic principles, discourages interest (Riba) and endorses the
concept of profit sharing (Nouman & Ullah, 2014; Warde, 2000). Therefore, it is characterized
by an array of unique Shariah compliant financial services (Hearn, Piesse, & Strange, 2012).
However, due to the absence of a stable benchmark, Islamic banks are using interest-based
benchmarks as pricing reference for all products throughout the world (Ayub, 2007; Reuters,
2016; Usmani, 2002b).
Benchmarking interest has become a subject of debate in the Islamic banking and finance
literature (Jaman, 2011). Moreover, it has opened several avenues for research. Several studies
have investigated the effect of interest rate on different aspects of Islamic banking industry. For
example; various studies have considered the impact of interest rate on the profitability of
Islamic banks (See for example Gul, Irshad, & Zaman, 2011; Haron, 1996; Khan & Sattar, 2014;
Malik, Khan, Khan, & Khan, 2014). Similarly, many researchers have investigated the effect of
interest rate on deposits of Islamic banks (See for example Akhtar, Akhter, & Shahbaz, 2017;
Hakan & Gülümser, 2011; Haron & Ahmad, 2000; Haron & Azmi, 2005; Kolapo & Fapetu,
2015; Relasari & Soediro, 2017; Yap & Kader, 2008; Zainol & Kassim, 2010). Whereas, the
impact of interest rate on the stock prices of the Islamic banks was considered by Ayub and
Masih (2013), Hussin, Muhammad, Abu, and Awang (2012) and Shamsuddin (2014).
On the contrary, literature on the link between interest rate and the financing of Islamic banks is
relatively limited. Few studies have investigated the effect of interest rate on the financing of
Islamic banks in the Malaysian context. For example, Yusoff, Rahman, and Alias (2001)
attempted to investigate the effect of interest rate on the supply of Islamic financing in Malaysia.
They found that the growth of the growth of financing in Islamic banking is significantly
influenced by changes in interest rate. While, Yap and Kader (2008) and Kader and Leong
(2009) empirically investigated how interest rate fluctuations in a dual banking system effects
the demand for the financing Islamic banks. Using monthly data of Malaysian Islamic banks for
the time period 1999 to 2007, they found that any rise in the base offering rate would motivate
profit oriented clients to gain funding from Islamic banks and vice versa. The study concluded
that Islamic banks, though operating on interest free ideologies, are prone to interest rate risk due
to the fact that most of the customers are profit seekers. Similarly, Khalidin and Masbar (2017)
investigated the effect of interest rate on Islamic banks’ financing in the Indonesian context.
However, by far, the link between interest rate and financing of Islamic banks remains
unexplored in the context of other countries. Hence, the current study contributes to the extent
literature by investigating the link between interest rate and the financing portfolio of Islamic
banking industry in the Pakistan.
3. Research Methodology
3.1 Data and variables
In this research secondary data has been used. Quarterly data on the financing mix of Islamic
banking industry for the time period December-2003 to September-2018 was obtained from
Islamic banking bulletins issued by the Islamic banking department (IDB) of State Bank of
Pakistan (SBP). The financing mix of the Islamic banking industry includes the financing of all
scheduled Islamic banks operating in Pakistan via different modes. Among different modes of
financing, the most dominant modes including Murabahah, Diminishing Musharakah, Ijarah,
Salam, Istisna, Musharakah, and Mudarabah were considered for the present study. On the other
hand the time series quarterly data of interest rate was collected from the SBP official website.
Since, in Pakistan Islamic banks use Karachi Inter Bank Offer Rate (KIBOR) as a benchmark for
determining the cost of financing. Therefore, KIBOR was used as proxy for interest rate.
3.2 Statistical Tests and Model
Different statistical tools were used for the analyses of the data. The Phillips Perron (PP) and
Augmented Dickey-Fuller (ADF) tests were used for testing the unit root problem at level and 1st
difference of each series. Moreover, the multivariate Johansen and Jusiles (JJ) Co-integration test
has been used for testing long run association among interest rate and the financing of Islamic
banking industry via different modes. Finally, Vector Error Correction Model (VECM) has been
used for testing the short run relationship between the interest rate and the financing of Islamic
banking industry.
3.2.1 Unit Root
Economic and financial time series are usually influenced by the changing environment (Zivot &
Wang, 2006), particularly the dynamic state of economy and natural disasters. Therefore, the
time series variables usually follow random walk ( i.e., they do not exhibit constant mean or
variance, or both across time), referred to as the unit root (or non-stationary) problem (Gujarati,
2009). The unit root induces problems in statistical inference involving time series models.
Therefore, the non-stationary time series cannot be predicted precisely.
Two tests are used in the present study for testing unit root problem including the Phillip-Perron
(PP) test (Phillips & Perron, 1988) and Augmented Dicky-Fuller (ADF) test (Dickey & Fuller,
1981). The equation for ADF test is given as follows:
∆𝑍𝑡 = 𝛾1 + 𝛽𝑧𝑡−1 + 𝜋 ∑ ∆𝑍𝑡−1
𝑘
𝑡=1
+ 𝜇𝑡 (1)
In above equation ∆ represent the 1st difference operator, 𝛾1 is the constant, the coefficient 𝛽 =
(𝜌 − 1), while 𝜇𝑡 is the error term. The null hypothesis for the above equation is H0: 𝜌 = 1 or 𝛽 =
0, which implies the non stationary nature of the time series i.e., having a unit root. The
alternative hypothesis is H1: 𝛽 < 0, implying that the time series is stationary. For optimal lag
selection the Hannan-Quinn criterion (HQC), Akaike information criterion (AIC), and Schwarz
information criterion (SIC) are used.
On the other hand, for Phillip-Perron (PP) test the following equation is used:
∆𝑧𝑡 = 𝛼1 + 𝜋𝑧𝑡−1 + 𝜇𝑡 (2)
Where, 𝛼1 is the constant term, and 𝜇𝑡is the error term. Both ADF and PP test produce same
results, however, they differ mainly on how the heteroskedasticity and serial correlation in the
error terms is dealt with (Akbar, Ali, & Khan, 2012). In particular, where the ADF test uses a
parametric auto-regression to estimate the error term in the test regression, the PP test, being a
non-parametric test, ignores the serial correlation. In the present study both PP test and ADF test
are applied for cross checking and ensuring accuracy of the results.
To resolve the unit root problem, the non-stationary time series need transformation (Gujarati,
2009). The difference transformation in most cases resolves the unit root problem. However, if a
time series is stationary, it does not need differencing and is called stationary at level or
integrated of order zero i.e., I(0). On the other hand, If a time series is non-stationary but it
becomes stationary through the first difference transformation, the time series is termed as
integrated of order one I(1). Similarly, if a variable becomes stationary after differencing for the
second time, it is called integrated of order two i.e., I(2), and so on.
3.2.2 Cointegration
Two time series are said to be cointegrated when they follow a common equilibrium path.
Statistically speaking, in certain conditions the linear combination of two or more non-stationary
series (i.e., I(1)) may result into a stationary time series (i.e., I(0)). In such a case the variables
are said to be cointegrated (Gujarati, 2009). In other words, an array of time series are termed as
cointegrated if all of them are integrated at the same order and their linear combination is
stationary. Such linear combination imply the presence of a long-term relationship among the
variables (Johansen & Juselius, 1990). Based upon this notion, cointegration is used to test the
long run relationship among two or more time series.
Two approaches are widely used to investigate Cointegration in time series including: (i) Engle
and Granger (1987) unit root test of the regression residuals known as the Engle-Granger (EG)
cointegration test, and (ii) the Johansen and Juselius (1990) maximum-likelihood based test
known as the Johansen and Juselius (JJ) cointegration. Among these, the JJ Cointegration is
generally more powerful since it covers the short comings of EG Cointegration test. Therefore,
the present study applies JJ cointegration to investigate the long-term relationship between
interest rate and financing portfolio of Islamic banking industry. The general form of JJ model is
given as follows:
∆𝑥𝑡 = 𝜙𝑥𝑡−1 + ∑ 𝜗𝑖∆𝑥𝑡−𝑖
𝑘−1
𝑡=1
+ 𝜆𝑦𝑡 + 𝜀𝑡 (3)
Where, 𝑥𝑡 is (nx1) vector of I(1) time series variables, 𝜆𝑦𝑡 is a vector of constants, 𝜙 is the (n ×
n) matrix of long term parameters of the error correction, while 𝜗𝑖 represents the (n × n) matrices
of short term parameters of lagged difference factor.
The JJ Cointegration involves a three step procedure: In the first step the order of integration is
determined for each variable. In the second step the optimal lag length is selection using some
basic criterion e.g., Akaike information criterion (AIC), Schwarz information criterion (SIC), and
Hannan-Quinn criterion (HQC). Whereas, in the last step the cointegrating vectors are
determined using two tests namely Trace test, and Maximum Eigen value test, which eventually
indicate the presence or absence of cointegration. The formulas for Trace test, and Maximum
Eigen value test respectively are given as follows:
𝜆𝑡𝑟𝑎𝑐𝑒(𝑟) = −𝑇 ∑ In
𝑘
𝑖=𝑟+1
(1 − �̂�𝑖) (4)
𝜆𝑚𝑎𝑥(𝑟,𝑟+1) = −𝑇In(1 − �̂�𝑟+1) (5)
Where, r represents the number of cointegrating vectors under the null hypothesis while �̂�𝑖 are
the estimated Eigen values from the estimated matrix 𝜙. The trace and maximum Eigen values
tests help determine the number of cointegrating vectors. The presence of one or more
cointegrating vectors ultimately indicate that the variables are cointegrated, which implies that
the variables cannot move independently of each other. Put differently, the coitegration helps in
identifying the variables that would not drift too far from each other in the longer term, and
would tend to revert to the equilibrium (i.e., the mean distance between them).
3.3.3 Vector Error Correction Model (VECM)
After determining the cointegration, Vector Error Correction Model (VECM) is used to examine
speed of the adjustment process towards the long-term equilibrium i.e., how time series reconcile
errors while it pursues the long-run equilibrium. The mathematical presentation of VECM is as
follows:
∆y𝑡 = 𝑘 + ∑ 𝛽1𝑖∆𝑋1𝑡−𝑖
𝑘
𝑖=0
+ ∑ 𝛽2𝑖∆𝑋2𝑡−𝑖
𝑘
𝑖=0
… + ∑ 𝛽𝑛𝑖
𝑘
𝑖=0
𝛥𝑋𝑛𝑡−𝑖 + 𝑍1𝐸𝐶1𝑖−1 + 𝜀1𝑡 (6)
Where K is constant, Yt is the dependent variable, and ∆ is the 1st difference operator.
Similalry, X represents the independent I(1) variables 1…n, 𝛽1 to 𝛽𝑛 represent the coefficient of
independent variables X1…. Xn respectively, Z1 is the coefficient of error correction term, EC1t -
1 is the error correction term , and 𝜀1𝑡 is the residual or error term.
4. Results and Discussion
Table 1 presents results of Philips and Perron (PP) test and Augmented Dickey and Fuller (ADF)
test. Results of both tests indicate that all variables have unit root problem at level. However,
these variable become stationary at the first difference indicating that all variables are integrated
at order 1 I(1). Hence, the Johansen and Juselius (JJ) co-integration test can be used to check the
long run association among the variables under consideration.
Table 1: Results of Unit Root test
Variables
Augmented Dicky Fuller (ADF) test Phillips—Perron (PP) test
At level At first difference At level At first difference
Pro.
Pro.
Pro.
Pro.
Murabahah -3.038443 0.1353 -5.957237 0.0001 -3.04617 0.1333 -25.4406 0.0000
Ijarah -1.379737 0.8500 -3.768033 0.0314 -0.72603 0.9638 -6.59459 0.0000
Musharakah -1.010998 0.9307 -6.595182 0.0000 -3.91156 0.9102 -6.60906 0.0000
Dimin. Musharakah 0.855359 0.9997 -6.154707 0.0000 0.716264 0.9995 -6.20161 0.0000
Salam -2.126865 0.5120 -8.159308 0.0000 -1.83400 0.6671 -10.4342 0.0000
Istisna -2.723863 0.2332 -13.13378 0.0000 -5.21445 0.2332 -12.198 0.0000
Mudarabah -2.975746 0.1523 -6.123009 0.0001 -2.55418 0.3022 -6.17788 0.0000
Total investment -0.966195 0.9360 -5.118414 0.0011 -1.17317 0.9007 -5.08033 0.0012
Interest Rate -2.147901 0.5051 -4.29417 0.0077 -1.63501 0.7622 -3.96501 0.0177
Table 2 presents results of Johansen and Juselius (JJ) co-integration test. The JJ co-integration is
applied to evaluate the Long run relationship among interest rate and modes of financing. The
Trace statistics and the Maximum-Eigen values indicate that there are at most seven (07) co-
integrating equation, indicating that the variables are co-integrated. Consequently, based on the
results it is concluded that long-run relationship exists between interest rate and different modes
of Islamic financing. These results are consistent with the findings of Kader and Leong (2009),
Khalidin and Masbar (2017), Yap and Kader (2008), and Yusoff et al. (2001).
Table 2: Result of multivariate Johansen and Juselius (JJ) co-integration test
No. of
Hypothesized
CE(s)
Trace Stat. Critical Value Prob.** Max-Eigen
Stat. Critical Value P-value.**
None * 629.2249 197.3709 0.0001 179.2253 58.43354 0.0000
At most 1 * 449.9996 159.5297 0.0000 123.1880 52.36261 0.0000
At most 2 * 326.8116 125.6154 0.0000 111.1018 46.23142 0.0000
At most 3 * 215.7098 95.75366 0.0000 94.56068 40.07757 0.0000
At most 4 * 121.1491 69.81889 0.0000 43.69849 33.87687 0.0025
At most 5 * 77.45065 47.85613 0.0000 36.91135 27.58434 0.0024
At most 6 * 40.53929 29.79707 0.0020 28.82633 21.13162 0.0034
At most 7 11.71296 15.49471 0.1712 8.993764 14.26460 0.2867
At most 8 2.719197 3.841466 0.0991 2.719197 3.841466 0.0991
Since all the financing modes of Islamic banks of Pakistan and interest rate reveal cointegrating
(long-run) relationships, VECM was assessed to model short-run dynamics of each scheme.
Table 3 reports the results of the VECM model. These results indicate that, the Error Correction
Terms (ECTs) are statistically significant and negative for most of the equations indicating the
propensity to adjust to any aberrations in the long-run equilibrium. The significance of these
ECTs provides further proof for a cointegrating relationship among the interest rate and the
financing modes or products of Islamic banks.
Table 3: Results of Vector Error Correction Model
Error
Correction: D(Interest) D(D-MUSH) D(IJ) D(IST) D(MUD) D(MUR) D(MUSH) D(SA)
CointEq1 -0.672253 -3.382036 -0.581539 0.430316 -24.46044 -2.560423 2.655683 -1.745189
(0.11804) (2.11383) (0.77197) (1.14907) (35.6962) (8.24812) (2.38265) (1.52503)
[-5.69530] [-1.59995] [-0.75332] [ 0.37449] [-0.68524] [-0.31043] [ 1.11459] [-1.14437]
CointEq2 -0.037377 0.043735 0.031475 0.493875 2.432885 0.506982 0.237529 0.127325
(0.01292) (0.23134) (0.08449) (0.12576) (3.90666) (0.90269) (0.26076) (0.16690)
[-2.89339] [ 0.18905] [ 0.37255] [ 3.92726] [ 0.62275] [ 0.56164] [ 0.91091] [ 0.76287]
CointEq3 0.318315 -2.442175 -0.420135 -0.206332 -26.88263 -1.974563 2.458692 0.171841
(0.08369) (1.49868) (0.54731) (0.81467) (25.3082) (5.84782) (1.68927) (1.08123)
[ 3.80367] [-1.62955] [-0.76763] [-0.25327] [-1.06221] [-0.33766] [ 1.45548] [ 0.15893]
CointEq4 0.050100 0.001641 -0.012469 -2.227717 -3.342971 -0.953983 -0.975892 -0.246477
(0.05626) (1.00753) (0.36795) (0.54769) (17.0141) (3.93136) (1.13566) (0.72688)
[ 0.89049] [ 0.00163] [-0.03389] [-4.06750] [-0.19648] [-0.24266] [-0.85932] [-0.33909]
CointEq5 -0.010013 -0.011126 0.009922 -0.027594 -0.513296 -0.067951 -0.017963 -0.017378
(0.00151) (0.02704) (0.00988) (0.01470) (0.45668) (0.10552) (0.03048) (0.01951)
[-6.63032] [-0.41143] [ 1.00460] [-1.87703] [-1.12396] [-0.64394] [-0.58928] [-0.89069]
CointEq6 0.027583 -0.072926 -0.017174 -0.423708 -3.977291 -0.905436 0.017069 0.027022
(0.01477) (0.26456) (0.09662) (0.14381) (4.46758) (1.03230) (0.29820) (0.19087)
[ 1.86715] [-0.27565] [-0.17776] [-2.94626] [-0.89026] [-0.87711] [ 0.05724] [ 0.14158]
CointEq7 -0.069444 0.708680 0.073959 0.316387 5.691700 -0.099243 -0.629024 -0.003684
(0.02495) (0.44684) (0.16318) (0.24290) (7.54577) (1.74356) (0.50366) (0.32237)
[-2.78316] [ 1.58598] [ 0.45323] [ 1.30254] [ 0.75429] [-0.05692] [-1.24890] [-0.01143]
The p-values for each co-integrating equation are presented in Table 4 to determine whether
short run relationship exists among variables. To calculate p-values the system equations have
been used. The estimated coefficients of the ECT indicate the speed of adjustment towards the
equilibrium point. The co-efficient of co-integrating equation 1 indicate that 67.22% deviance in
the interest rate from its equilibrium point recuperate every quarter (since the current study uses
quarterly data). The probability value of the error correction coefficient for interest rate is
significant (less than 0.05) and negative in sign, categorically proposing that short run causality
exist, which moves from the interest rate to financing modes of Islamic banking industry.
Table 4: Probability values for System Equations
Coefficients Standard Errors t-Stat P-value
C (1) - 0.672253 0.118036 - 5.695301 0.0001
C (2) - 0.037377 0.012918 - 2.893386 0.0126
C (3) 0.318315 0.083687 3.803666 0.0022
C (4) 0.050100 0.056261 0.890491 0.3894
C (5) - 0.010013 0.001510 - 6.630320 0.0000
C (6) 0.027583 0.014773 1.867147 0.0846
C (7) - 0.069444 0.024952 - 2.783157 0.0155
C (8) 0.036347 0.125650 0.289269 0.7769
C (9) - 0.062175 0.091319 - 0.680851 0.5079
C (10) 0.017809 0.012012 1.482567 0.1620
C (11) - 0.013637 0.013204 - 1.032798 0.3205
C (12) - 0.152602 0.086830 - 1.757486 0.1023
C (13) - 0.043072 0.038353 - 1.123042 0.2817
C (14) 0.020206 0.032544 0.620872 0.5454
C (15) 0.013767 0.013713 1.003936 0.3337
C (16) 0.008078 0.001757 4.597213 0.0005
C (17) 0.003570 0.001527 2.338428 0.0360
C (18) - 0.026675 0.015059 - 1.771422 0.0999
C (19) - 0.009682 0.009788 - 0.989160 0.3406
C (20) - 0.019166 0.019070 - 1.005032 0.3332
C (21) 0.007437 0.015362 0.484134 0.6363
C (22) - 0.000193 0.021206 - 0.009104 0.9929
C (23) - 0.014031 0.009731 - 1.441956 0.1730
C (24) 0.423356 0.325637 1.300085 0.2162
The results of VECM reveal that there are seven equations which are negative in sign and
significant, indicating that the short run association exist between the time series and are capable
to adjust their errors from long run equilibrium in sufficient way.
5. Conclusion
The findings of this study suggest that a significant long-term and short-term relationship exists
between the interest rate and the financing of Islamic banking industry via different modes. Thus
any rise in the base offering rate would motivate profit oriented clients to gain funding from
Islamic banks and vice versa. The study therefore concludes that invariably strong links exist
between the Islamic and the conventional banking systems due to benchmarking interest rate.
Therefore, Islamic banks, though operating on interest free ideologies, are prone to interest rate
risk due to the fact that most of the customers are profit seekers. Our findings are consistent with
the view of Bacha (2004) and Rosly (1999) who suggest that benchmarking interest based rate
exposes Islamic banks to the interest rate risk. Based on the findings it is strongly recommended
that Islamic banks should have their own benchmark.
6. References
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