Economic Change and Restructuring (2005) 38:113–127 DOI 10.1007/s10644-005-4525-3
Springer 2006
The Causal Relationship between Stock, Credit Market and Economic Development: An Empirical Evidence for Greece CHAIDO DRITSAKI* and MELINA DRITSAKI-BARGIOTA Department of Applied Informatics, University of Macedonia, 156 Egnatia str., 54006 Thessaloniki, Greece (*Corresponding author: E-mail:
[email protected]) Abstract. This paper examines empirically the causal relationship among financial development, credit market and economic growth by using a trivariate autoregressive VAR model in Greece for the examined period 1988:1–2002:12. The results of cointegration analysis suggested that there is one cointegrated vector among the functions of stock market, the banking sector development and economic growth. Granger causality tests have shown that there is a bilateral causal relationship between banking sector development and economic growth and a unidirectional causality between economic growth and stock market development whereas there is no causal relationship between the stock market and banking sector development. JEL Classification Numbers: O11, C22 Key words: credit market, economic development, Granger causality, Greece, stock market
1. Introduction In recent years, development issues are at the forefront. Many academicians consider the role of financial sector – banks and capital markets – to be more striking in the development process. Technological innovation and radical changes in various sectors in many economies brought a widespread euphoria to the economic performance of many countries. Greece is one of the EU members that can be characterized as an emerging market, having experienced a number of facts that truly justifies this characterization. After joining the European Monetary Union (EMU) and adopting the new currency, euro, in 1 January 2001, the Greek economy operates in a new monetary environment. The progress that was achieved in terms of low inflation, the improvement of fiscal policy of Greece and the enlargement and upgrade of production potential has already formed more suitable conditions for development. In 1996, the Greek stock market reached its highest point of development. Not so much concerning prices but other areas, such as:
114 • Market capitalization has reached 120% of GNP • The contribution of foreign institutional investors • The raising of capital from the primary market Furthermore, financial innovations and technological transformations, in view of the modernization of networks, ATM’s, Internet and mobile telephony in Greek banks as well as the integration within the European and Monetary Union, created a radical reorganization of banking environment. ‘‘Another characteristic of the changing landscape in recent years has been the withdrawal of the state’’ (Garganas, 2003), taking the form of statutory liberalization and privatization. It will be an omission not to mention ‘‘the expansion of Greek banks in the Balkans and the South Eastern Mediterranean. Many domestic banks have now realized that these markets, although still in financial uncertainty and dominated by political considerations, have significant potential for growth in the long run’’ (Karatzas, 2000).
2. Literature review The relationship between stock and credit market and economic development had become an issue of extensive analysis. The question is whether stock and credit market precedes or simply follows economic development. Evidence, on whether finance cause growth, help to reconcile these views. The theoretical framework goes back to the study of Schumpeter (1912) who emphasizes the positive influence of the development of a country’s financial sector on the level and the rate of growth and argues how necessary for economic growth are the services that financial sector provides – of reallocating capital to the highest value use without substantial risk of loss through moral hazard, adverse selection or transactions costs. Lewis (1955), one of the ‘‘pioneers’’ of development economics, postulates a two-way relationship between financial development as a consequence of economic growth which in turn feed back as a stimulant to real growth. Jung (1986) investigates the causal relationship between financial development and economic growth. He uses data on 56 countries, of which 19 are developing and the rest are characterized as less-developing countries (among them is Greece). Jung mostly based his results and conclusions on the ‘‘demand-following’’ hypothesis and the ‘‘supply-leading’’. He focused on the causal relationship between the DC and LDC countries and also among the LDC only. He found out that the LDC countries have a supply-leading causality which means that there is a causal relationship from financial development to economic growth whereas for DC the reverse causal direction occurs.
115 Empirically, King and Levine (1993) show that the level of financial intermediation is a good predictor of long-run rates of growth, capital accumulation and productivity. In addition, well-developed stock markets can easily lead to economic development through its enhanced liquidity as the investors diversify their risk in different shares creating a portfolio with high return investments, thus accelerating productivity growth. Levine and Zervos (1993) suggest that stock market development is strongly correlated with growth rates of real GDP per capita and real physical capital per capita. Most importantly, they found that both stock market liquidity and banking development predict the future growth rate of economy when they both enter the growth regression. Wachtel and Rousseau (1995) examined the relationship between finance and growth in the USA, UK and Canada in a long-run perspective. Briefly they found a robust correlation between the financial sector and economic growth, although the correlation is not significant for every type of intermediation. Their tests for Granger-causality demonstrate that financial development causes economic growth. However, because the variables are non-stationary they failed to capture any cointegrating relationship between growth and financial intermediation. Boyd and Smith (1996) argued that the financial initiative will affect and be affected from the development of real sector. They present an endogenous model of growth, which incorporates contemporaneously the purchase of shares and banks. In their model investors can choose between debt and shares and their decisions will be based mainly from the information that investors should collect in order to make the investment. So, capital accumulation derives from two technologies: one with an expected profit but subject to an informational framework (credit markets) and a technology with a low expected profit but commonly observable (purchase of shares). While the economy is developing, the model shows that investors will make use more of stock markets. The role of stock market is to provide investors low-funds in order to promote economic growth. So economic development is a positive function of stock market development and banking sector development. A paper that is of high interest is the one of Luintel and Khan (1999). In their paper, they empirically examined the long-run causality between financial development and economic growth in a multivariate time-series using data from 10 sample countries. They tried to find solutions to problems that were being present in the existing bivariate time series. Their findings, the bi-directional causality between financial development and economic growth in all sample countries analyzed, are highly consistent with the theoretical predictions of both the finance-growth literature and the endogenous growth models (Greenwood and Jovanovic, 1990).
116 Arestis and Demetriades (1998) in their work contradict the conclusion of King and Levine (1993) that ‘finance leads economic growth’ and explore the causality between financial development and economic growth for each examined country separately. Based on the taxonomy established by Gerschenkron (1962), they adopt the division of financial systems into two categories: the ‘bank-based’ and the ‘capital-market based’ financial system. After analyzing the key features of the ‘bank-based’ systems and those of ‘capital-market based’, they test the causality and stress out that the causality between financial intermediation and economic growth is likely to be either from finance to growth or bi-directional in the case of the ‘bank-based’ systems. In the case of ‘capital-market based’ system it is expected to be from growth to finance but a bi-directional relationship cannot be ruled out. Arestis and Demetriades (1998) conclude that in investigating causality between finance and growth, the investigation of financial system for each country has its own institutional details and it is of paramount importance. It will be an omission not to mention the empirical evidence conducted by Kar and Pentecost (2000) about the financial development and economic growth of Turkey. Using Granger causality tests and vector error-correction mechanisms they found that the direction of causality between financial development and economic growth is sensitive to the choice of measurement for financial development in Turkey. So they concluded that it is not easy to accept the view that ‘finance leads growth’ or that ‘finance follows growth’. However, their results showed that in the case of Turkey economic growth leads financial development. In a more recent work, Shan and Morris (2002) found meager evidence that financial development ‘leads’ economic growth, either directly or indirectly. The sample consists of 19 OECD countries (including Greece) and also two Asian emerging economies, China and South Korea covering the period 1985 until 1998. Using as proxies for financial development total credit and the spread of interest rates, for investment the variable ‘stock market price’ and for productivity the consumer price index, they tested the causality between the patterns of relationships between financial development and economic growth and the relationship between financial development and investment and productivity growth. They observed that there is no causal relationship in either direction, in the Granger sense, between financial development and economic growth in most countries (including Greece) as well as between finance development and investment and productivity. Caporale et al. (2002) in a sample of seven countries try to examine the causal linkage between stock market development, financial development and economic growth. Based on Wald tests, they try to perform causality in a bivariate context, using only financial development and economic growth. The results provided little evidence of causality. So, in a trivariate context
117 (where stock market development is being involved), the causality results can lead to important conclusions. Domestic credits cause economic growth (Greece, Korea, Philippines) and also bank deposits have a causal effect on economic growth (Greece, Korea and Portugal). As far as stock market development is concerned the results shown that it has a causal effect on economic growth in five out of seven countries (among them is Greece). Beck and Levine (2002) found that banks and stock market development always enter jointly significant in all the system panel growth regressions using alternative conditioning information sets and alternative panel estimators. These findings suggest that stock markets provide different financial services from banks or in other words, multicollinearity would produce jointly significant results but would not produce results where stock market and bank indicators each enter the growth regression significantly. Econometrically, this paper’s techniques improve significantly over existing studies on the link between banks, stock markets and economic growth. Thus, the data are consistent with theories and emphasize an important positive role for financial development in the process of economic growth. The aim of this paper is to investigate the causal relationship, which might exist between the examined variables, and to answer in the following causal hypothesis in order to get useful conclusions. The examined causal hypotheses are the following: • Do the functions of stock market cause economic growth? • Does banking sector development cause the functions of stock market? • Does the banking sector cause economic growth? The rest of the paper has the following structure: Section 3 outlines the theoretical framework. Section 4 set outs the Dickey–Fuller tests and examines the stationarity of the data used. The cointegration analysis between the used variables is discussed in Section 5. Section 6 describes the VAR and error-correction model. The Granger causality test is given in Section 7 and Section 8 concludes. 3. Data and the theoretical framework In the analysis of the relationship between financial, credit and economic growth the following function is used: EG ¼ f(SD, BD) where EG =Economic growth SD=Functions of stock market BD=Banking sector development
ð1Þ
118 For the empirical analysis we use the industrial production as a proxy for economic development, market capitalization as a proxy for stock market development and money supply (M2) as a proxy for banking sector development. The data employed for this research are monthly and cover the period from 1988:1 to 2002:12 and are available from the IFS (International Financial Statistics) database, Bank of Greece, OECD Database and the base year is 1996. All data are expressed by logarithms in order to include the proliferative effect of time series and are symbolized with the letter L preceding each variable name.
4. Unit root test Many macroeconomic time series contain unit roots dominated by stochastic trends as developed by Nelson and Plosser (1982). Unit roots are important in examining the stationarity of a time series, because a non-stationary regressor invalidates many standard empirical results. The presence of a stochastic trend is determined by testing the presence of unit roots in time series data. In this study, unit root test is tested using Augmented Dickey– Fuller (ADF) (1979) Table I.
Table I. Tests of unit root hypothesis Augmented Dickey–Fuller sl LEG LSD LBD DLEG DLSD DLBD
)0.82729 )1.1727 )1.8722 )9.3455 )3.2599 )4.3577
ss )0.75346 )1.5537 )0.92715 )9.5785 )5.0618 )6.0642
j 4 7 12 3 6 10
Notes: sl is the t-statistic for testing the significance of d2 when a time trend is not included in Equation (2) and ss is the t-statistic for testing the significance of d2 when a time trend is included in Equation (2). The calculated statistics are those reported in Dickey–Fuller (1981). The critical values at 1, 5 and 10% are )3.61, )2.94 and )2.60 for sl and )4.21, )3.53 and )3.19 for ss respectively. The lag-length structure of aI of the dependent variable xt is determined using the recursive procedure in the light of a Langrange multiplier (LM) autocorrelation test (for orders up to four), which is asymptotically distributed as chi-squared distribution and the value t-statistic of the coefficient associated with the last lag in the estimated autoregression.
119 The augmented Dickey–Fuller (ADF) (1979) test is referred to the t-statistic of d2 coefficient on the following regression: 4Xt ¼ d0 þ d1 t þ Ad2 Xt1 þ
k X
ai 4Xti þ ut
ð2Þ
i¼1
The ADF regression tests for the existence of unit root of Xt, namely in the logarithm of all model variables at time t. The variable DXti expresses the first differences with k lags and final ut is the variable that adjusts the errors of autocorrelation. The coefficients d0 , d1 , d2 , and ai are being estimated. The null and the alternative hypothesis for the existence of unit root in variable Xt is: H0 : d 2 ¼ 0
He : d2 p0 lags, is tested by the LR test statistic, which is computed as: LR ¼ 2ðl0 l1 Þ where li (i = 0, 1) is the log likelihood reported in the VAR with pi (i = 0, 1) lags. The results showed that the value p = 4 is the appropriate specification for the above relationship. The order of r is determined by using the likelihood ratio (LR) trace test statistic suggested by Johansen (1988). ktrace ðq; nÞ ¼ T
k X
lnð1 ^ki Þ
ð3Þ
i¼qþ1
for r = 0, 1, 2,…,k)1, T = the number of observation used for estimation ^ ki is the ith largest estimated eigenvalue. Critical values for the trace statistic defined by Equation (3) are 24.05, and 21.46 for Ho: r = 0, 12.36 and 10.75 for Ho: r 1 and 4.16, and 3.04 for Ho: r 2 at the significance level 5% and 10% respectively as reported by Osterwald-Lenum (1992). The maximum eigenvalue LR test statistic as suggested by Johansen is: kmax ðq; q þ 1Þ ¼ T lnð1 ^kqþ1 Þ
ð4Þ
The trace statistic either rejects the null hypothesis of no cointegration among the variables (r=0) or does not reject the null hypothesis that there is one cointegrating relation between the variables (r £ 1). The results that appear in Table II suggest that the number of statistically significant cointegration vectors is equal to 1 and are the following: LEG=3.2033LSD 0.97943LBD
ð5Þ
The coefficients estimations in equilibrium relationships, which are basically the long-term estimated elasticities relatively to economic growth, suggest that stock market functions are elastic whereas the banking sector development is inelastic. According to the signs of the vector cointegration components and based on the basis of economic theory the above relationships can be used as an error correction mechanism in a VAR model.
121 Table II. Johansen and Juselious cointegration tests variables LEG, LSD, LBD maximum lag in VAR = 4 Null
Eigenvalues r=0 r=1 r=2 Trace statistic r=0 r=1 r=2
Alternative
Eigenvalue
Critical values 95%
90%
r=1 r=2 r=3
31.0598 6.9229 3.7974
17.6800 11.0300 4.1600
15.5700 9.2800 3.0400
r=1 r=2 r=3
41.7801 10.7203 3.7974
24.0500 12.3600 4.1600
21.4600 10.7500 3.0400
Notes: Critical values are taken from Osterwald–Lenum (1992). r denote the number of cointegrated vectors. Schwartz Criteria (SC) was used to select the number of lags required in the cointegration test. The computed Ljung – Box Q – statistics indicate that the residuals are white noise.
6. VAR model with an error correction mechanism After determining that the logarithms of the model variables are cointegrated, we must estimate afterwards a VAR model in which we shall include a mechanism of error correction model (MEC). The error-correction model arises from the long-run cointegration relationship and has the following form: 4LEGt ¼ laggedð4LEGt ; 4LSDt ; 4LBDt Þ þ kut1 þ Vt
ð6Þ
where D is referred to the first differences of the variables. ut)1 are the estimated residuals from the cointegrated regression (long-run relationship) )1SD
Notes: The figures in parentheses after the F-statistics are the numbers of lags and leads for the dependent variables and causal variables. Critical value: (2,180) 2.30, 3.00, 4.61 (3,180) 2.08, 2.60, 3.78 (4,180) 1.94, 2.37, 3.32 for 10%, 5%, 1% significance levels respectively. *, **, and *** indicate the 10, 5 and 1% significance levels respectively. The underlined figures indicate that the statistical criteria are not satisfied at the 10% level.
124 • There is no causal relationship between functions of stock market (SD) and economic growth (EG). • There is a bilateral causal relationship between economic growth (EG) and banking sector development (BD). • There is a bilateral causal relationship between banking sector development (BD) and economic growth (EG). • There is no causal relationship between functions of stock market (SD) and banking sector development (BD). • There is unidirectional causal relationship between banking sector development (BD) and functions of stock market (SD) with direction from banking sector development to functions of stock market. Furthermore, from Table IV we observe the following: The result of normality, in the equation we are examining the causality between economic growth and banking sector development, is troublesome. Also, there is a troublesome result in heteroscedasticity in the equation where we examine the causality between stock market development and banking sector development.
8. Summary and conclusions This paper empirically examines the causal relationship between stock, credit market and economic development using a multivariate autoregressive VAR model for Greece in the examined period 1988:1–2002:12. The results of cointegration analysis suggested that there is one cointegrated vector among the examined variables while Granger causality test have shown that there is a causal relationship between stock market development and economic growth as well as banking sector development and economic growth. Cointegration techniques permit the estimation and testing of the long-run equilibrium relationships, as suggested by economic theory. Vector error correction (VEC) models provide a way of combining both the dynamics of the short run (changes) and long-run (levels) adjustment processes simultaneously. Using data from stock market, banking sector development and economic growth for Greece, the long-run economic relationships among these variables have been estimated. Prior testing for cointegration among a set of variables, the ADF test of non-stationarity is performed to determine the order of integration of the individual time series. Johansen’s maximum likelihood procedure is used for estimation and testing of the cointegrating relations based on vector autoregressive models. The methods used, and the results presented in this paper, provide some useful insights into the effects of
125 the functions of stock market, credit market and economic development and how they are interrelated in the economic performance of Greece. The existence of a long-run equilibrium relationship among stock market, banking sector development and economic growth in the model appear to be supported by the data used for the examined period. According to the theory of cointegration, the estimated cointegrating residual should appear as the error correction term in a dynamic VEC model. An important finding from the dynamic models presented is that the error correction terms are negative and statistically significant. All regressors in the VEC models are statistically significant, there is no evidence of any problems associated with serial correlation, functional form, normality, heteroscedasticity. Given a statistically significant error correction model in a dynamic VEC model, it can be interpreted as evidence supporting cointegration, which suggests the existence of an equilibrium long-run relationship among the variables of the model. The results of causality analysis show that there is a unidirectional causality relationship between economic growth and stock market development with direction from economic growth to the functions of stock market. Also, between banking sector development and stock market development there is a causal relationship with direction from banking sector to stock market development. Furthermore, the results of Granger causality suggest that there is a bilateral causal relationship between the banking sector development and economic growth. Finally, the results of this paper show that there is no Granger causality between the functions of stock market and banking sector development for the examined country. Generally speaking, we can reach a synopsis by stating that even though many empirical evidence found a causal relation from growth to finance, as far as Greece is concerned, our work using data from 1988:1 to 2002:12 gives light also to a more interesting relation. The most probable explanation of our findings can be that Greece, during the last years, is a member state of European Monetary Union and also that is going to host the Olympic Games in 2004.These two events have changed the way of Greek economy using rapid processes compared to those of past years. References Akaike, H. (1974), ÔA new look at the statistical model identificationÕ, IEEE Transaction on Automatic Control AC-19, 716–723. Arestis, P. and Demetriades, P.O. (1998) ÔFinance and growth : is Schumpeter ‘Right’?Õ, Ana´lise Econo´mica 16(30), 5–21. Beck, T. and Levine, R. (2002), ‘Stock markets, banks, and growth: panel evidence’, Working Paper 9082, National Bureau of Economic Research, Cambridge, MA 02138, July 2002. Available from: http://www.nber.org/papers/w9082.pdf. Boyd, J. and Smith, B. (1996) ÔThe coevolution of the real and financial sectors in the growth processÕ, The World Bank Economic Review 10, 371–396.
126 Caporale, G.M., Howells, P.G.A. and Soliman, A.M. (2002), ‘Stock market development and economic growth: the causal linkage’, Working Paper Series No. 2, East London Business School. Dickey, D.A. and Fuller, W.A. (1979) ÔDistribution of the estimators for autoregressive time series with a unit rootÕ, Journal of the American Statistical Association 74, 427–431. Engle, R.F. and Yoo, B.S. (1987) ÔForecasting and testing in cointegrated systemsÕ, Journal of Econometrics 35, 143–159. Garganas, N. (2003), ‘The changing structures of European banking: the Greek experience’, Speech addressed by Nicholas C. Garganas on 14/2/2003. Available from: http:// www.bankofgreece.gr/en/announcements/text_speech.asp?speechid=32. Gerschenkron, A. (1962), Economic Backwardness in Historical Perspective, a Book of Essays, Cambridge, MA, Harvard University Press. Godfrey, L.G. (1978), ÔTesting against general autoregressive and moving average error models when the regressors include lagged dependent variablesÕ, Econometrica 46, 1293– 1301. Granger, C.W.J. (1986), ÔDevelopments in the study of cointegrated economic variablesÕ, Oxford Bulletin of Economics and Statistics 48, 213–228 Greenwood, J. and Jovanovic, B. (1990) ÔFinancial development, growth and the distribution of incomeÕ, Journal of Political Economy 98, 1076–1107. Gujarati, D.N. (2003), Basic Econometrics. 4th ed., McGraw-Hill, New York. Jarque, C. and Bera, A. (1980) ÔEfficient tests for normality, homoscedasticity and serial independence of regression residualsÕ, Economics Letters 6, 255–259. Johansen, S. (1988), ÔStatistical analysis of cointegration vectorsÕ, Journal of Economic Dynamics and Control 12, 231–254. Johansen, S. and Juselious, K. (1990) ÔMaximum likelihood estimation and inference on cointegration with applications to the demand for the moneyÕ, Oxford Bulletin of Economics and Statistics 52, 169–210. Johansen, S. and Juselious, K. (1992) ÔTesting structural hypotheses in a multivariate cointegration analysis at the purchasing power parity and the uncovered interest parity for the UKÕ, Journal of Econometrics 53, 211–244. Jung, W.S. (1986), ÔFinancial development and economic growth: international evidenceÕ, Economic Development of Cultural Change 34, 336–346. Karatzas, T. (2000), ‘The Greek banking system after EMU’, in Speech presented at the economist conference on ‘‘Greece at the threshold of EMU’’, The Brewery, London, 21 February, 2000. Available from: http://www.capitallink.com/events/presentations/theodoros.htm. Kar, M. and Pentecost, E.J. (2000), ‘Financial development and economic growth in Turkey: further evidence on the causality issue’, Economic Research Paper No. 00/27, Department of Economics, Loughborough University. Available from: http://www.lboro.ac.uk/departments/ec/Dept%20Research%20papers/Papers2000/00-27/erp00-27.pdf. King, R.G. and Levine, R. (1993) ÔFinance and growth: Schumpeter might be rightÕ, Quarterly Journal of Economics 108, 717–737. Levine, R. and Zervos, S. (1993) ÔWhat we have learned about policy and growth from crosscountry regressions?Õ, American Economic Review, Papers and Proceedings 83, 426–430. Lewis, W.A. (1955), The Theory of Economic Growth, George Allen and Unwin London. Luintel, K.B. and Khan, M. (1999) ÔA quantitative reassessment of the finance-growth nexus: evidence from a multivariate VARÕ, Journal of Development Economics 60, 381–405. Maddala, G.S. (1992), Introduction to Econometrics, 2nd ed. New Jersey, Prentice Hall. Mills, J. and Prasad, K. (1992) ÔA comparison of model selection criteriaÕ, Econometric Reviews 11, 201–233.
127 Nelson, C.R. and Plosser, C.I. (1982) ÔTrends and random walks in macroeconomic time series: some evidence and implicationsÕ, Journal of Monetary Economics 10, 139–162. Osterwald-Lenum, M. (1992), ÔA note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statisticsÕ, Oxford Bulletin of Economics and Statistics 54, 461–472. Ramsey, J.B. (1969), ‘Test for specification errors in classical linear least squares regression analysis’, Journal of the Royal Statistical Society B 350–371. Schumpeter, J.A. (1912), Theorie der Wirtschaftlichen Entwicklung (The Theory of Economic Development). Leipzig: Dunker and Humblot, Translated by Redvers Opie, Cambridge, MA, Harvard University Press, 1934. Schwartz, R. (1978), ÔEstimating the dimension of a modelÕ, Annuals of Statistics 6, 461–464 Shan, J.Z. and Morris, A.G. (2002) ÔDoes financial development ‘Lead’ economic growth?Õ, International Review of Applied Economics 16(2), 154–169. Sims, C. (1980), ÔMacroeconomics and realityÕ, Econometrica 48, 1–48. Wachtel, P. and Rousseau, P.L. (1995), Financial intermediation and economic growth: a historical comparison of the United States, the United Kingdom, and Canada, in: Bordo, M.D. and Sylla, R. (eds), Anglo-American Financial Systems, Homewood, IL, Business One Irwin.
