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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
Impact of Inflation on the Ghanaian Banking Industry:
A Blessing or a Curse?
David Kwashie Garr
Faculty of Business Administration,
Islamic University College, Ghana.
Email: [email protected]
Abstract — The purpose of this research was to determine the effect
of the volatility of the macro economy on credit risk, interest rate
spread and the performance of banks using inflation volatility as a
proxy for the macro economy. The study used both secondary and
primary data for the analysis. The research covered all the thirty-three
banks that operated in Ghana between 1990 and 2010. The
GARCH model was adopted in modelling inflation volatility.
Analysis was carried out in three scenarios: analysis of questionnaire,
descriptive data analysis and regression analysis. Regression analysis
was ran using the panel root, co-integration and Fully Modified
Ordinary Least Square methods. Primary data source was
questionnaire completed by 853 bank customers. The results
established a positive relationship between past volatility of inflation
and current period inflation. It was also confirmed that inflation
influenced credit risk, interest rate spread and bank performance
significantly. The results suggest that policymakers must pay attention
to both the macro and microeconomic variables to achieve efficiency in
the industry and the economy.
Index Terms — credit risk, interest rate spread, bank performance,
volatility, efficiency.
INTRODUCTION
Even though most developing countries have gone through
costly banking sector reforms, the sector is still
characterized by persistently high interest rate spreads, high
loan losses and high level of inefficiency. The situation has
been aggravated by the high levels of inflation that has
bedevilled these economies for a very long time. The
business of banking involves taking and managing risks. A
major activity of banks is lending which involves the risk
that the borrower will not pay back the loan as promised,
and paying a fixed rate of interest on term deposits. Also
lending rates could drop, leaving the bank earning less on
its investments than it is paying out on deposits. The debate
on the justification for studying bank activities by focusing
on risk management can be traced to Merton (1974) who
argued that financial systems should be analysed in terms of
a functional perspective rather than an institutional
perspective since over long periods of time, functions have
been much more stable than institutions. In the words of
McKay and Sowa (2008), one of the defining characteristics
Manuscript received: June 29, 2016; revised: July 31, 2016;
accepted: October 30, 2016.
Corresponding author: David Kwashie Garr.
of the Ghanaian macro economy has been its high and
often variable rates of inflation. High and variable inflation
is typically seen as a symptom or indicator of
macroeconomic instability.
This paper investigates the effect of inflation on the
activity of banks, using secondary data from Ghana
covering the period 1990 to 2010 and also a survey using
questionnaire completed by 853 bank customers. Inflation
can have positive and negative effects on an economy.
Negative effects include a decrease in the real value of
money and other monetary items over time; uncertainty
about future inflation may discourage investment and
saving. Positive effects include a mitigation of economic
recessions, and debt relief by reducing the real level of debt.
Economists believe that negative effects of inflation far
outweigh the positive effects and this calls for concern in a
country like Ghana that experiences high and volatile
inflation. A study of the effect of inflation on credit risk,
interest rate spread and the efficiency and profitability of
the banking sector is therefore very important for a country
like Ghana.
This study assesses the influence of macroeconomic
volatility, with particular focus on inflation volatility on
credit risk (CR), interest rate spread (IRS) and bank
performance (BP) using a short unbalanced panel data for
thirty-three banks in Ghana for the period 1990 to 2010.
Sources of secondary data are the Bank of Ghana and the
various commercial banks in Ghana. The study began with
an analysis of the survey data where possible and the results
confirmed with the regression analysis:
(a) The relationship between inflation and inflation
volatility.
(b) The effect of inflation and inflation volatility on CR.
(c) The effect of inflation on IRS.
(d) The effect of inflation on bank performance.
(e) Other macroeconomic factors influencing CR, IRS and
bank performance.
(f) Microeconomic factors influencing CR, IRS and bank
performance.
METHODS
This section presents the results of empirical studies
done in the area of inflation and inflation volatility and their
impact on credit risk, interest rate spread and bank
performance respectively, the research methodology, model
specification.
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
Relationship between Inflation and
Inflation Volatility
Dagha (2007) referred to volatility as the frequency on
movement on upside or downside. It is considered a
measure of risk, and investors as well as businesses want
premium for investing in risky assets. According to Berry
(2010), volatility clustering models attempted to capture the
volatility of the financial markets, which are sometimes low,
sometimes high, over a given period of time. But within
each state (and over a short time period), there is a strong
chance that a day of high volatility will be followed by
another day of high volatility. Therefore, we may estimate
volatility conditionally to the observation of previous days.
Nor (2009), Kontonikas (2004) Cukierman and Meltzer
(1986) also have observed that on the theory side, there is a
positive relationship between the level of inflation and
inflation uncertainty. Higher inflation leads to greater
uncertainty. Rother (2004) confirmed the above assertions
by researchers but added that the harmful effect of inflation
on growth is driven by inflation volatility. That high
inflation induces high inflation volatility and uncertainty on
growth. However, Holland (1995) presented a dissenting
view that greater inflation uncertainty leads to lower
average inflation, not higher inflation rate if central bank
attempts to minimize the welfare losses arising from
inflation uncertainty. A similar study done recently on
Jordan by Ananzeh, Jdaitawi and Alwan (2014) and Hossain
(2014) also modeled inflation in GARCH process to
generate conditional volatility of the inflation series. The
results of the GARCH model according to their study
indicated strong support for the presence of a positive
relationship between the level of inflation and its
uncertainty.
country. Even though there are a number of studies on the
relationship between inflation and inflation volatility and
credit, not much has been done on credit risk. This present
work has attempted to fill that gap.
Relationship between Inflation and Inflation
Volatility and Credit Risk
Pioneering works by Stiglitz and Weiss (1981) and
Williamson (1987) on the relationship between inflation
volatility and credit emphasised that increased uncertainty
in the economy causes the banks to ration credit leading to
disequilibrium in credit markets and this has been
confirmed by Landskroner & Ruthenberg (1985), and
Miller (1992) who have found a negative relationship
between total credit and inflation uncertainty due to
increased bank costs. According to the consultative paper
issued by the Basel Committee on Banking Supervision in
July 1999, the major cause of serious banking problems
continues to be directly related to among others a lack of
attention to changes in economic or other circumstances
that can lead to deterioration in the credit standing of a
bank’s counterparties. According to Saxton (2005), tests on
both developed and developing economies have indicated
that inflation uncertainty has significant effect on credit
markets either directly or indirectly regardless of the depth
of financial markets. Therefore, the removal of inflation
uncertainty will decrease the risk and will ensure efficiency
in the allocation of resources and growth of investment in a
Other Macroeconomic Variables
This research laid emphasis on macroeconomic factors
that it considered important for its objectives. These factors
are, inflation and inflation volatility, Treasury Bill Rate
(TBR), Discount Rate (DR), Government borrowing (GB)
and Required Reserve (RR). Also investigated is the Gross
Domestic Product Per Capita (GDPPC) which is
considered as a proxy for Economic Growth.
Relationship between Inflation and Interest
Rate Spread
Hadjimichalakis and Hadjimichalakis (1995) and Ngugi
(2001) also observe that as anticipated inflation rises,
borrowers expect to repay their debt in inflation-cheapened
dollars and, thus, are willing to pay a higher interest rate.
Lenders demand a higher interest rate to compensate them
for the expected loss of purchasing power from inflation.
An earlier work by Cukierman and Hercowitz (1990)
explains that when the number of banking firms is finite, an
increase in anticipated inflation leads to an increase in
interest spread but when banking firms approach infinity
(competitive case), there is no correlation between interest
spread and inflation. Bawumia, Belnye and Ofori (2005)
also confirmed that the inflation variable, representing
inflationary changes, taken as the second difference of the
CPI, is positively associated with interest spreads. Tennant
and Folawewo (2008) find that inflation has a consistently
positive and significant impact on both changes in and
levels of banking sector spreads. This suggests that low
inflation is a critical element in the minimization of banking
spreads.
Relationship between Inflation and
Bank Performance
Most studies, for example Bourke (1989), Molyneux
and Thornton (1992) observe a positive relationship
between inflation and bank performance. Boyd, Levine and
Smith (2001) showed that countries with high inflation have
underdeveloped financial systems and banks, while,
Huizinga (1993), and George and Morriset (1995) claimed
that uncertainty of inflation would sometimes lead to higher
profit fluctuations and may result in increased investment.
Microeconomic Variables
Microeconomic factors are part of the models to
ensure a balance between the macro and micro economic
forces, since existing literature has emphasized the role of
both bank-specific and industry-specific factors in addition
to the macroeconomic factors in the activity of banks.
Ownership structure, that is, whether the banks are foreign
or local (Demirguc-Kunt & Huizinga, 1998; Koeva, 2003
and Grenade, 2007) and Management Efficiency (ME)
measured as cost/income ratio are known as two of bankspecific factors influencing CR, IRS and BP. Two
important industry-specific factors featuring in the models
are Financial Sector Development (FSD) and competition.
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
FSD is measured in this research in two ways; firstly as the
ratio of money supply to GDP (M2+/GDP), (FSD 1) and
secondly as the ratio of bank assets to GDP (FSD 2). The
research used the Hirschman-Herfindahl Index (HHI) as an
indicator for the level of competition.
Model Specification
Empirical literature identifies several factors as
impacting CR, IRS and BP in the banking industry. These
have categorized the factors into three broad groups: bank
specific factors, industry and macroeconomic factors ( Das
and Ghosh, 2007; Gonzalez-Hermosillo, Pazarbasioglu and
Billings 1997; and Demetriades and Luintel, 1996). Also
Rajaraman, Bhaumik and Bhatia (1999), Das and Ghosh
(2007) identify macroeconomic factors affecting credit risk.
Credit Risk, Interest Rate Spread and Bank
Performance Models
This
study
investigates
the
influence
of
macroeconomic volatility on CR, IRS and BP using the
panel models below adopted from the works of Garr (2013)
and Garr and Kyereboah-Coleman (2013). The models
express credit risk, interest rate spread and bank
performance as a function of a vector of controls including
bank level characteristics, industry variables and
macroeconomic indicators:
CRit   i   j X Fjit   j X Ijit    j X Mjit   it
PERFit   i   j X Fjit   j X Ijit    j X Mjit  it
IRS it   i   j X Fjit   j X Ijit    j X Mjit   it
where CR represents credit risk, measured by the ratio of
Loan Loss Provision to total bank asset and the ratio of net
interest income to total bank asset; IRS represents interest
rate spread, measured as the difference between bank
lending rate and bank borrowing rate (Garr 2013; Garr and
Kyereboah-Coleman 2013; Folawewo and Tennant 2008;
Sologoub 2006; Moore and Craigwell 2000; Demirguc-Kunt
and Huizinga 1998); and PERF represents bank
performance which is measured using two approaches.
These are the health status of the banking system and
the efficiency of the banking system. The health status is
evaluated using two indices; (1) the solvency index (SI),
which is the loan loss provision as a percentage of total
loans, and (2) transactions costs (TC), which is the total
operating expenses as a percentage of total operating
income. Transaction cost is used as a proxy for assessing
the amount of resources needed to generate a unit of
revenue (through loans and other income-generating
activities). The efficiency of the banking system is measured
using return on assets (ROA) which is measured as the ratio
of profit before tax (PBT) to Total Assets (TA) (Institute of
Statistical, Social and Economic Research [ISSER], 2006).
XF represents a vector of bank level characteristics, XI
represents industry characteristics and XM represents
macroeconomic indicators including inflation volatility; αi
represents bank specific unobserved heterogeneity and  it
is the error term.
Measuring Inflation Volatility
Volatility in inflation is modeled using the Generalized
Autoregressive Conditional Heteroskedasticity Mean
(GARCH-M) process. The model for inflation is specified
as follows:
t =  + t + β1t-1 + t
t = 1990 ... 2010
Where π represents inflation rate and t is assumed to be
normally distributed with zero mean and variance ht. Thus
t~N(0, σt)
The conditional variance, σt equation described above
is a function of three terms: the conditional mean
information about volatility from the previous period,
measured as the lag of the squared residual from the mean
equation, t-m (ARCH term), and last periods forecast
error variance, t-1, (the GARCH term). The ARCH-M
process is specified as:
p
q
n 1
m 1
 t2      n t 1    m  t2 m
If δ = 0 then the model reduces to the ARCH-M
process. The choice of p and q is informed by the Akaike
Information Criterion (AIC), and the Schwartz Bayesian
Information Criterion (SBIC). This study allows for
heterogeneity only through individual effects in both the
conditional mean and conditional variance equations.
Estimation Procedure
The study employed the panel data model below:
Yit   i   t   ' X jit   it
t = 1,…, T; j  1,..., k & i  1,....N
where α represents cross sectional heterogeneous effect
which is time invariant, δ time variant effect but crosssectionally invariant, Xit is a vector of explanatory variables,
i represents the number of Banks, j is the number of
explanatory variables and t represents time period,
measured in years. ε is the unobserved time specific effect
and μ is the idiosyncratic error term.
Techniques, Tools, Approaches, and Devices
The methodological approach is in three scenarios,
namely, panel data using regression analysis, descriptive
data analysis with the aid of graphs and survey data analysis.
The regression analysis used the panel root, co-integration
and Fully Modified Ordinary Least Square method. The
principal method used to analyse the cross-sectional time
series behaviour of the data involves panel root, cointegration, and fully modified ordinary least square model
(FMOLS). To estimate dependent variables the study uses
panel cointegration framework. The cointegration analysis
of panel data involves three steps:
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
First, Panel Unit Root Tests; the purpose of unit root
tests is to check the stationary of data. Two different test
statistic, ADF - Fisher Chi-square and ADF - Choi Z-stat
are used for the unit root testing. Secondly, Cointegration
Tests; Cointegration test is primarily used to investigate the
problem of spurious regression, which exists only in the
presence of non-stationary. The Kao (1999) test is also used
to check cointegration of data. Kao Residual Cointegration
Test technique is used in estimating the cointegration to
determine whether each of the series is integrated of the
same order. The number of co-integration ranks (r) is tested
with ADF - Fisher Chi-square and ADF - Choi Z-stat test
statistic. The ADF - Fisher Chi-square and ADF - Choi Zstat statistic tests the null hypothesis of no cointegrating
vector against the alternative of at least one cointegrating
vector. Lastly, Panel Fully Modified OLS estimates the
individual long run relationship by using fully modified
ordinary least square (FMOLS) technique developed by
Pedroni (2000). The resulting Fully Modified OLS
(FMOLS) estimator is asymptotically unbiased and has fully
efficient mixture of normal asymptotic allowing for
standard Wald tests using asymptotic Chi-square statistical
inference. The FMOLS estimator not only generates
consistent estimates of the β parameters in relatively small
samples, but it controls for the likely endogeneity of the
regressors and serial correlation. Formally, the FMOLS
estimator for the i-th panel member is given by
βi* = ( Xi’Xi)-1(Xi’yi* - Tδ)
where y* is the transformed endogenous variable, δ is a
parameter for autocorrelation adjustment, and T is the
number of time periods. The Autoregressive Conditional
Heteroskedascity Mean (ARCH-M) model is used in
estimating inflation volatility.
RESULTS AND DISCUSSION
This section discusses the results of the study.
Relationship between Inflation and Inflation Volatility
The GARCH (1, 1) Model has been used in measuring
inflation volatility. Before ARCH model can be used, we
need to estimate the mean equation as indicated in
Appendix 1a. It can be seen that DR and TB rate
significantly influence inflation at the 5% significance level.
The residual of inflation in Figure 1 helps to determine if
the ARCH model can be used. It can be seen from Figure 1
that there is prolonged period of low volatility from 1990 to
1992 and also there exists a prolonged period of high
volatility from 1993 to 1995. In other words, periods of
high volatility are followed by periods of high volatility and
periods of low volatility tend to be followed by periods of
low volatility. This suggests that the residual or error term is
conditionally heteroskedastic and can be represented by
ARCH model.
Residual derived from mean equation is used in
making variance equation in Appendix 1a. The
@SGRT(GARCH) in the mean equation is the standard
deviation of GARCH. The @SGRT(GARCH) is significant
at the 5% significance level and has a positive coefficient.
Whenever @SGRT(GARCH) is significant and positive it
means CPI is a very risky variable. From the variance
equation it can be seen that GARCH(-1) significantly
influences inflation volatility at the 5% significance level.
This means that previous year residual variance or volatility
of inflation influences current period volatility of inflation.
The RESID (-1)^2 which is previous year’s squared
residual or previous year’s inflation information about
volatility also known as ARCH significantly influences
current period inflation volatility. It means that inflation
volatility is influenced by its own ARCH and GARCH
factor or shock. This means inflation volatility are internal
causes or shocks caused by own family.
60
50
40
30
20
15
10
10
0
5
0
-5
-10
-15
1992
1994
1996
1998
Residual
2000
2002
Actual
2004
2006
2008
Fitted
Figure 1: Residuals of Inflation for 21 years
This suggests that indeed the past values of inflation
are sufficient to determine the current value of inflation. In
other words, when given the value of inflation at time t-1,
as well as the disturbance period at that same time, volatility
in inflation can be determined. The relationship is such
that, there exists a positive relationship between inflation at
time, t and inflation at time, t-1. Hence, the model suggests
that inflation at time, t would consistently be higher than
previous inflation levels for the industry since an increase in
inflation at time, t-1 points to an increase at time, t as well.
The results confirm the findings of Cukierman and Maltzer
(1986), Kontonikas (2004), Jdaitawi and Alwan (2014), and
Hossain (2014).
The Heteroskedasticity test in Appendix 1b tests the
presence of arch effect. The test is significant, meaning that
no arch effect. This means that ARCH model can be run.
This model was chosen because of its lowest Akaike
Information Criterion value.
Relationship between Inflation and Inflation Volatility
and Credit Risk
The @SGRT(GARCH) is significant at the 5%
significance level and has a positive coefficient. Whenever
@SGRT(GARCH) is significant and positive it means CPI
is a very risky variable. As the variance equation shows,
previous year residual variance or volatility of inflation
influences current period volatility of inflation and also
previous year’s inflation information about volatility
significantly influences current period inflation volatility.
The regression analysis suggests that CPI influences CR1
and the relationship is negative. It is also established that
previous period volatility of inflation influences current
period inflation. It can therefore be concluded that CR is
influenced by the volatility of inflation significantly. This
confirms the results of the work done by Rother (2004),
and Nor (2009) that inflation uncertainty has significant
effect on credit markets. The descriptive data analysis
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
confirms that there is a negative relationship between
inflation and inflation volatility on one hand and CR
measured by LLP/bank asset ratio especially between 1990
and 2001.
Relationship between Inflation and Credit Risk
Analysing the results of the regression methodology, a
significant relationship is found between inflation (CPI) and
CR1 but not with CR2 and this relationship is negative
suggesting that as inflation increases CR1 reduces and vice
versa. CR may reduce because assets reduce in real value
during times of high inflation making loan repayment much
easier. Bank customers enjoy debt relief as increase in
inflation reduces the real level of debt. This confirms the
result of the works of Al-Smadi and Ahmed (2009) who
indicated that a high inflation rate leads to decrease in CR
and Hadjimichalakis and Hadjimichalakis (1995) who
observed that when inflation rises borrowers repay their
debt in inflation-cheapened dollars. The implication of this
is that as inflation increases the ratio of LLP to bank assets
reduces suggesting an improvement in bank loan
performance. The negative relationship between CPI and
CR1 was conspicuous between 1992 and 1999 as shown in
Figure 2. Using the descriptive data analysis, we can
conclude that the period 1990 to 2001 was a period of very
high and volatile inflation compared to the period after it.
The relationship as revealed by the graph indicates a
positive relationship between CR 2 and inflation, suggesting
that banks earn more NII relative to assets during periods
of high and volatile inflation than periods of low and stable
inflation. From the foregoing analysis, it can be concluded
that increases in inflation rate are good for banks in terms
of credit performance.
0.8
Inflation
0.6
0.4
LLP/Total
Assets
0.2
2008
2005
2002
1999
1996
1993
1990
0
NII/Total
Assets
Figure 2: Relationship between inflation and credit risk
Again according Boyd (2001), countries with high
inflation have underdeveloped financial systems and banks
and so in this research the period 1990 to 2001 in Ghana
can be described as a period of a relatively less developed
banking industry. According to the survey the impact of
inflation on the business lives of the respondents was that
inflation affects staff cost and operating cost more than it
affects the interest paid on bank loans. This suggests that
an increase in the rate of inflation, all things being equal,
will push the operating cost of businesses up more than it
affects cost of loans. On the effect of inflation on loan
repayment, it was the opinion of majority of respondents
that the default that results is due to the general increase in
the cost of living due to inflation and increase in cost of
production due to increase in inflation. Again, the impact of
increase in the cost of living due to inflation and increase in
the cost of production due to inflation as the main causes
of loan default was emphasised by majority of respondents.
The assertions above are supported by the fact that most
loans are fixed interest rate loans and not variable interest
rate loans. Interest payment on existing loans hardly
changes even if there is a general increase in interest rates.
Policy makers' reliance on inflation targeting in
managing the economy might not yield the desired results
for banks as the negative relationship between inflation and
credit risk implies the lowering of inflation will lead to an
increase in LLP as a ratio of Total assets. Discount rates are
normally adjusted upwards anytime there is an increase in
inflation and therefore borrowing from the central bank
becomes more expensive. Commercial banks therefore
have to transfer this high DR to their customers in the
form of higher lending rates. Because of increase in the cost
of production during times of high inflation loan default
rates are supposed to be high but the inverse is the result
portrayed by this research because as discussed above, loans
become cheaper. It also means that banks are more careful
with their lending in times of high inflation than in times of
low inflation. This could be the case because in times of
high inflation other products and services compete with
bank loans in revenue generation. For instance during times
of high inflation, interest rates on TBs also rise providing
safer and compensatory means for revenue generation for
banks and banks may not be reckless in their loan
processing activities. Government TB rates generally
increase in times of high inflation and making them more
attractive to banks than loans. Administratively, investing in
and managing TBs are easier and less costly than managing
customer loans, therefore if TB rates are high then loans
can only be granted at a much higher premium otherwise
banks would prefer investing a chunk of the surplus funds
in TBs. Moreover, banks in Ghana have a large percentage
of customer deposit on which interest is not paid or even if
paid, is at a very low rate. These deposits are demand
deposits and the percentage of demand deposits to total
deposits is quite high averaging about fifty percent of total
deposits. With high amount of interest-free deposits, banks
can afford to invest in low-yielding assets and still make
good returns.
The problem with inflation targeting in most
developing countries is that, inflation is to be brought down
by increasing the interest rate through increases in the
policy rate or DR. This strategy assumes that there is excess
money supply in the economy and hence there is the need
to reduce this excess. The aim is to reduce the demand for
goods and services. Unfortunately, according to Bawumia
(2008) inflation in developing economies is not as a result
of excess demand but rather high cost of production. The
effect of these is more on the supply side than the demand
side thus any increase in prices of these items are bound to
cause increase in domestic inflation. Increases in the policy
rate with the aim of reducing the money supply and then
bring down inflation is not effective in such an
environment as high interest rates will continue to raise the
cost of borrowing in the country and increase the cost of
production and reduce the amount of goods supplied and
further raising the cost goods and services.
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
0.8
0.6
Inflation
0.4
0.2
2008
2005
2002
1999
1996
1993
1990
0
Interest
Rate Spread
Figure 3: Relationship between Inflation and IRS
Relationship between Inflation and Bank Performance
Inflation does not have any statistically significant
relationship with the TC, however, the descriptive data
analysis in Figure 4, points to a lowering of the TC during
periods of high and volatile inflation (i.e., between 1990 and
2000) and an increase in the TC during periods of relatively
lower and stable inflation. This suggests that banks either
incur lower cost relative to income or higher income
relative to cost during times of high and volatile inflation
than in times of relatively lower and stable inflation. Again
based on this observation we can conclude that high and
volatile inflationary periods are the best periods for banks
confirming the result arrived at when analysing CR.
The CPI (Appendix 6) also does not have a significant
relationship with the solvency index meaning the index
does not change with changes in inflation rates. The
descriptive data analysis as shown in Figure 4, however,
indicates generally a positive relationship between inflation
and the solvency index. However, some peak periods of
inflation are matched by low levels of the solvency index
confirming an earlier observation made under credit risk
that loan repayments improve during times of high
inflation.
CPI according to the regression analysis in Appendix 7
does not influence ROA significantly, but again, the
descriptive data analysis in Figure 4 suggests that ROA
increased during the time inflation was high and volatile
and declined as inflation dropped and became more stable.
Again this result supports the findings under CR in this
research that banks perform better in periods of high and
volatile inflation than periods of low and stable inflation.
0.8
Inflation
0.6
0.4
0.2
ROA
0
1990
1993
1996
1999
2002
2005
2008
Relationship between Inflation and Interest Rate
Spread
The regression analysis, in Appendix 4, establishes a
significant positive relationship between inflation and IRS.
According to Hadjimichalakis and Hadjimichalakis (1995)
during times of high inflation, because debts become cheap,
borrowers are willing to pay higher interest rates. That
lenders also demand a higher interest rate to compensate
them for the expected loss of purchasing power from
inflation. In the 1990s, when inflation was very high and
volatile, as shown in Figure 3, both lending and borrowing
rates were rising but the IRS was smaller. This changed
after 2000 when both lending and borrowing rates began to
decline with the spread becoming wider. This means that
during times of rising inflation, banks borrow at very high
rates but are not able to adjust the lending rates quickly
upwards, but in times of declining inflation borrowing rates
are adjusted downwards much more quickly than the
lending rate. This confirms the fact that bank deposits have
shorter maturity periods than bank loans. Again in times of
high inflation, customers are likely to increase their
borrowing as a result of increase in overhead cost, leading
to shortage of funds and making loans expensive. Lenders
of funds to the banks who are savers will demand a higher
return on their savings because of uncertainty and the
general increase in the price of goods and services. It is also
noted that between 1990 and 2000, when the economy was
much more characterised by high and volatile inflation,
most of the local banks were state owned and so
Government's control of lending and borrowing rates was a
dominant factor in determining the rates. The positive
relationship between inflation and IRS based on the
regression analysis, however, suggests that reducing
inflation will lead to a reduction in the IRS. The lower IRS
experienced between 1990 and 2000 also confirms the
interference from Government in setting the borrowing
and lending rates.
Figure 4: Relationship between Inflation and Bank
Performance
The attractiveness of the TB investment is confirmed
by the fact that about half of the 356 respondents who had
investment in TB had over 60 percent of all their funds
invested in it. Concerning the level of lending rates in
Ghana, majority of respondents claimed both the bank
lending and bank borrowing rates were too high. When
asked to state and rank what they thought were the main
cause(s) of high interest rates in Ghana, respondents
attributed it to high inflation rates, central bank's restrictive
policy, high credit (default) risk and the general
macroeconomic uncertainty in that order. For reasons
ranked second, we have high CR, high inflation rate, central
bank's restrictive policy and macroeconomic instability and
uncertainty. Inflation is therefore considered as the main
reason for the high interest rates in Ghana. Other results of
the regression analysis are as follows:
Firstly, three variables namely DR, FSD 1 defined as
the ratio of money supply to GDP and FSD 2 defined as
the ratio of total asset to GDP) significantly influence CR 1
defined as the ratio of loan loss provision to total asset at
5% significance level. All these three variables have negative
relationship with CR1 except the DR which is positive.
Also four variables namely Competition, DR, FSD1 and
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
http://ijhass.org
FSD2 significantly influence CR 2, defined as the ratio of
NII to total asset at the 5% significance level and the
relationship is positive for all. Secondly, four variables
namely FSD1, ME, TB rate and RR significantly influence
IRS at the 5% significance level. Here too the relationship
is positive for all. Thirdly, five variables namely ME,
Competition, Ownership, GDPPC and DR significantly
influence SI at the 5% significance level. The coefficients of
the DR and the GDPPC are negative while the other three
have positive coefficients. The TC significantly influenced
at 5% significance level by six variables, namely FSD1,
FSD2, RR, the TB, DR and competition. Three of the
variables: FSD2, RR and TB rate have positive coefficients
while the rest have negative coefficients. Finally, three
variables namely FSD2, DR and GDPPC significantly
influence ROA, measured as the ratio of Profit Before Tax
(PBT) to total assets at the 5% significance level with only
the GDPPC having a negative coefficient with ROA while
the others have positive coefficients.
CONCLUSIONS
While there have been several contributions to the
literature on the impact of the volatility of the macro
economy on inflation, and also the impact of inflation on
credit risk, IRS and bank performance empirical studies
have been at variance on the results. This research
attempted to address these shortcomings by extending the
scope of the methodology to embrace three scenariosquestionnaire data analysis, descriptive data analysis and
regression analysis, instead of limiting it to only the
regression analysis which has always been the case. There
are specific theoretical and policy implications falling from
the results of this study. There are implications for the
banks, the industry and the economy in general. Even
though this work cannot be considered as having
completely filled existing gap in the subject of study, it has
added to existing knowledge. This section discusses these
implications.
Implications for Theory
The findings revealed that inflation influences bank CR
exposure, IRS determination and BP in various ways.
Concerning the relationship between inflation and inflation
volatility it has been established through this research that
periods of high volatility are followed by periods of high
volatility and periods of low volatility tend to be followed
by periods of low volatility. Also past values of inflation are
sufficient to determine current value of inflation and
previous year's inflation information about volatility
significantly influences current period inflation volatility. It
is also established that CPI has a negative relationship with
CR; that is as CPI increases, CR measured by LLP/total
bank assets ratio falls, however as indicated by the
descriptive data analysis, the NII/total bank asset ratio
increases. These results confirm that banks are better off
during times of high and volatile inflation. However, an
increase in inflation is likely to result in an increase in
operating cost of bank customers than increase in the cost
of bank loans leading to a loss in the value of bank assets.
Implications for Practice and Policy
There are several implications for practice and policy
that can be gleaned from the results of the research.
Evidence suggests that there is a strong chance that a year
of high volatility will be followed by another year of high
volatility. Therefore as suggested by Jdaitawi and Alwan
(2014), Hossain (2014) and Dagha (2007) which confirmed
the earlier works of Kontonikas (2004) and Cukierman and
Meltzer (1986) that there is a positive relationship between
past inflation and uncertainty about future inflation, we may
estimate volatility conditionally to the observation of
previous years. The policy implication for high inflation
countries is to aim at lowering the average inflation rates in
order to reduce the negative consequences of uncertainty
like high cost of production for firms and a reduction in the
real value of bank loans which in the long run make banks
worse-off. Lowering and stabilising inflation is also good
for both the bank and the investor in the sense that interest
rate fluctuations are curtailed and therefore making
forecasting and planning easier. In a volatile macro
economy, if anticipated changes do not take place, or
changes take place in opposite direction, banks can
experience heavy losses.
Also during times of rising inflation, investors must
avoid interest-sensitive securities and long-term debt
instruments. On the other hand in periods of declining
inflation, long-term debt obligations are typically sound
investments. Lenders may not be willing to lend money for
long periods if the purchasing power of that money will fall
below the original value at the time of repayment. To
minimise this problem, banks resort to high IRS
(demanding inflationary premium) and short term lending
which might not be suitable for projects with long term
gestation periods.
If inflation increases causing policy makers to increase
the DR and also the TB rate as has always been the case,
immediate benefits may seem to accrue to the banks and
their customers. However, if high inflation persists it may
turn out to be a disadvantage to both banks and their
customers as real value of assets continue to fall. An
increase in inflation leads to a fall in LLP as ratio of bank
assets (i.e., a lower CR) and increase in IRS. However, the
improved performance caused by an increase in inflation is
due to the fact that bank loans have become cheaper and
therefore banks receive lower real value for their loans. An
increase in IRS could mean a very low deposit interest rate
which may discourage savings and a high lending rate which
may also discourage bank lending. A situation of this nature
does not auger well for the development of the financial
sector. Policy makers should therefore adopt policies that
will ensure narrowing of the IRS to promote financial
intermediation. A policy recommendation arising from this
analysis is that achieving effective monetary and fiscal
adjustments may be necessary conditions for deepening and
increasing the efficiency of the banking industry.
The macro economy of developing economies is very
volatile and significantly influences CR and IRS. This
suggests that the banking environment is quite risky and
therefore banks should make the necessary effort to adopt
strong risk management frameworks, systems and processes
to ensure regulatory compliance, operational excellence and
optimal risk management.
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IUG Journal of Humanities and Social Sciences (IJHASS), vol. 1, no. 1, 2016
Research and IT, Islamic University College, Ghana
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Biography
Dr. David Kwashie Garr holds a PhD
in Business Administration from Open
University, Malaysia, Master of
Business Administration (Finance) and
a Bachelor of Arts Degree in
Economics all from University of
Ghana, Legon. He worked in the
banking and microfinance industries
for a total of 28 years engaging mostly in banking
operations. He has been a facilitator in Corporate Finance,
Investment Analysis and Banking courses at Accra Institute
of Technology for five years at the post graduate level and
also a lecturer at Islamic University College, Ghana since
2013 in Bank Management, Banking Operations and Ethics
and Banking Law and Practice at the undergraduate level.
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