cointegration_of_loans-libre.pdf

AFRICA DEVELOPMENT AND RESOURCES RESEARCH INSTITUTE (ADRRI) JOURNAL

ADRRI JOURNAL (www.adrri.org)

pISSN: 2343-6662 ISSN-L: 2343-6662 VOL. 7,No.7(2), pp 52-66, April, 2014

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Cointegration Analysis of Customer Deposits and Bank Loans and Advances by the

Ghanaian Listed Banks.

Victor Curtis Lartey1 and Godfred Kwame Abledu2

1Faculty of Business and Management Studies, Koforidua Polytechnic 2Faculty of Applied Sciences and Technology, Koforidua Polytechnic

1Correspondence: Email: [email protected] Tel: +233208441868

Received: 5th April, 2014 Revised: 26th April, 2014 Published Online: 30th April, 2014

URL: http://www.journals.adrri.org/

[Cite as: Lartey, V. C. and Abledu, G. K. (2014). Cointegration Analysis of Customer Deposits and Bank

Loans and Advances by the Ghanaian Listed Banks. Africa Development and Resources Research

Institute Journal, Ghana: Vol. 7, No. 7(2), Pp. 52-66.]

Abstract

The objective of this paper was to model the long term relationship between loans and deposits;

therefore, a cointegration analysis was the ideal tool. The population of this study was made up of all

commercial banks listed on the Ghana Stock Exchange. These included CAL Bank Limited, Ecobank

Ghana Limited, Ecobank Transnational Incorporated, Ghana Commercial Bank Ltd., HFC Bank Ltd, SG-

SSB Ltd., Standard Chartered Bank Ltd., Trust Bank Ltd. and UT Bank Limited. In this study, purposive

sampling was used to select seven (7) out of the nine (9) banks listed on the Ghana Stock Exchange. The

results of both the Dickey-Fuller (DF) and the augmented Dickey-Fuiler (ADE) unit root tests of the null

hypothesis of nonstationarity of loans and deposits tested against the alternative hypothesis of

stationarity indicate that both loans and deposits are not stationary in their levels. The findings also

suggest that a positive relationship exists between deposits and loan. This confirms Obamuyi (2013) who

also found positive relationship between deposit mobilization and bank lending.

Keywords: customer deposits, bank loans, stationarity, cointegration analysis, VECM and panel data

analysis, unit root tests




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INTRODUCTION

It is believed that how speedily and cheaply a financial system is able to channel funds from

surplus economic units to deficit units for productive investments, while ensuring

reasonable returns for the financial intermediaries, tells the efficiency of such financial system

(Obamuyi, 2013). Financial sector of an economy does matter in economic development (Shaw

1973); although Obamuyi (2012) observed that the financial systems of most developing

countries lack the sophistication required for economic growth.

Commercial banks are considered to remain dominant in the financial system of today in terms

of their shares of total assets and deposit liabilities. Their total loans and advances, a major

component of total credits to the private sector, are still on the increase in spite of the

major constraints posted by the government regulations, institutional constraints and other

macro economic factors (Olokoyo, 2011; Olumuyiwa et al, 2012). Haron and Azmi (2006)

believe that, most business organizations, especially in developing countries mainly depend

on bank loans as a source of capital and the ability of banks to give out loans depends much

on their ability to attract deposits. Freixas and Rochet (2008), as cited in Fouopi-Djiogap and

Ngomsi (2012), observed that bank loans are one of the most important long-term financing

sources in many countries today.

LITERATURE REVIEW

Diamond and Rajan, (1998) observed that Banks transform liquid assets like deposits into

illiquid assets like loans. This transformational process of banks’ activity is influenced by some factors such as macroeconomic, bank level (Peek and Rosengreen, 1995) and industry

level characteristics (Boot and Thakor, 2000). According to Khalily, Meyer and Hushak (1987)

there are major factors which influence deposit functions of a bank. These include income,

interest rates, access to banking facilities, transaction costs, yields on alternate investments,

the quality of services provided to depositors, the awareness of banking services by the

public and perceptions of the safety of depositors. Other factors identified include the level of

income, customers satisfaction, service quality and demographic factors such as number of

dependants and location (Dadzie, Winston and Afriyie 2003) as cited in ( Haron and Azmi

2006); and (Obamuyi 2013). According to Boot and Thakor (2000), the level of banking industry

competition greatly influences bank lending strategy positively. The size of a bank is also

considered as an important determinant of bank lending decision (Berger and Udell, 2006,

Uchida et al. 2007). Large and complex banks tend to lend few loans to small scale firms (Berger




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and Udell 2006). Small banks have comparative advantages in producing soft information

whereas large banks also have comparative advantages in lending based on hard information

Stein (Stein 2000).

Ladime et al, (2013) also observed a relationship between bank lending behaviour and a set

of macroeconomic indicators, industry and bank level characteristics. They concluded that

bigger banks seem to be in a better position to lend more than otherwise. This might be due to

enough resources they have to cushion lending. Similarly, high level of bank capital is found to

support much higher volumes of bank lending. In addition to the above, the macroeconomic

environment in which a bank operates matter for its lending decision. For instance, in the

period of economic boom, businesses demand for loans to take advantage of expansion and

banks investment opportunities equally rise. However, in periods of economic recession,

demand for credit falls. This provides a pro-cyclical relationship between economic growth

and bank lending 〉Dell’“riccia and Marquez, ｲｰｰ6╉ Ladime, ｲｰｱｳ《. Deposits play a pivotal role in bank funding, as a major portion of a commercial bank’s assets is usually financed through customer deposits (Bologna 2011).

The deposit taking and lending activities of banks decide to a very large extent, the profitability

of banks. This is because banks generate their income from the interest differentials from what

they pay for deposit and what they charge for their loans and advances. On the other hand,

and as Vohra and Sehgal (2012) argued, lending is one of the two principal functions of

banks, not only because of their social obligation to cater to the credit needs of different

sections of the community, but also because lending is the most profitable, for the interest

rates realized on loans have always been well above those realized on investments.

Obamuyi (2013) examined the extent to which banks in Nigeria have performed their

intermediation functions of deposit mobilization and granting of loans and advances and the

effects on their performance. The banks were found to have performed creditably well in

deposit mobilisation, as well as in granting of loan and advances, despite various socio-

cultural and institutional problems inhibiting financial sector development in Nigeria. They

concluded that the banks that mobilized huge amount of deposits and granted more loans

and advances were found to have higher profits than the others. This shows the positive

relationship between deposit mobilization and bank lending. According to Obamuyi (2013), ｠as Jayaratne and Morgan (1997) posit, lending and deposits move together because faster




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deposit growth signals growing demand for loans. This confirms that banks generate their

incomes through the lending and investment activitiesを.

The purpose of this study is to analyse the trend in customer deposits taken by the listed banks

in Ghana, and the loans and advances they have given out. It is also meant to describe the

relationship between the deposits taken and the loans and advances given to their customers

within 2005-2012.

METHODOLOGY

Research Design

The study is descriptive in nature. In descriptive research, a researcher begins with a well-

defined subject and conducts a study to describe it accurately and the outcome is a detailed

picture of the subject (Neuman, 2007). The study adopts the longitudinal time dimension,

specifically the panel study type. Panel study is a powerful type of longitudinal research in

which the researcher observes exactly the same people, group, or organisation across multiple

time points. This study seeks to describe the relationship between the deposits taken by the

listed banks and the loan and advances given by them within the period 2005-2012.

The population of this study was made up of all commercial banks listed on the Ghana Stock

Exchange. These included CAL Bank Limited, Ecobank Ghana Limited, Ecobank Transnational

Incorporated, Ghana Commercial Bank Ltd., HFC Bank Ltd, SG-SSB Ltd., Standard Chartered

Bank Ltd., Trust Bank Ltd. and UT Bank Limited. In this study, purposive sampling was used to

select seven (7) out of the nine (9) banks listed on the Ghana Stock Exchange. The two banks

excluded were Ecobank Transnational Incorporated and Trust Bank Ltd. These banks were

excluded from the study because their financial statements were reported in currencies other

than the Ghana Cedis. Ecobank Transnational Incorporated reported in US Dollars while Trust

Bank Ltd reported in Gambian Dalasi. Including the above two banks in the research would

distort the analyses and comparison.

Instrumentation and Data Collection

Data was mainly collected from secondary sources. Data emanated from listed banks’ financial reports, published and unpublished books, scholarly journals, business and financial news

papers and other magazines and corporate journals. As the study needs historical financial data,

which are from corporate reports, accessing publicly available data is assumed as the suitable




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method for the accuracy of the data. As public data is accessible to everyone; the study made

use of the financial performance data which were of interest to the present research. Financial

reports and other relevant information of the listed banks for the period 2005-2012 were

retrieved from the internet, by search engines.

RESULTS AND DISCUSSIONS

The study utilizes annual data on loans and deposits of 7 listed banks in Ghana within period

2005 through to 2012. Since the objective of interest was to model a long term relationship

between loans and deposit, a cointegration analysis was the ideal tool. Three step procedures

were used to examine the cointegration of deposit and loan.

First, we test for linear cointegration. The reason is that linear cointegration is the easiest case to

analyze and most cointegration tests are built on the basis of linear regressions. In this study,

two popular cointegration tests were employed the augmented Engle-Granger (AEG, Engle and

Granger 1987) and Johansen tests (Johansen 1988, 1991).

Second, if linear cointegration is not found, we then go on to test for nonlinear cointegration.

The method adopted here is a two-step testing procedure that was initially proposed by

Granger and Hallman (1991) and Granger (1991). According to this procedure, we first used an

algorithm-the ACE-to transform the nonlinear Relationship into a linear form and then apply

the conventional cointegration tests to the transformed (and also linearized) relationship. As

Granger and Hallman (1991) and Granger (1991) argued, linear cointegration for the

transformed relationship can be characterized as nonlinear cointegration for the original

relationship. Finally, if nonlinear cointegration is not found, we conclude that loans and

deposits are not cointegrated. However, an important caveat must be issued here.

We used the Johansen (1991) procedure since it has been shown to have good finite sample

properties. The Johansen (1991) procedure is based on a vector error correction model (VECM)

to test for at least one long run relationship between the variables. For the VECM we first

determine the order of integration of the variables, making use of Augmented Dickey-Fuller

and the Phillips-Perron tests, and then applied the Johansen procedure. In conducting

cointegration tests, the time series are required to be nonstationary in their levels. Moreover, it

is important that all time series in the cointegrating equation have the same order of integration.




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Consequently, the study first ascertained the time series properties of loans and deposits by

employing both the DF and ADF tests for stationarity. The equation estimated for the ADF test

takes the form: m

t 0 1 t-1 i t-1 ti=1

ΔX =α +β X + ht+ θ ΔX +D itÕ å (1)

where et is a sequence of zero-mean p-dimensional white noise vectors, D is the difference

operator , m is the maximum lag length, and Dt seasonal dummies. The term Xt includes our

variables and is a p x 1 vector. The parameters are the p x p matrix G and Π denotes a p x p

matrix that contains the information about the rank and hence the long term relationship among

the variables. There are three possible cases to be considered╈ Rank 〉Π《 = p and therefore vector

Xt is stationary╉ Rank 〉Π《 = ｰ implying absence of any stationary long run relationship among

the variables of Xt or Rank 〉Π《 < p and therefore r determines the number of cointegrating

relationships.

The equation has an error correction representation where Π =aß. The columns of matrix ｠aを are

called adjustment (or loading) factors and the rows of matrix ｠ßを are the cointegrating vectors

with ßxt being stationary even if Xt consists individually of I(1) processes. Johansen developed

two different tests of the hypothesis that there are at most r cointegrating vectors; the trace

statistic which tests the null hypothesis of at most r cointegrating relationships against a general

alternative in a likelihood ratio framework and the maximum eigenvalue statistic which tests

the hypothesis of r cointegrating relationships against the defined alternative of r+1

cointegrating relationships. The existence of at least one cointegrating vector in the system

indicates the presence of causality between loans and savings.

The causal relationship between loans and deposits is explored with Granger-causality test

based on VECM. This procedure is particularly attractive over the standard VAR because it

permits temporary causality to emanate from (1) the sum of the lagged coefficients of the

explanatory differenced variables and (2) the coefficient of the lagged error-correction term. In

addition, the VECM allows causality to emerge even if the lagged differences of the explanatory

variables are not jointly significant. It must be pointed out that the standard Granger-causality

test omits the additional channel of influence (zt-1). In this study, the causality tests are based on

the following VECM:




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a

t 1 i t-1 i t-1i=1 j=1

ΔX =αz + β ΔX ΔY + Db

t t tf m- + Õ +å å (2)

t 1 i t-1 i t-1i=1 i=1

ΔY = z + ΔY ΔY + Dc d

t t tj q l e- + Õ +å å (3)

where, zt-1 represents the error correction term lagged by one period, X is the gross customer

deposit, Y stands for gross bank loan, a, b,c, and d represent the optimal lag lengths obtained

from the Akaike Information Criterion (AIC).

As a preliminary step of the Cointegration analysis, we need to test for the order of integration

of the data for deposits and loans. Two popular unit-root procedures that we used are the

augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The results suggest that the

series for each data are 1(1). That is, there is a unit root in the level of the series but none in the

first-difference series. We now proceed to test for linear cointegration of bank deposits and

customer loans. We made use of two cointegration methods-the Augmented Engle-Granger

(AEG, Engle and Granger 1987) and Johansen tests (Johansen 1988, 1991). Both tests are widely

used in empirical research. The AEG method is based on assessing whether there is a unit root

in the residuals from a single-equation regression involving the variables that are potentially

cointegrated. It is suitable for this study because customer deposits and bank loans constitute a

single equation regression). The Johansen test, on the other hand, is a system-based test and

enjoys greater power than the AEG Test as it incorporates system dynamics. It is developed to

test for the cointegrating rank (r) of a system written in a Vector Autoregressive (VAR) form.

The cointegrating rank (r), as the name implies, is the number of independent cointegrating

relationships among the variables that constitute the system. The null hypothesis of the

Johansen test is 0 0:H r r or 0r r where 0r is an integer.

Based on the formulation of the null hypothesis, two types of test statistics, trace and max , are

calculated. For both test statistics, if their computed values are greater than their respective 5%

critical values, the null hypothesis is rejected. The cointegrating rank (r) is determined when the

null hypothesis cannot be rejected for the first time as 0r runs through the integer sequence of 0,

1,2.




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Table 1: Augmented Engle-Granger (AEG) test for linear cointegration.

Bank Test Statistics

ECO -1.634

GCB -3.735

CAL -1.634

HFC -1.432

SG-SSB -2.639

SCB -2.335

UTB -3.237

CAL -1.831

ECO -1.276

This table reports the test statistics from the augmented Engle-Granger (AEG) test. The

AEG test first runs a regression of customer deposits on bank loans and then conducts a

unit-root test and the augmented Dickey-Fuller (ADF) test on the residuals. If the ADF

test statistics is greater (in absolute value) than the conventional-level critical values, the

null hypothesis of no cointegration would be rejected. The critical values provided by

Engle and Yoo (1987) are: -4.80 (1%), -4.19 (5%) and -3.88 (10%). The lags used in the

ADF test are determined by the Akaike Information Criterion (AIC).

In equation (2) the rejection of the null hypothesis that gross saving does not Granger-

cause loan requires that the Xj's conjointly be statistically significant and/or (ii) the error

correction term (zt-1) be statistically significant. Similarly, in equation (3) the null

hypothesis that gross customer deposit does not cause gross bank loan is rejected

provided that the Xj's are jointly statistically significant and/or the error-correction term

(zt-1) is significant.

To compare the forecasting power of VAR model and error correction model, the root

mean square error (RMSE) is used The RMSE of the out-of sample forecast for vector




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error correction model was 0.025 compared to 0.036 for the VAR model. This represents

a 44% reduction in the forecast errors when the error correction model is used.

Therefore, it appears that the error correction model which takes into account the long-

term relationship outperforms the vector auto regression model.

Empirical Results

The data used in this study comprise Customer Deposits and Bank Loans by the

Ghanaian Listed Banks between 2005 and 2012. The results of both the Dickey-Fuller

(DF) and the augmented Dickey-Fuller (ADE) unit root tests are presented in Table 1.

The null hypothesis of nonstationarity between customer deposits and bank loans is

tested against the alternative hypothesis of stationarity. The results indicate that both

customer deposits and bank loans are not stationary in their levels. However, after first

differencing, the null hypothesis of no unit root is rejected in all of the cases. In all, the

results indicate one order of integration [1(1)] for deposit and loans. The nonstationarity

of the time series in their levels calls for the application of cointegration procedure to

avoid the problem of spurious regression. The critical values at the 5% level of

significance are -2.96 and -3.57, respectively for without trend and with trend. The 10%

critical values for without trend and with trend are -2.26 and -3.20, respectively. The lag

orders are determined by Akaike Information Criterion (AIC).

.

Table 2: Dickey-Fuller (DF) and the augmented Dickey-Fuller (ADE) unit root tests

Series

DF ADF Lag

Order Level Difference Level Difference

GDS

Tm -1.82 7.70** 1.64 -3.13** 2

TT -1.83 -8.63** -1.54 3.85** 2

GDI

Tm -1.78 6.98** 2.17 -3.62** 2

TT -1.81 5.73** -1.76 -5.33** 2

* Significant at 10% level, ** significant at 5% level, Tm = without trend. DF Dickey-Fuller statistic, ADF

= augmented Dickey-Fuller statistic, TT = with trend. GDS = Ratio of gross domestic deposits to GDP

and GDI = ratio of gross domestic loans to GDP.




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Having determined the order of integration, the Johansen procedure was applied to

ascertain whether customer deposits and bank loans are cointegrated. The results of the

Johansen cointegration tests are presented in Table 3. The null hypothesis of no

cointegration between customer deposits and bank loans (i.e. r = 0) is rejected by both

the trace and maximal eigenvalue ( maxl ) tests at the 5 percent significance level in all of

the cases. The fact that customer deposits and bank loans are found to be cointegrated

suggests that capital is immobile on the stock exchange market relative to the sample

data. The critical values for the trace test hypotheses 1r£ and 0r £ are 11.54 and 18.33

respectively. The critical values for the maxl test hypotheses 1r£ and 0r £ are 11.54 and

23.83 respectively.

Table 3: Johansen Cointegration Test Results

Null: r = 0 Null: 1r £

Trace maxl Trace maxl

26.86** 21.66** 5.20 5.20

** indicates the rejection of the null hypothesis at the 5% significance level

The rank test results in this study are summarized in Table 4. For the case of the rank

test, we computed the autocorrelation adjusted test statistics. The null hypothesis of

this rank test is that the customer deposits and bank loans are not cointegrated, which is

in contrast to the alternative hypothesis that states that the two variables are

cointegrated. The null hypothesis is rejected in favor of the alternative hypothesis when

the critical value exceeds the test statistic; otherwise, the null hypothesis is supported.

As is shown by the * statistic in Table 4, the null hypothesis is rejected for all seven of

the listed banks examined in this study because the test statistics are larger than the

conventional critical values at the 1 percent significance level. According to the *

statistic, we observed cointegrating relationships between the customer deposits and

bank loans for all all seven of the listed banks. Therefore, this indicates that the rank test

employed in this study provides some evidence of the existence of long-run

relationships between the all seven of the listed banks examined. Based on the

cointegrational relationships previously identified above, it is possible to distinguish

between non-linear and linear cointegration using the rank sum linearity test developed

by Breitung (2001). It is evident from Table 4 that the null hypothesis of linear




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cointegration is rejected at all conventional levels; thus, the rank sum linearity test

results for the TR2 also indicate that the cointegrating relationships can be non-linear.

Table 4: Results of the Cointegration and Non-Linearity Rank Tests

Bank Rank Test ( * ) Linearity Test(T.R2)

CAL 0.02718 *** 15.62711 ***

ECO 0.03412 6.78413 **

GCB 0.04313 *** 7.09212 **

HFC 0.07414 *** 3.03814 ***

SG-SSB 0.08211 *** 12.00316 ***

SCB 0.09714 *** 8.20518 **

UTB 0.07318 *** 2.90612 *

*** indicates significance at the 0.01 level; ** indicates significance at the 0.05 level; and * indicates

significance at the 0.1 level.

The results for the error-correction based Granger-causality tests are presented in Ta ble 5.

These tests are conducted with residuals from the cointegration equations. The results indicate

that causality runs from customer deposits and bank loans. The null hypothesis that customer

deposit does not Granger- cause loan is rejected because the error-correction terms are

statistically significant. This finding implicates the error-correction terms as the only channel of

influence since the lagged differences of deposit are not conjointly significant.




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Table 5: F-Statistics for Bivariate Causality Tests Based on VECM

Deposit and Loan F-Statistics

Deposit Equation

1tz - 9.13*

DDå 2.86

Loan Equation

1tz - 0.07

SDå 0.43

* indicates significance at the 0.1 level

CONCLUSION

The results of both the Dickey-Fuller (DF) and the augmented Dickey-Fuiler (ADE) unit root

tests of the null hypothesis of nonstationarity of loans and deposits tested against the alternative

hypothesis of stationarity indicate that both loans and deposits are not stationary in their levels.

The findings also suggest that a positive relationship exists between deposits and loan. This

confirms Obamuyi (2013) who also found positive relationship between deposit mobilization

and bank lending.

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