MF36,12

1028

Managerial FinanceVol. 36 No. 12, 2010pp. 1028-1042# Emerald Group Publishing Limited0307-4358DOI 10.1108/03074351011088432

On the determinants of interestmargin in transition banking:

the case of SerbiaSrdjan Marinkovic and Ognjen Radovic

Faculty of Economics, University of Nis, Nis, Republic of Serbia

Abstract

Purpose – The purpose of this paper is to study the link between, on one hand the interest margin ofthe bank, and the determinants of the interest margin on the other. The basic importance of bankinterest margin or spread (BIS), arises from the fact that it presents an indicator of a bank’s profitabilityas well as the cost of financial intermediation imposed on both its depositors and debtors.Design/methodology/approach – To test the relationship using multiple linear regressions withlagged variables (OLS – ordinary least squares). In addition using correlation analysis as well asbootstrapping model was necessary to overcome the issue of unknown statistical distribution of smalldata samples.Findings – The quantitative study reveals proposed positive and significant correlation between bankinterest margins and proxies of interest-rate risk, negative correlation with risk averseness, positive butslightly lower correlation with credit risk variable, and finally, not so strong influence of foreign bank entry.Research limitations/implications –To be more reliable, models should include individual bank-specific data for cross-banks examination, an area worthy of further research.Social implications – Having implemented the methodology, the paper draws some policyrecommendations. To make interest margin optimal, authorities should redesign existing system ofdeposit protection together with building institutional credit guarantees and thus enable relevantinformation to flow freely amongst participants, i.e. to establish official information sharing arrangementsfor bank industry.Originality/value – This is the first econometric study of the bank interest spread determinants for theSerbian banking industry.

Keywords Serbia, Banks, Interest, Default, Financial restructuring

Paper type Research paper

1. IntroductionBank interest margin, or net interest margin as it is commonly referred to, is usuallydefined as the difference between revenue and interest expense expressed aspercentage of average earning assets. Interest spread, on the other hand, is thedifference between the yield rate on average interest earning assets and the cost rate oninterest bearing fund, with both elements expressed in percentage terms. Clearly, thebank interest margin and spread need not be identical unless there are zero non-interestbearing funds. However, abstracting from the mentioned difference caused bynon-interest bearing funds, those two bank efficiency measures could be consideredequal. For the sake of our analysis, which is oriented to macroeconomic and macro-financial determinants of those measures, the difference is rather irrelevant. Ignoring itallows us to use literature researching both measures. Therefore, we are to be ratherinformal here and use those two terms synonymously. Moreover, accurate and publiclyavailable data in Serbia cover only interest-rate spread, so we have naturally beeninclined to this measure.

Importance of bank interest margin arises from the fact that it becomes a centralpolicy tool in a market-oriented banking system. It is well known that bank interestmargin is an important component of bank profitability. It is set by banks to cover the

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/0307-4358.htm

Determinantsof interest

margin

1029

costs of intermediation, merely to compensate a bank for risk-taking activity impliedby the intermediation. More precisely, adequate interest margin should generatesufficient income to increase the capital base as risk exposure increases.

However, there is ambivalence between interest-rate margin as a measure of bankprofitability and the overall industry efficiency. As a rule more efficient bankingintermediation implies less wide margin. Banking system in Serbia, in last three years(since November 2000) has experienced steady decreasing trend. However, relative toother transition countries and particularly to Euro zone, regarding to this criterion,domestic banking system is still significantly below the average and on the bottom ofthe list (EBRD, 2004, p. 23).

Incentive structure of the main stakeholders in domestic banking system has beeninverted, until the reform. Behaviour of the shareholders was oriented to protecting rents(rent-seeking behaviour) coming from control over the soft credit policy. Namely, seats inmanaging board were considered as a means of directing soft credit allocation policy,rather than making profit for shareholders themselves. The bill was paid by minorityshareholders and depositors particularly. Even advanced prudential rules were not ableto fight effectively against perverse practice, because the system of major shareholders-debtors was inherited from the past and was system-wide. Those shareholders were thekey problem in failure-prone bank corporate governance. In fact, this seriouslyundermined microeconomic efficiency and system robustness. However, after reform themodus operandi started to change. Providing a competitive return on assets and equity,growth of market share, achieving a competitive advantage or surviving in a morecompetitive environment, reducing portfolio risk, improving prestige in financialcommunity, introducing new technology for internal, or/and customer use are acceptedas the main strategic objectives from domestic banks. All those changes are likely toimpact on banking sector interest margin, because both deposit and loan rates (bothmutually determine the bank interest spread or/and margin) take the role of aninstrument for obtaining competitive advantage.

The structure of the paper is as follows. We start with the introductory section, thenin section 2 we review selected theoretical and empirical contributions that deal withinterest margin and its determinants. Then we go to estimate empirically chosenvariables by employing multiple linear regression, correlation analysis, as well asbootstrapping analysis. In section 3, we conclude the analysis with a summary of theresults and implications for the determinants of bank interest margin. Also, somepolicy guidelines are given as well as proposals for further research.

2. Determinants of bank interest margin2.1 Review of theory and evidence on interest margin and its determinantsThe dealership model of banking firm, presented by Ho and Saunders (1981), was amongthe earliest theoretical models that dealt with interest-rate spread determinants. The modelis based on the assumption that banks demand a type of deposit and supply a type of loan.Those demand and supply are stochastic and asynchronous, so that a bank must hold aninventory and thus takes the interest-rate risk. Angbazo (1997) extends the Ho andSaunders model to include default risk and its interaction with interest-rate risk. Apartfrom interest rate and default risk, Wong (1997) spreads the determinants analysis over thegroup of factors like degree of competition in banking industry, a bank market power, aswell as operating expenses. The author underlined positive expected relation between thewhole set of factors except the degree of competition which is expected to be opposite. Alsothe author has modelled the incentive problems a la (Stiglitz and Weiss, 1981), and found

MF36,12

1030

that an introduction of incentive problems among borrowers lowered the optimal bankinterest margin. This happens because asymmetric information makes adverse selectionand moral hazard more likely, thus we might have here Ackerlof-type (1970) failure ofcompetitive market. An intuition behind the statement should be clear. The presence of theincentive problems in the loan market penalizes any aggressive loan pricing behaviour ofthe bank. As a result, the bank lowers its loan rate in order to partially insulate itself fromthe opportunistic behaviour of the borrowers. But, it is worth noting that for Stiglitz-Weissrationing to work the banks’ shareholders must be incentive compatible. As we stress inthe introduction section, this was not the case before the reform took place.

As known from the theory, presence of risk and uncertainty is only a part of the wholestory. In order to have risks included into bank margin, specific risk attitude is needed.Thus, a related task is to consider the impact of an increase in the banks’ degree of riskaversion on theirs spread decisions. Like in Wong (1997) we expect the bank interestmargin to be larger when a bank and/or banking system are more risk averse. Having inmind incentive structure of domestic banks before reform starts, hardening budgetconstraints should be a big step forward to incentive compatible financial intermediary.An assumption that hardening budget constraints brings necessary stimulus for banksto behave prudently, weakens risk tolerance, is intuition for considering restructuringefforts as possible explanatory variable. However, we cannot produce a time consistentseries to present influence of restructuring. We rather use systemic dummy.

Economic models that tend to explain the behaviour of contemporary corporations, statethat managers, operating with a greater degrees of monopoly power (i.e. control over price/rates) tend to hire more staff, pay higher wages, and be less conscious of costs in general. Inother words, they exhibit expense-preference behaviour and perhaps, propensity to the bestof all monopoly profits: a quiet life. More competitive market does not allow such behaviour,so that sub-optimal maximizing would be eliminated (Sinkey, 2002, p. 195). Theoryrecognizes (Bonin et al., 2005; Fortanier, 2001) that foreign entry in a transition bankingcould be followed both by positive and negative external effects. Among positive ones thefollowing are often stressed: possibility to get more funds for the economy, increasedstability of the financial system, improved quality of services, and some positive ‘‘spillover’’effects, such as transfer and dissemination of technology through vertical and horizontalchains, internationalization of R&D, and increased mobility of skilled labour force. Negativeeffects could be ‘‘cherry-pick’’ attitude and ‘‘crowding-out’’ effect, decreased stability in anstrategic industry and, finally, difficult supervision. Regardless positive-negative dilemma,it is still unambiguous that new bank entry, especially foreign entry, implies morecompetitive industry. A number of empirical studies support assumed positive effect ofincreasing foreign share in banking industry, especially with respect to its competitivenessand efficiency. Recent studies (Dages et al., 2000) show that foreign bank presence isassociated with reduced profitability and diminished overhead expenses for domesticbanks, and therefore with enhanced domestic bank efficiency. Findings of increaseddomestic bank efficiency and heightened competition are also supported in the Argentineexperience of the mid-1990s (Clarke et al., 1999). They support our starting assumption thatincreased foreign competition in loan market reduces associated net margins.

Searching for comprehensive analysis of Yugoslav banks’ margin we managed to findonly a few attempts to research. Vasiljevic (1997) as well as Krstic and Marinkovic (2002)in slightly less formalized framework stressed the importance of several determinants:

. adverse maturity structure of deposit base (share of demand to total depositsvaries from 98 per cent (February 1992) to 67 per cent (December 2002) producinga bank system extremely exposed to interest-rate risk;

Determinantsof interest

margin

1031

. non-performing asset problem;

. operating expenses;

. asset-liability currency mismatch; and

. rate of inflation.

Those studies are descriptive ones, so they derive relevant conclusions without exploringhigh-frequency data. Therefore, although we agree with the importance of operatingexpenses as an explanatory variable, data availability does not satisfy requests of anyrigorous statistical analysis. Additionally, currency mismatch emerged, in early period,merely due to gap in foreign assets and liabilities amounts in ‘‘frozen items’’ of balancesheet. So, pre-tax profit was influenced, but without any significant impact on cash flow.Since, bank balance sheets have been cleaned up from ‘‘frozen items’’ big part of currencymismatch disappeared. The reason why we do not consider here the influence of inflationon interest margin is because our model covers period with significant monetary stability.Besides, the so-called Fisher equation applies to both deposit and loan market, so wecould expect the interest margin to remain more or less resistant to inflation influence.

However, a researcher should bear in mind that there are many ambiguousness thatmake interest margin most sensitive variable in banking business. For instance, instudies exploring efficiency of a banking sector (Dziobek and Pazarbasioglu, 1998, p. 3)interest margin is used as a leading indicator of achieved or restored efficiency, as well asa sign of success in restructuring efforts. But, analysing margin data through time maybe biased. Changing deposit and particularly credit market condition in terms of differentborrowers’ selection makes a comparison not perfectly consistent. Holmstrom and Tirole(1997) stress a significant empirical problem. Because of a possible flight to quality,interest-rate spread across different periods is not fully comparable. Composition ofborrowers is changed so that in bad times there is a decline in the share of credit flowingto borrowers with high agency costs, that is, small firms. This may explain why thefindings based on interest-rate spread are less consistent (cf. Bernanke, 1993).Nevertheless, interest-rate spread/margin can be taken as an indicator (albeit animperfect one) of the effective cost of intermediation to the users of the banking system.Its macroeconomic importance comes from the fact that widening of interest margininfluences economic activity on the way similar to increase of fiscal burden on bankborrowers and depositors (Daniel and Saal, 1997, p. 17).

Among other possible determinants theory indicates a number of imperfections andregulatory restrictions, which are likely to impact on actual margins, that is,opportunity costs of required reserves, the cost of implicit interest payments ondeposits in the form of service change remissions or subsidies, deposit insurancepremium, and finally capital requirements. However, during the test period all theseelements remained constant, and thus without influence on margin variation.

2.2 The data and empirical variablesIt becomes clear that there must be the influence of default risk, interest-rate risk, bankliquidity, risk averseness, and industry competitiveness on bank interest-rate spread.We do not want to cover all possible determinants here, but rather to test some of them.In choosing relevant variables we have been strongly constrained with data availability.Namely, Serbian banks do not report regularly and frequently on their net interestmargin. Even interest-rate spread data for individual banks are not publicly availableon monthly basis, so we were not able to construct any meaningful time series on

MF36,12

1032

dependent variable to produce cross-banks examination of interest spreaddeterminants. The only possible way was using aggregate data that already containaveraged weighted interest spread of Serbian banks. The ultimate source of those datawas banks’ monthly reports on interest rates charged on their assets and paid on theirliabilities, both created during the reporting period (a month). But, we have taken datafrom National Bank of Serbia’s official reports. We have faced similar problems whenattempting to construct coherent time series on some explanatory variables.

In choosing explanatory variables we have been trying to obtain proxies that fit thetheory requests best. Intuition for using saving data as a proxy for interest-rate riskand liquidity risk comes from the fact that more non-demand deposits (i.e. term andsaving deposits) decrease need for maturity transformation and, therefore, decreaseinterest-rate risk and probability of banks to get illiquid. Row data on saving depositedat banking system as a whole, top 15 banks and foreign banks are given in theAppendix, Table AI together with statistics on other variables.

So-called credit or quality spread is chosen as a proxy for default risk. This is simplyyield difference between default-risk free issues and risky securities of the samematurity, as possible. A classic example of risk-free security is short-term bill ofGovernment (Treasury bill) or National Bank of Serbia (formerly Yugoslavia) bill. Thosesecurities are actively traded through an open market and thus their yield representsrelevant market supply and demand forces. Since risky securities differ significantly bytheir credit quality (depending primarily upon issuer’s creditworthiness) again anaverage figure has been used, so it approximates average cost the most prominent part ofdomestic company sector should pay to rise short-term funds from the market. Row dataon quality spread are included both in levels and growth rates in the Appendix, Table AI.

Another proxy must be included to capture competitive nature of deposit and loanmarket. Bargaining power of a bank and therefore its price setting behaviour cruciallydepends on industry competitiveness. Although the number of banks operating in thesystem after the reform is even lower than before, index of concentration seems to havedecreased. Furthermore, currently 47 banks operate in Serbia, while top five hold 48per cent of total assets (2003). The leading bank in Serbia (Delta Banka, a.d.) holds only12 per cent of total assets, which means that, compare to similar countries the system isnot highly concentrated. Measured by Herfindahl-Hirschman index Serbia’s bankingsystem is among the least concentrated worldwide. In case of Serbia the figure is 607(author’s calculations) which is significantly less than even in most advanced transitioncountries, i.e. Czech Republic (1757), Hungary (1241), and Poland (899) (see Gelos andRoldos, 2004, Table 1, p. 44). Namely, competitiveness increased mostly because somenew players came into the system from abroad. A total of 12 foreign banks startedrunning business since the reform. They influence predominantly retail bankingoperations – deposit and consumer loan services. Here, foreign bank share on retaildeposit market is taken as a proxy for the industry competitiveness. Data on savingdeposited at foreign banks are presented in the Appendix, Table AI in levels, whilegrowth rates stand for increase in foreign bank share. The reason for that is twofold:

(1) the data on deposit share are available monthly and reliable; and

(2) competition between domestic banks themselves is incomplete because of stillsignificant government share in industry and widespread cross-shareholdings.

State-owned banks own 32.4 per cent of the bank sector capital, foreign banks 19.4 percent, private banks 39.7 per cent, while socially owned enterprises (not restructured, yet)have majority shareholding in banks that hold 8.5 per cent of the industry capital (2003).

Determinantsof interest

margin

1033

Dummy is defined as the binary variable which codes restructuring efforts as one andwhich assigns zero to periods that miss such an operation. By restructuring efforts wemean exactly presence of a respectable action directed at building market discipline.Specifically, two significant regulatory actions were undertaken. The first wasannouncement of an independent auditing result, which revealed deep insolvency of thecore of the banking system. At the time, final decision was not foreseen. Soon after theannouncement the decision was made. Four biggest banks together with seven smallbanks, mostly of regional importance, went under bankruptcy early in 2002(www.bra.gov.yu/bankeustecaju). This operation was completely unforeseen and produceda radical turnover in a bank attitude toward risk. Before that, ambiguity was presentedwhether the National bank of Yugoslavia would follow a regulatory forbearance, try tobail-out distressed banks or choose the radical way. Finally, the lesson was given.

Again, it should be noted that these included factors are not intended to beexhaustive but are among the most amenable to measurement. The economic rationaleand the proxies for the risk and control variable are summarized in Table I.

The sample period in the analysis is from July 2000 to August 2003. Before July 2001the banking system did not record any respectable amount of saving. One reason for notusing later data is the problem of losing track of some proxy variables. Namely, authoritiesstopped reporting on savings, both foreign and domestic currency denominated. Wecannot be straightforward on reasons for that, since we see no good reason for rejectingthe good practice. That could be motivated only by political reasons – stagnant amount ofsaving – which was exactly the case in the period lacking detailed data, obviously may betaken as current authority’s responsibility, so they could be blamed for that.

2.3 Empirical specificationThis econometric study specifies a linear model between bank interest margin and somemacroeconomic explanatory variables. Bank interest margin (dependent variable) ismeasured as a difference between average deposit and average loan rate and, thus, ismore like pure interest spread.

The empirical specification focuses on the reported bank interest spread (BIS)which is assumed to be a function of the following factors, reflecting the compensationfor bank inventory risk arising from uncertainty about loan and deposit transactions:

BIS ¼ FðInterest-rate risk; Default risk; Competition; Risk aversenessÞ ð1Þ

The set of macroeconomic fundamental variables is selected on the basis of prevailingeconomic theory and contains variables like the saving, a proxy for competitiveness,

Table I.Empirical model

variables

Variable Proxy Sign Rationale

Interest-raterisk

SAVING ¼ total savingstotal assets

Negat. SAVING ") int. rate risk #)int. risk premium #

Default risk QUALIT ¼ market rate �riskless rate

Posit. QUALIT ") Default risk ")def: risk premium "

Competit. effect FOREIG ¼ foreign bank savingtotal saving

Negat. FOREIG ") competition ") margin #

Restruct. effect Dummy ¼ 1

0Negat. Dummy½1� ) risk averseness ") margin #

MF36,12

1034

default-risk premium, and also restructuring actions, represented by regime dummyvariable. Accordingly, the regression estimates are based on:

BISit ¼ b0 þ b1 � SAVINGit þ b2 � QUALITit þ b3 � FOREIGit þ b4 � Dummyit þ "it

ð2Þ

In choosing direction of causality we assume that saving dynamics – that is driven more-less independently on bank interest rates – influence interest margin, although the oppositemust be relevant too. Namely, it seems reasonable to assume, quite contrary, that anincrease of saving – both foreign and domestic – was a consequence of rise in real depositrates. We estimate country specific models by ordinary least squares (OLS) constructingmultiple linear regressions. In some of the models the variables are measured in growthrates. In other models both growth models and the levels of the variables enter the model.In Equation (3) the legged explanatory variables are included in the set. Moreover, wediscuss some time series properties of the variables, but not co-integrating relations.

2.4 The results2.4.1 Multiple linear regression. The purpose of multiple linear regressions is toestablish a quantitative relationship between a group of predictor variables and aresponse, or, dependent variable. This relationship is useful for:

. understanding which predictors have the greatest effect;

. knowing the direction of the effect (positive or negative); and

. using the model to predict future values of the response when only the predictorsare currently known.

The elements of the vector statistics are the regression R2 statistic, the F statistic (for thehypothesis test that all the regression coefficients are zero), and the p-value associatedwith this F statistic. Furthermore, the coefficient of determination (R2) is slightly lowerthan 0.30 for all models and also insensitive to whether the lagged dependent variable isincluded in the model, or not. Hence the models do not fit the data well, and the interest-rate margin variability stay largely unexplained by the specific models, less than 30per cent of overall variability is explained by the models. Generally, the fit (measured byR2) varies slightly between the models and stands better for the models estimated inlevels than for the models estimated in first differences, as usual in time series (note thatthe models estimated in levels are not included in Table II because they fail to approvestationarity). R2 for the models is 0.2666, 0.2932, and 0.2917, respectively. It indicates thatthe models account for over 26 per cent of the variability in the observations, in the worstcase. The test shows the p-value of the null hypothesis that the error term in equation isnormally distributed. The F statistics of about 4 and its p-value of 0.033 indicate that thefirst model fits well and it is unlikely that all of the regression coefficients are zero.Slightly worse are the other two, especially the third one. Anyway, the result leads torejection of null hypothesis that regression parameters are equal to zero. But stillunsatisfactory levels of R2 statistic negate any value of the models for using them topredict future values of the response.

This table presents OLS estimates of regressions of the net interest margins oninterest-rate risk (SAVING), competition effect (FOREIG), default risk (QUALIT), andrisk averseness (Dummy). The dummy variable is included to capture the regimeswitch effect. It could be one (if there is change) or zero (there is no change).

Determinantsof interest

margin

1035

2.4.2 Correlation coefficients. Correlation analysis returns a matrix of correlationcoefficients calculated from an input matrix whose rows are observations and whosecolumns are variables. Also, the analysis returns a matrix of p-values for testing thehypothesis of no correlation. Each p-value is the probability of getting a correlation aslarge as the observed value by random chance, when the true correlation is zero. Thus,if value of p is small say less than 0.05 (for 95 per cent confidence interval) then thecorrelation is significant. The correlation coefficients and related p-values have beencalculated using the corrcoef function (Matlab) and presented in Table III.

The coefficients are for correlation between dependent and all tested explanatoryvariables, individually. The first column that contains numbers is the one with no timeleg. The second is for one period (a month) leg, while the third is for two period leg. Arationale for including time leg in investigation of relationship should be likelihoodthat some of explanatory variables do not influence (if any) interest spread instantly, so

Table II.Multiple linear

regression estimates

Variable Regression 1 Regression 2 Regression 3

Intercept 1.2151 1.1270 1.1432(0.6258) (0.5360) (0.4985(1.8043) (1.7180) (1.7878)

SAVING �0.3735 �0.2733 �0.2655(�0.6643) (�0.6114) (�0.6739)(�0.0827) (0.0649) (0.1428)

QUALIT 0.1760 0.1719 0.1837(�0.2921) (�0.2890) (�0.3135)

(0.6442) (0.6328) (0.6808)FOREIG �0.0325a

(�0.3045)(0.2395)

Dummy �0.1100 �0.0998(�0.3178) (�0.3553)

(0.1043) (0.1557)

R2 0.2666 0.2932 0.2917F 3.9986 3.0423 2.0594p 0.0330 0.0503 0.1244n 26 26 26

Notes: Figures in the parentheses are: lower (first) and upper (second) boundary of probabilitydistribution (95 per cent of confidence); athe regression coefficient is calculated with one period lag

Table III.Correlation coefficients

Variable X(t) X(t�1) X(t�2)

SAVING �0.48 0.14 �0.20(0.0119) (0.1772) (0.3308)

QUALIT 0.17 �0.02 �0.26(0.3883) (0.3666) (0.2157)

FOREIG �0.11 �0.19 �0.10(0.6002) (0.3430) (0.5902)

Dummy �0.42 0.14 0.07(0.0298) (0.1130) (0.2198)

Note: Figures in the parentheses are p-values

MF36,12

1036

we should allow for delay. The test confirms that foreign banks are more likely toinfluence the bank interest spread with one period leg (both coefficient and p-value aremore reliable). For other variables time leg extension produce no additional reliability.

The variables reported to have a significant effect on the dependent variable are: the totalsavings (�0.48) and the regime dummy (�0.42). On the contrary, the share of foreign banks(�0.19), and partly the default risk proxy (0.17) are reported not to have significant effect onthe dependent variable. Again, regarding p-values, only the numbers for interest-rate riskproxy (SAVING) and restructuring effects (Dummy) indicate significant relationship.

2.4.3 Bootstrapping method. In recent years the statistical literature has examined theproperties of re-sampling as a means to acquire information about the uncertainty ofstatistical estimators. The bootstrap is a procedure that involves choosing random sampleswith replacement from data set and analysing each sample the same way. Sampling withreplacement means that every sample is returned to the data set after sampling. Thus, aparticular data point from the original data set could appear multiple times in a givenbootstrap sample. The number of elements in each bootstrap sample equals the number ofelements in the original data set. The range of sample estimates we obtain allows us toestablish the uncertainty of the quantity we are estimating. Namely, in small samples thedistribution of the test statistic may deviate considerably from the normal distribution (aprecondition for using OLS regressions as well as correlation coefficient). Then, rather thanmaking unrealistic assumptions about the distribution of the test statistic, it may be betterto estimate the distribution from the sample at hand. For that reason we benefit from usingbootstrap. One should note that even if the sample distribution is the non-parametricmaximum likelihood estimator of the population distribution, the bootstrapped distributiondoes not necessarily approximate the true distribution of the test statistic only if the sampleis a good approximation of the population. Lack of congruence between the sampledistribution and the population could arise, either because of a small sample or justbecause of bad luck. Hence, bootstrapping does not necessarily solve all problems causedby the fact that the population distribution is unknown. But, bootstrapping should beregarded as an alternative to standard parametric methods, in particular in situationswhere we a priori expect the test statistic not to have a standard distribution or where wea posteriori reject the assumption that the distribution of the test statistic is standard.

Therefore, what may a bootstrap tell us about the correlations? The number �0.48(0.0119) describes the negative connection between total savings and interest margin, butthough �0.48 may seem not large, we still do not know if it is statistically significant.Using the bootstrap function we can resample the total savings and interest marginvectors as many times as we like and consider the variation in the resulting correlationcoefficients. This command resamples the vectors 1,000 times and computes the corrcoeffunction on each sample. See the histogram of the results in the Appendix (Figures A1and A2). Nearly all the estimates lay on the interval [�1; 0]. This is strong quantitativeevidence that total savings and interest margin are negatively correlated. Moreover, itdoes not require us to make any strong assumption about the probability distribution ofthe correlation coefficients. Statistical significant stability of correlation coefficient wasfound also between quality spread and interest margin as well as between share offoreign bank’s savings and interest margin. But, in the later case only with one periodlag, so we can conclude that interest margin is positively connected to quality spread andnegatively connected to foreign banks competition effect with a month lag.

This quantitative analysis suffers from one shortcoming. The study does not takeinto account one serious econometric problem. In the econometric models problemcould arise because regression equations like above are normally the so-called

Determinantsof interest

margin

1037

unbalanced ones. An equation is balanced if and only if the dependent variable is of thesame order of integration as the explanatory variables (either individually orcollectively, as a co-integrated set). A stationary variable has a constant mean and aconstant variance, whereas both the mean and the variance of non-stationary variablesvary over time. Therefore, non-stationary variables cannot explain the behaviour of astationary variable, simply because non-stationary variables behave in afundamentally different way, unless linear combination of the non-stationary variablesis stationary, in which case the non-stationary variables are said to co-integrate. Themodel’s malfunctioning calls for further research.

3. ConclusionsThe main picture from this study is not disappointing. It seems difficult to findaccurate relationship between chosen macroeconomic variables and the dependentvariable. The estimated regression coefficients largely confirm the theory. Some of theexplanatory variables suggested by financial theory seem to have little effect oninterest-rate spread, but all of the coefficients were estimated with the predicted sign.Unfortunately, the multiple linear regressions did not return a reliable econometricmodel capable to predict future levels of interest margin. There may be several reasonsfor these unsatisfactory empirical results in our study. The econometric models may bemisspecified, i.e. the functional form may be wrong or relevant explanatory variablesare not included in the set of fundamentals. However, the study reveals that interestmargin is related to chosen macroeconomic fundamentals. Several variables, e.g. thesavings and quality spread, had a significant effect on the interest margin with thepredicted sign. Other variables, though without statistically significant effect areestimated with the ‘‘right’’ sign.

Apart from being another econometric examination of bank interest margindeterminants, this study can also help in designing policy guidelines. It seems quiteclear from the study that the margin may be narrower if only:

. the risks (interest-rate risk, credit risk, etc.) a bank assumes by intermediationreduce; and

. bank attitude toward risk optimizes.The first condition tells us that authorities should create necessary preconditions tofacilitate increase of long-term saving both foreign and domestic currency denominated.A useful policy tool for implementation might be institutional incentive-compatibledeposit guarantee scheme, while encouraging public to shift its deposit claims towardlocal currency denominated assets must be additionally supported by adequatemonetary policy. Public must be confident in long-term monetary stability for what thefiscal sustainability (foreign debt burden) stands crucial. The second is simply the resultof two opposite influences:

(1) degree of bank risk averseness; and

(2) presence of information frictions in loan and deposit market.

To make a bank optimally risk averse we need to build right incentives of the mainbank stakeholders (managers, bank owners, regulators, depositors, etc.). But thisissue remains out of the scope of this discussion. From the other side, fightingagainst information-driven frictions is the case for reform in accounting/auditing,disclosure, as well as legal/regulatory framework. Namely, institutional solutionmust be provided to encourage all public producers of information to share their

MF36,12

1038

information privilege with others. This could lead to more competitive and fairoutcome. Another possible way to circumvent information friction that disturbscredit market is a comprehensive system of credit guarantees aimed to supportsmall and medium sized enterprises, especially through better access to credit(lower loan rates), and at the same time, decreasing bank margin without adverseinfluence on bank profitability.

Competition in financial service industry here is not confirmed as an influential factor.Some domestic banks are connected to each other by cross-shareholdings. Thus,strategic behaviour is likely to be strong factor of weakening competition effect.Moreover, it seems that foreign banks take the opportunity of enjoying quite comfortablenet interest margin. Namely, though the timing of decreasing the bank interest marginlargely coincides to accelerating of foreign banks activity in retail banking, theeconometric analysis shows that it was mostly due to restructuring efforts. But in orderto be quite confident in this conclusion a data analysis on individual bank level must beundertaken, for what relevant data are lacking.

References

Ackerlof, A.G. (1970), ‘‘The market for ‘lemons’: quality uncertainty and the market mechanism’’,Quarterly Journal of Economics, Vol. 84 No. 3, pp. 488-500.

Angbazo, L.A. (1997), ‘‘Commercial bank net interest margins, default risk, interest-rate risk and off-balance sheet banking’’, Journal of Banking and Finance, Vol. 21 No. 1, pp. 55-87.

Bernanke, B. (1993), ‘‘Credit in the macroeconomy’’, Federal Reserve Bank of New York, QuarterlyReview, Vol. 18, pp. 50-70.

Bonin, P.J., Hasan, I. and Wachtel, P. (2005), ‘‘Bank performance, efficiency and ownership intransition countries’’, Journal of Banking and Finance, Vol. 29 No. 1, pp. 31-53.

Clarke, G., Cull, R., D’Amato L. and Wachtel, P. (1999), ‘‘The effect of foreign entry on Argentina’sdomestic banking sector’’, working papers, World Bank, Washington, DC.

Dages, G.B., Goldberg, L. and Kinney, D. (2000), ‘‘Foreign and domestic participation in emergingmarkets: lessons from Mexico and Argentina’’, FRNBY Economic Policy Review,September, Vol. 6 No. 3, pp. 17-36.

Daniel, J. and Saal, M. (1997), ‘‘Macroeconomic impact and policy response’’, in Alexander,W.E. et al. (Eds), Systemic Bank Restructuring and Macroeconomic Policy, IMF,Washington, DC.

Dziobek, C. and Pazarbasioglu, C. (1998), ‘‘Lessons from systemic restructuring’’, EconomicIssues, Vol. 14, IMF, Washington, DC.

EBRD, (2004), Spotlight on South-Eastern Europe: An Overview of Private Sector Activity andInvestment, EBRD, London.

Fortanier, F. (2001), ‘‘FDI and technology transfer’’, The Costs and Benefits of FDI, OECD, Paris.

Gelos, R.G. and Roldos, J. (2004), ‘‘Consolidation and market structure in emerging marketbanking systems’’, Emerging Markets Review, Vol. 5, pp. 39-59.

Ho, T.S.Y. and Saunders, A. (1981), ‘‘The determinants of bank interest margins: theory andempirical evidence’’, Journal of Financial and Quantitative Analysis, Vol. 16 No. 4,pp. 581-600.

Holmstrom, B. and Tirole, J. (1997), ‘‘Financial intermediation, loanable funds, and the realsector’’, Quarterly Journal of Economics, Vol. 112 No. 3, pp. 663-91.

Krstic, B. and Marinkovic, S. (2002), ‘‘Kamatni raspon u jugoslovenskom bankarstvu:teorijsko-empirijska analiza za period 1995-2000’’, Strategijski Menadzment, Vol. 1,pp. 48-57.

Determinantsof interest

margin

1039

Sinkey, F.J. Jr (2002), Commercial Bank Financial Management, Prentice-Hall, Upper Saddle River, NJ.

Stiglitz, J.E. and Weiss, A. (1981), ‘‘Credit rationing in markets with imperfect information’’,American Economic Review, Vol. 71 No. 3, pp. 393-410.

Vasiljevic, B. (1997), ‘‘Problemi formiranja kamatnih stopa domacih banaka, u Pavlovic’’, Bozic R.i R. (ured.), Finansijski i Bankarski Menadzment u Savremenim Uslovima Privredjivanja,Institut za ekonomska istrazivanja, Pristina, pp. C42-51.

Wong, K.P. (1997), ‘‘On the determinants of bank interest margin under credit and interest raterisks’’, Journal of Banking and Finance, Vol. 21 No. 2, pp. 251-71.

Further reading

Fries, S., Neven, D. and Seabright, P. (2002), ‘‘Bank performance in transition economies’’,Working Paper No. 76, November, EBRD, London.

Appendix

Table AI.Statistics fordata sample

Year Month

Levels Growth rates

BISTotal

savingsForeignsavings

Top 15savings

Qualityspread BIS

Totalsavings

Foreignshare

Qualityspread

2001 July 1.27 92 3 74 2.24 0.88 – – 0.95Aug 1.34 102 6 83 3.05 1.05 1.10 1.72 1.36Sep 0.92 113 15 90 1.76 0.69 1.11 2.24 0.58Oct 0.95 129 31 105 2.65 1.03 1.15 1.84 1.51Nov 0.99 160 61 134 2.52 1.04 1.24 1.60 0.95Dec 0.54 373 151 332 2.25 0.54 2.33 1.06 0.89

2002 Jan 0.59 497 196 447 2.11 1.09 1.33 0.97 0.94Feb 0.35 689 252 623 2.29 0.59 1.39 0.93 1.08

Mara 0.59 (1.74) 670 251 609 2.15 1.69 0.97 1.02 0.94Apr 1.59 649 243 593 2.07 0.91 0.97 1.00 0.96May 1.55 693 239 633 1.65 0.97 1.07 0.92 0.80Jun 1.39 714 266 657 1.69 0.90 1.03 1.08 1.02July 1.43 731 275 673 1.74 1.03 1.02 1.01 1.03Aug 1.40 752 262 693 1.66 0.98 1.03 0.92 0.95Sep 1.26 764 288 703 1.76 0.90 1.02 1.08 1.06Oct 1.29 778 296 712 1.63 1.02 1.02 1.01 0.93Nov 1.35 792 303 730 1.55 1.05 1.02 1.00 0.95Dec 1.28 813 316 750 1.56 0.95 1.03 1.01 1.01

2003 Jan 1.10 840 326 775 1.47 0.86 1.03 1.00 0.95Feb 1.04 873 334 815 1.40 0.95 1.04 0.98 0.95Mar 1.01 881 339 819 1.17 0.97 1.01 1.00 0.84Apr 1.18 902 345 829 1.24 1.17 1.02 0.99 1.06May 1.05 922 352 835 1.26 0.89 1.02 1.01 1.01Jun 1.07 927 357 848 1.26 1.02 1.01 1.00 0.99July 0.96 911 354 835 1.27 0.90 0.98 1.00 1.01Aug 1.01 905 357 829 1.27 1.05 0.99 1.02 1.00

Notes: Saving amounts are in millions of EUR; asince March 2002 the authorities report on bankinterest spread that includes demand deposits

MF36,12

1040

Figure A1.Bootstrap analysis ofcorrelation coefficients

Determinantsof interest

margin

1041

Figure A1.

MF36,12

1042

Corresponding author

Srdjan Marinkovic can be contacted at: srdjan.marinkovic@eknfak.ni.ac.rs

Figure A2.Bootstrap analysis ofregression coefficients

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints