Yuga Raj Bhattarai, Ph.D^{1}
Abstract
This
study has examined determinants of interest rate spreads of commercial banks in
Nepal using the panel data of 7 commercial banks over the period of 6 years
(20102015). This study has employed the pooled OLS model, fixed effect model
and random effect model to investigate the bankspecific variables affecting interest rate
spread.
The estimated regression models reveal that default risk, profitability and bank size have significant and
positive impact on interest rate spreads. Cash reserve requirement has
negligible effect on interest rate spreads. Thus, this study concludes that the
major determinants of commercial banks’ interest rate spreads are default risk,
profitability and bank size in Nepal.
Keywords:
Interest rate spreads, commercial banks, Nepal, default risk, profitability,
cash reserve requirement
JEL Code: C23, C33, C87, E43, G21
I. Introduction
Banking
sector’s ability to channel savings into productive uses is judged through its
level of depositlending rate spreads. In fact, a commercial bank achieves the
main benefit from the difference between interest paid on deposits and interest
received from facilities and loans. The gap between the lending rates and the
deposit rates is termed bank interest rate spread. Bank interest rate spread is
the interest rate charged by banks on loans to customers minus the interest
rate paid by banks for demand, time or savings deposits. When borrowing rates
are high, it encourages deposit and provides needed funds for the bank to lend
out. However, for a given lending rate, an increase in borrowing rate will lead
to a decline in the interest rate spreads which could affect bank
profitability. Likely, for a given borrowing rate, a lower lending rate will
tend to reduce the bank interest rate spreads which could also affect bank
profitability. However, when lending rates are low, they tend to induce
investment in an economy leading to growth and development.
^{ }
^{1}
Dr Bhattarai is an Associate Professor at Patan Multiple Campus,
Tribhuvan University. He can be reached
at yugarajbhattarai@gmail.com
A high interest rate
spread acts as an impediment to the expansion of
financial intermediation necessary for growth and development of an economy. It
is often argued that the higher the interest rate spread, the higher would be the cost of credit to the borrowers
for any given deposit rate. Alternatively, a high interest rate spread could mean unusually low deposit rates discouraging
savings and limiting resources available to finance bank credit. In this
perspective, Chand (2002) has found the several reasons for high interest
rate spreads such as: lack of adequate competition, scale diseconomies due to
small size of markets, high fixed and operating costs, high transportation
costs of funds due to expensive telecommunications, existence of regulatory
controls and perceived market risks.
Moreover, DemirgucKunt and Huizinga (1998), Moore
and Craigwell (2000), Brock and RojasSuarez (2000), Gelos (2006), Sologoub
(2006), and Crowley (2007) assert that the specific characteristics of
commercial banks that can have an impact on their spreads include the size of
the bank, ownership pattern, the quality of the loan portfolio, capital
adequacy, overhead costs, operating expenses, and shares of liquid and fixed
assets. However, past empirical literatures provide an extensive list of variables
that affect the spreads and categorize these determinants into five main groups
such as: bankspecific variables, systemwide measures of market structure,
regulatory environment, legal and institutional environment and macroeconomic
variables.
In a country like Nepal, a high interest
rate spread raises the cost of credit and
restricting the access of potential borrowers to credit markets thus, reducing
investments and limiting growth potential of the economy. Moreover, problems
become more acute for small businesses, household enterprises and rural
industries which are vital to promoting equitable growth and reducing poverty
in low income countries. The spread between lending rate and deposit
rate in Nepal has been widening over the years. It is noted that this situation
accounts for the slow growth rate of the economy, as private businesses are
unable to borrow at the current interest rate to expand their businesses so as
to create employment to absorb the unemployed masses. There is a general perception
that while lending rates are too high to induce any meaningful investment and
are at the core of low private sector investment in Nepal, however, borrowing
rates are too low for savings mobilization.
In
Nepal, the banking sector plays a dominant role in the financial sector,
particularly with respect to mobilization of savings and provision of credit.
In Nepal, banks and financial institutions were now given full autonomy to
determine their interest rates on deposits and lending. However, commercial
banks in Nepal discourage potential savers due to low returns on deposits and
thus, limits financing for potential borrowers, this is due to banks tendency
of maximizing profits and widening the interest rate spreads. High interest
rate spreads from Nepalese commercial banks attracted a lot of debate in both
public and policy forums. Therefore, an analysis of bank interest rate spreads
has become central to explore its determinants. There is lack of the empirical
studies with respect to the analysis of interest rate spreads at the commercial
bank level in Nepal. Thus, the aim of this study is to empirically investigate
factors that determine interest rate spreads in Nepal.
The
rest of the study is organized as follows: section 2 reviews the literature on
determinants of interest spreads while the research methodology is outlined in
section 3. Section 4 provides findings and discussions followed by conclusion
in section 5. Section 6 incorporates policy recommendations.
II.
Literature Review
The
major studies related to determinants of interest spreads have been reviewed as
follows:
DemirgüçKunt
and Huizinga (1999) examine interest spreads in a crosscountry set up using
data covering commercial banks from 80 countries across the world. The authors
find that differences in interest margins and bank profitability are explained
by several factors such as bank characteristics, macroeconomic variables,
explicit and implicit bank taxation and deposit insurance regulation. After
controlling for factors such as differences in bank activity, the extent to
which banks are leveraged, and the macroeconomic environment, they claim that
lower interest margins and lower profits are associated with larger banks asset
to GDP ratio and a lower market concentration ratio. Additionally, they assert
that foreign banks are associated with higher interest margins and higher
profits compared to local banks in developing countries while the opposite is
true for developed countries.
Ngugi
(2001) has analyzed the interest rates spread in Kenya from 1970 to 1999 and
found that interest rate spread increased because of yettobe gained
efficiency and high intermediation costs. Increase in spread in the
postliberalization period was attributed to the failure to meet the
prerequisites for successful financial reforms, the lag in adopting indirect
monetary policy tools and reforming the legal system and banks’ efforts to
maintain threatened profit margins from increasing credit risk as the
proportion of nonperforming assets. She attributed the high nonperforming
assets to poor business environment and distress borrowing, owing to the lack
of alternative sourcing for credit when banks increased the lending rate, and
the weak legal system in enforcement of financial contracts. According to her
findings, fiscal policy actions saw an increase in treasury bill rates and high
inflationary pressure that called for tightening of monetary policy.
Grenade
(2007) has examined the determinants of commercial banks interest rate spreads
in the Eastern Caribbean Currency Union using annual panel data of commercial
banks. The author claims that spread is found to increase with an increase in
market power, the regulated savings deposit rate, real GDP growth, reserve
requirements, provision for loan losses and operating costs.
Afzal (2011) has
investigated the determinants of interest rate spreads and margins in
Pakistan‘s commercial banking sector in the post transition period from 2004 to
2009. The author employed an exhaustive set of firm level and macro variables
in the model for analysis. The findings
of the study reveal that bank size, operational efficiency, asset quality,
liquidity risk, absorption capacity and GDP growth were important determinants
of banking spreads.
Aikaeli,
Mugizi and Ndanshau (2011) have examined the determinants of interest rate
spreads in Tanzania and argued that factors that determine interest rate spread
can be clustered as bank specific factors, including size, capital structure,
management efficiency, ownership pattern, quality of loan portfolio, overhead
costs, profit maximization motive, and shares of liquid and fixed assets.
Akinlo (2012) has investigated the determinants of
interest rate spreads in Nigeria using a panel of 12 commercial banks for the
period 19862007.The results suggest that cash reserve requirements, average
loans to average total deposits, remuneration to total assets and GDP have
positive effect on interest rate spreads. However, noninterest income to
average total assets, treasury certificate and development stocks have negative
relationship with interest rate spreads.
Mannasoo
(2012) has examined the role of recent global financial crisis on interest
spreads in Estonia. The pure spread is explained by the degree of bank risk
aversion and the market structure of the banking sector. The volatility of
money market interest rates is found to have a longrun impact on the spreads.
Other factors that drive the interest margins are the regulatory variables,
efficiency of banks and bank portfolio effects. Credit risk was found to play a
minimal role while higher bank liquidity was associated with lower interest
margin.
Afroze (2013) has analyzed the interest rate spread (IRS) of the commercial banks in
Bangladesh perspective. Based on the empirical analysis of data for the period
19742011 drawn from various publications of Bangladesh bank and other sources,
the author concludes that there is statistically significant correlation
between interest rate spread and deposit rate but no correlation with the
lending rate. The data series for interest rate spread, deposit rate, and
lending rate contained a unit root and were integrated of order one. However,
the Granger causality test failed to indicate any bilateral causal relationship
between IRS and deposit rate, IRS and lending rate, and also to deposit rate
and lending rate. The author also found that IRS prevailing in the Bangladeshi
banking sector was high compared to that in its neighboring countries.
Nampewo
(2013) has examined the determinants of the interest rate spreads of the
banking sector in Uganda using time series data for the period 19952010. The
author applied the Engle and Granger twostep procedure to test for
cointegration between the bank rate, treasury bill rate, exchange rate
volatilities, the ratio of money supply to gross domestic product (M2/ GDP) and
the proportion of nonperforming loans to total private sector credit. The
author concludes that the interest rate spread in Uganda is positively affected
by the bank rate, the Treasury bill rate and non performing loans. However, the
findings of the study show that M2/GDP ratio and real GDP have a negative
influence on the spread.
Were and Wambua (2013) have investigated the
determinants of interest rate spreads in Kenya‘s banking sector based on panel
data analysis. The empirical results show that bankspecific factors play a
significant role in the determination of interest rate spreads. These include
bank size based on bank assets, credit risk as measured by nonperforming loans
to total loans ratio, liquidity risk, return on average assets and operating
costs. The impact of macroeconomic factors such as real economic growth and
inflation is not significant.
Njeri, Ombui,
and Kagiri (2015) have investigated the determinants of interest
rate spreads in commercial banks of Kenya based on data analysis and quantified
the impact of those factors on interest rate spreads. The research involved collecting secondary data from commercial banks in
Kenya, CBK, financial journals and newspapers as well as primary data
Questionnaires were used to collect primary data where drop and pick
method was used. Based on the results
from the inferential statistics the authors conclude that inflation rate,
credit risk, liquidity ratio and returns on average assets significantly
influence interest rates spreads in commercial banks of Kenya.
The
relevant literature reviewed indicates the existence of several studies in
developed and emerging economies while there was paucity of studies in Nepal.
Therefore, this study has sought to fill the gap of investigating determinants
of interest rate spreads in Nepalese context.
III. Research Methodology
The sample
The
study has analyzed data obtained from 7 commercial banks. The study has
employed descriptive and causal comparative research design. The banks, as
sample, were selected based on the availability of the relevant data on the
various variables used in the study. Thus, convenience sampling method was used
in choosing the banks for the study. Moreover, in selecting the 7 banks for the
study, due care is given to include banks such as: joint venture, domestic,
best performer, average performer and comparatively week performer in the
sample. The banks selected
for the study are: Nepal Investment Bank Ltd, Everest Bank Ltd,
Nepal Bangladesh Bank Ltd, Machahapurche Bank Ltd, Siddarth Bank Ltd., Sunrise
Bank Ltd. and Sanima Bank Ltd. The population of this study is the “A class” commercial
banks listed in the NEPSE. This study assumes that the selected samples fairly
represent the study population.
The
data
Data were collected from the annual reports of the
banks in the sample over the period of 6 years (20102015). The data include
timeseries and crosssectional data, i.e. panel data set. In this study three
panel models such as: pooled OLS model, fixed effect model and random effect
model have been employed for analyzing the data. Pooled OLS model is one where
the data on different units are pooled together with no assumption on
individual difference. The adoption of pooled OLS model is based on the assumption that there is no
group or individual effects among the banks in the sample. Dumicic, Zmuk
and Mihajlovic (2016) have
asserted that the selection of an appropriate panel model (pooled OLS, fixed
effect, and random effect) is made using the Ftest for the fixed effect
model, the BreuschPagan Lagrange Multiplier (LM) test and the Hausman test.
Moreover, Baltagi, Bresson and Pirotte
(2003) have asserted that the choice
between the random effect and fixed
effect estimators can be based upon the
standard Hausman test. If this standard Hausman test rejects the null
hypothesis that the conditional mean of the disturbances given the regressors
is zero, the applied researcher reports the fixed effect estimator. Otherwise,
the researcher reports the random effect estimator. In this study, data
analysis was done using the Gretl 1.1 version.
The
model
This study has
employed three econometric models for analyzing the data. Initially the pooled
OLS model specification is represented by the following equation:
IRS_{it}
= α + β X_{it} + ε_{it}
Where, IRS_{it}
is defined as interest rate spreads for bank i and time t, X_{it }is a
vector of bankspecific variables for bank i and time t; and ε_{it }is
error term for bank i and time t. In this model, the assumption is that the
error term is distributed independently and identically in a manner that the
variance is equal to zero. Based on the prescribed econometric model, the
determinants of interest rate spreads of Nepalese commercial banks have been estimated with the following
regression equation:
IRS_{it }= β_{0} + β_{1} DR_{it }+
β_{2} CRR_{it }+ β_{3} PROF_{it }_{ }+ β_{4} SIZE_{it }+_{ }e_{it}
Where:
IRS_{it = }Interest rate spreads of i^{th} bank in year t
DR_{it }= Default risk,
which is calculated as nonperforming
loans to total loans of i^{th}
bank in year t
CRR_{it }= Cash reserve ratio of
i^{th} bank in year t
PROF_{it
}= Profitability, which is
calculated as net income divided by total assets of i^{th} bank in year t
SIZE_{it
}=Natural logarithm of total assets of i^{th} bank in
year t
β_{0}
= Intercept of the regression line
β_{1,} β_{2,} β_{3, }β_{4 }= The slope which
represents the degree with which interest rates spreads changes as the
independent variable changes by one unit. The priori expectation is that
coefficients β_{1,} β_{2,} β_{3 }and β_{4 }> 0.
e_{it } = error component
In addition to pooled OLS model, this study has
employed fixed effect approach. Fixed effect approach allows for the
unobservable bank heterogeneity. This approach allows for different constants
for each bank in the sample. In this instance, Greene (2002) suggested
following fixed effect model:
Y_{it}
= X_{it} β + α_{i }+ ε_{it}
Where, α_{i
= }z_{i }α, embodies all the observable effects and specifies
an estimable conditional mean. Greene (2002) has asserted that fixed effect
approach takes α_{i }to be a
groupspecific constant term in the regression model. However, the use of a fixed effects model
will eliminate the timeinvariant hidden bank features that affect interest
rate spread, and will make fixed effects estimations less efficient than the
random effect estimation counterpart.
Additionally, random effect model has also been used
in the study. In the random effect model constants for each bank are taken as
random parameters, hence, incorporated in the error term. Greene (2002) pointed
out that if the unobserved individual heterogeneity, however, formulated, can
be assumed to be uncorrelated with the included variables, then the model may
be formulated in random effect form. The model suggested by Greene (2002) can
be written as:
Y_{it}
= X_{it} β + α_{ }+ u_{i }+ ε_{it}
This random effects approach specifies that u_{i } is a
group specific random element, similar to ε_{it
}except that for each group, there is but a single draw that enters
the regression identically in each period.
In this study, these three regression models have
been used in order to investigate the determinants of interest rate spreads in
Nepal.
Study
variables and hypothesis
In
this study, the choice of variables was mostly affected by the approach in
other empirical studies, as well as by determinants of interest rate spreads
suggested by the literature. The factors affecting interest rate spreads were
examined by defining a set of variables. The selected dependent and independent
variables are specified as follows:
Dependent
variable
In view of goal of this
study, the study has used the interest rate spreads as dependent variables.
This refers to the difference between bank’s lending and deposit rate. It was
calculated as average bank lending rate minus average bank deposit rate.
Interest rate spread of commercial bank is hypothesized to be a function of
selected bank specific factors.
Independent
variables
Default risk (DR)
Nonperforming
loans to total loans ratio is used as an indicator of default risk or quality
of loans. This is equal to the average of the past due receivables, overdue and
doubtful to total loans. An increase in provision for loan losses implies a
higher cost of bad debt write offs. Given the riskaverse behavior, banks
facing higher credit risk are likely to pass the risk premium to the borrowers,
leading to higher spreads. Likely, Chirwa and Mlachila (2004) and Sidiqqui
(2012) found a positive impact of default risk (DR) on interest spreads of
commercial banks for Malawi and Pakistan, respectively. In the same manner,
Were and Wambua (2013) have also found that credit risk (default risk) has
positive and significant effect on interest rate spreads. Therefore, a
significant positive relationship is expected between interest rate spreads and
default risk.
H_{1}: Default risk has a significant and positive effect on interest rate spreads
Cash
reserve requirement (CRR)
This is the prescribed percentage of commercial
banks’ total deposits that must be kept with the monetary authority as a
caution. The cash reserve requirements are viewed as the implicit taxes that
increase interest rate spreads because banks tend to shift them to customers by
either increasing the lending rate or reducing the deposit rate. Akinlo and
Owoyemi (2012) conclude that cash reserve ratio is significantly positively
related to interest rate spreads. Akinlo (2012) also asserts that cash reserve requirements
have positive effect on interest rate spreads. In line with past empirical
evidence, it is hypothesized that cash reserve requirement is positively
related to interest rate spreads.
H_{2}: Cash reserve requirement has a significant and positive effect on interest
rate spreads
Profitability (PROF)
In this study, profitability is represented by
return on assets. Return
on assets explains the overall profitability of a bank emanating from
its asset portfolio. It is an effective measure for evaluating the performance
of a bank’s management. A bank with higher profitability can otherwise afford
to charge lower spreads (Norris & Floerkemeier, 2007). However, banks with
a higher return on assets can
have higher spreads while their interestsensitive assets perform better. This
is generally considered as a good indicator to evaluate the profitability of
the assets of a bank in comparison to other banks firms in the same industry.
Siddiqui (2012) finds a positive effect of return on assets on interest
spreads. Afzal and Mirza (2010) conclude that profitability (ROA) is
significant and positive; indicating higher spreads for banks with an efficient
use of assets. Were and Wambua (2013) have found that profitability has
positive and significant effect on interest rate spreads. Thus, a positive
relationship is expected between profitability and interest rate spreads.
H_{3}:
Profitability has a significant
and positive effect on interest rate spreads
Bank
size (SIZE)
In
this study, bank size is measured by natural logarithm of total assets of
selected banks. Bank size is used to gauge the possibility of economies of
scale in banking. Banks that enjoy economies of scale incur a lower cost of
gathering and processing information, resulting in greater financial
flexibility and, ultimately, higher spread. A stronger asset base is expected
to positively impact interest rate spreads (Maudos & Solis, 2009). Ideally
one would expect bigger banks to be associated with lower interest rate
spreads, arguably because of large economies of scale and ability to invest in
technology that would enhance efficiency. However, to the extent that bank size
connotes control of the market in the deposit and loan markets, a positive
relationship between interest rate spreads and bank size should not be
surprising. Afzal and Mirza (2010) have found significant positive effect of
bank size on interest rate spreads. Were
and Wambua (2013) have found
that bank size has positive and
significant effect on interest rate spreads meaning that big banks have
comparatively higher spreads than small banks. In line with past empirical
studies, a positive relationship is expected between bank size and interest
rate spreads.
H_{4}: Bank size has a significant and positive effect on interest rate spreads
IV
Findings and Discussions
Descriptive
statistics
Table 1 presents the descriptive statistics of the
variables used in this study. The minimum and maximum interest rate spreads of
Nepalese commercial banks during the sample period are 2.27% and 5.69%
respectively. The average interest rate spread is about 4.43%. The result
indicates that Nepalese commercial banks produce 4.43% average annual interest
rate spreads.
The results of the default risk, which is calculated as nonperforming
loans to total loans percentage ranged from minimum 0.004% to maximum 17.99%.
The average
credit risk is 2.16% and the standard deviation of the same
variable is 2.93%.
The result shows that Nepalese commercial banks do not have high default risk but it
varies drastically. The average cash reserve requirement is
about 17.79%, meaning that in
average Nepalese commercial banks have kept cash about 17.79% of
banks’ total deposits with the monetary authority as a caution. The standard
deviation of cash reserve requirements percentage shows that CRR varies significantly
during sample period.
Table
1
Descriptive
statistics of variables (n=42)
Variable

Scale

Mean

Std. Deviation

Minimum

Maximum

CV

Skewness

Ex. kurtosis

IRS

Percent

4.434

0.713

2.270

5.690

0.161

0.451

0.538

DR

Percent

2.164

2.926

0.004

17.990

1.352

3.940

18.614

CRR

Percent

17.789

8.580

5.610

34.030

0.482

0.341

1.049

PROF

Percent

1.680

1.331

0.000

8.150

0.792

2.817

11.541

SIZE

Ln

24.191

0.628

22.703

25..371

0.026

0.166

0.432

The
average
profitability (ROA) is 1.68%, which shows the weak
profitability position of Nepalese commercial banks. The
standard deviation of the profitability
(ROA) is about 1.33% which indicates the substantial variation of
profitability during sample period. The standard deviation of the bank size (lnassets)
indicates the minimal variation among the banks about their size during study
period.
Correlation analysis
Table 2 shows the degree of association among the
variables used in the study. The results indicate that interest rate spreads is
positively correlated with profitability and bank size. The results imply that
the interest rate spreads tend to move in the same direction with profitability
and bank size. Moreover, the relationship of interest rate spreads with default
risk seems positive but the relationships look weak. However, interest rate
spread is negatively correlated with cash reserve requirements. The result implies that the relationship is not
strong.
Table
2
Pearson
correlations coefficients (n=42)
IRS

DR

CRR

PROF

SIZE

Variables

1.0000

0.1683

0.0057

0.5113

0.3445

IRS


1.0000

0.0241

0.0466

0.2735

DR



1.0000

0.0268

0.3045

CRR




1.0000

0.0736

PROF





1.0000

SIZE

Moreover,
the correlation matrix of the variables presented Table 2 reveals that all
correlations coefficients among the independent variables are less than 0.40,
implying the absence of multicollinearity. Thus, there is no evidence of
presence of multicollinearity among the independent variables and thus, the
variables chosen seem appropriate for the regression model.
Regression results
In this study, data analysis techniques employed are
panel data regression models. Thus, model diagnostic test statistics were used
in order to choose the appropriate panel data model for the study. Testing and
determination of appropriate panel data model were done by using the ‘Joint
significance of differing group means’, BreuschPagan test statistic, and the
Hausman test. The joint significance of differing group means statistic is F(6,
31) = 1.9442 with pvalue 0.1048. The pvalue is 0.1048 which is higher than
0.05 indicates that pooled OLS model is adequate as compared to fixed effect
model. Likely, BreuschPagan test statistic has been used to compare pooled OLS
model with random effect model. BreuschPagan test statistic shows that LM =
1.6634 with pvalue = prob(chisquare(1) > 1.6634) = 0.1972. The pvalue is
0.1972, which is higher than 0.05, and thus, pooled OLS model is preferred over
random effect model. Moreover, Hausman test statistic has been used to compare
random effect model and fixed effect model. Hausman test statistic is H =
1.0847 with pvalue = prob(chisquare(4) > 1.0847) = 0.8967. The pvalue is
0.8967, which is higher than 0.05, thus the random effects model is preferred
as compared to fixed effect model. In view of model diagnostics statistics, the
pooled OLS and random effects model stood superior among three models
considered for the study. However, the results of these three models have been
presented and discussed to ensure precise estimation of the determinants of
interest rate spreads in the current study.
The
test for normality of residual has been conducted in the current study using
Gretl and the test statistics have been presented in Table 3. In pooled OLS model, the result of
the test for null hypothesis of normal distribution: Chisquare (2) = 1.434
with pvalue 0.4883 shows that null hypothesis is accepted. Thus, panel data
set used in pooled OLS model is normally distributed.
Table 3
Test statistics for normality
Statistics

Pooled
OLS Model

Fixed
Effect Model

Random
Effect Model


Test Statistics for
normality

Chisquare (2)

1.4340

0.4396

1.1124

pvalue

0.4883

0.8027

0.5734

Results
are drawn from GretlStatistical Software
In fixed effect model, the test for normality of
residual was performed. The null hypothesis was that error is normally
distributed. The result of the test statistics is Chisquare(2) = 0.4396 with
pvalue 0.8027. The insignificant pvalue of Chisquare indicates that the null
hypothesis is accepted that error is normally distributed. The test for
normality of residual has also been conducted for randomeffects model. The
null hypothesis was that error is normally distributed. The result of test
statistic is Chisquare (2) = 1.1124 with pvalue 0.5734. The insignificant
pvalue of Chisquare (2) test statistic indicates that the null hypothesis is
accepted. The result proves that error is normally distributed. Thus, the panel
data used in the analysis seems appropriate for the regression models.
The results of the determinants of interest rate
spreads using pooled OLS are presented in table 4.
The value of R^{2 }and adjusted R^{2 }are
0.4580 and 0.3994 respectively. The overall
explanatory power of the regression model looks good with R^{2 }of
0.4580. The result implies that about 45.80% change in interest
rate spreads is
explained
by the variations in explanatory variables, denoting that the regression model
has good fit and is reliable. The overall explanatory
power of the regression model also looks good with R^{2 }of 0.
6062 in the fixed effect model.
The pvalue (F_{Sig.}) of F statistics in the pooled OLS model represent that the model
is fairly fitted well statistically. Because, the Fstatistic,
a measure of the overall significance of the regression, shows that the
explanatory variables employed are significant at the 1% level. The overall
significance of the fixed effect regression model is proved with the pvalue (F_{Sig.}) of F statistics which
is also found significant the 1% level.
Table 4
Regression coefficients
(n=42)
Variables

Pooled
OLS Model

Fixed
Effect Model

Random
Effect Model


Coefficients

t

Sig.

VIF

Coefficients

t

Sig.

Coefficients

t

Sig.


Constant

8.4719

2.2646

0.0295


8.6733

2.4526

0.0200

8.6259

2.5580

0.0148

DR

0.0768

2.4874

0.0175

1.097

0.0791

2.4988

0.0180

0.0785

2.6604

0.0115

CRR

0.0124

1.1646

0.2517

1.118

0.0138

1.3676

0.1813

0.0134

1.4014

0.1694

PROF

0.2664

4.0985

0.0002

1.006

0.2813

4.4136

0.0001

0.2775

4.6131

0.0001

SIZE

0.4990

3.2992

0.0022

1.211

0.5051

3.5336

0.0013

0.5037

3.6981

0.0007


R^{2 }=, 0.4580, Adj.
R^{2 } = 0.3994,
F.= 7.8163, Pvalue(F)= 0.0001,
DurbinWatson statistic = 1.4643,
Pvalue (DW) = 0.2097
Test for differing group intercepts:
F(5,26)=, 1.9442,
pvalue = 0.1048

R^{2 }= 0.6062 Adj.R^{2}
= 0.4791,
F(9,26)=, 4.7717,
Pvalue(F)=, 0.0004,
DurbinWatson= 2.0061,
Pvalue (DW) = 0.4867

BreuschPagan
test:
Chisquare(1) = 1.6634,
Pvalue = 0.1972,
Hausman test:
Chisquare(4) = 1.0847,
Pvalue = 0.8967

***Significant at the 0.01 level (2tailed)
,** Significant at the 0.05 level (2tailed), * Significant at the 0.1 level
(2 tailed). Results are drawn from
GretlStatistical Software
As a test of the presence
of multicollinearity among independent variables in the pooled OLS model,
variance inflation factors (VIF) have been computed. The variance inflation
factors (VIF) show a value less than 1.3 for each variable. The larger the
value of VIF, the more troublesome or collinear the variables and as a
rule of thumb a VIF greater than 10 is unacceptable (Gujarati, 2004). The VIF less than 1.3 for each variable indicates the nonpresence of multicollinearity. Thus, the
independent variables chosen for the models are not suffered from
multicollinearity problem.
The empirical findings show that default risk is
positively related to interest rate spreads in three models estimated. The
coefficient is statistically significant at 5 percent level of significance.
The results show that increases in default risk will increase interest rate
spreads in Nepalese perspective. This result supports the findings of Chirwa
and Mlachila (2004), Sidiqqui (2012), and Were and Wambua (2013), where they
found a positive impact of default risk (DR) on interest rate spreads.
Profitability (ROA) is found
significantly positively associated with interest rate spreads
in all three models estimated. The result is found significant at 1% level of
significance in these three models used. The result indicates that profitable
commercial banks do increase interest rate spreads in Nepalese context. This
result is consistent to the priori expectation and supports the findings of
Afzal and Mirza (2010), Siddiqui (2012) and Were and Wambua (2013), where they found a positive
impact of profitability on interest rate spreads, indicating higher spreads for
banks with an efficient use of assets.
Bank
size is also found significantly positively
associated with interest rate spreads in these three models employed,
meaning that big Nepalese banks have comparatively higher spreads than small
banks. The result is found significant at 1% level of significance in these
three models estimated. The result is consistent to the priori expectation and
also supports the findings of Maudos and Solis (2009), Afzal and Mirza (2010)
and Were and Wambua (2013), where they have found positive and significant
effect of bank size on interest rate spreads.
The
result reveals that the cash reserve requirements is found statistically
insignificant in these three models estimated for explaining interest rate
spreads. Meaning that the strength of its effect on interest rate spreads is
considerably less than what was expected. Moreover, the low coefficients of the
cash reserve requirements in these three models estimated could also reflect
the fact that it doesn’t appear as the influencing variable for interest rate
spreads in Nepalese context.
V. Conclusion
This
study has investigated the determinants of interest rate spreads of commercial
banks listed in the Nepal Stock Exchange. Data were collected from the annual
reports of 7 commercial banks in the sample over the period of 6 years
(20102015). The data were analyzed using pooled OLS model, fixed effects model
and random effects model. The dependent variable chosen in the study is
interest rate spreads and whereas default risk, cash reserve requirement,
profitability, bank size are considered as independent variables. The estimated
regression models reveal that default risk, profitability
and bank size have positive association with interest rate spreads.
However, cash reserve requirement has immaterial impact on interest rate
spreads. Thus, this study concludes that the major determinants of commercial
banks’ interest rate spreads are default risk, profitability
and bank size in Nepalese context.
VI. Policy Recommendations
This study offers
the following recommendations based on the findings from the empirical
analysis.
Interest rate is inevitable in the financial sector
since it is the only way of rewarding depositors and meeting the costs in
commercial banks. The difference between lending and deposit rate can, however,
be controlled. Nepalese commercial banks have excessive levels of interest rate
spreads which can pose a significant threat to lending activities and deposit
collection. Banks should try as much as possible to strike a balance which will
help them to cover cost associated with lending and at the same time, maintain
good banking relationship with their borrowers and depositors. Moreover, bank
management should ensure that appropriate policies procedures, management
information systems and internal controls system should be followed to maintain
interest rate spreads at prudent levels with consistency and continuity.
Commercial banks
should increase the range of alternative investments available to institutional
investors which would improve their flexibility in managing both long term and
short term investments since highconcentration deposits from large depositors
are able to distort higher level spreads based on their leverage with the
individual bank.
There
is need to strengthen bank interest rate spreads policy through effective and
efficient regulation and supervisory framework. Commercial banks should develop
credit procedures, policies and improve analytical capabilities of loans by
which overall credit management could be effective to reduce nonperforming
loans and enhance their profitability. Moreover, commercial banks should avoid
giving out loans that will lead to bad debt. This sort of effort can reduce
interest rate spreads.
In an effort to open up the financial sector, policy
makers should device measures to promote the growth of medium sized banks in a
bid to enhance their ability to penetrate the market so as to break market
dominance by a few banks and also enhance competition. This kind of strategy
will increase competition among banks and hence, can reduce interest rate
spreads.
There
is a need for the government to provide essential infrastructural support to
both lenders and borrowers. The findings of this study also point to the view
that the banks should improve their management practices particularly in the
light of the practices in other developed and developing countries.
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