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The Effect of US and European Stock Exchanges on Greece’s
Stock Market: A VAR Approach
by
Nikolaos Veraros, CFAa and Evangelia Kasimatib
a
Investments and Finance Ltd., 10 Skouze, Piraeus 185 36, Greece
b
Corresponding author. Department of Economics and International Development,
University of Bath, Claverton Down, Bath BA2 7AY, UK
E-mail: E.Kasimati@bath.ac.uk, Tel: +44 (0) 1225 384864, Fax: +44 (0) 1225 383423
Abstract
Through a Structural Vector Autoregression model (SVAR) the effect of the European
and US stock exchanges on the Athens Stock Exchange (ASE) is examined. Consistent
to other studies on the effect of large stock markets on smaller regional ones, we find
that both the European and US stock exchanges affect ASE, with the European markets
exhibiting larger and more immediate effect.
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1. Introduction
A significant trend over the last decade in the financial community is the increased
interaction and interdependence among different stock exchanges. Fund managers in
developed countries are taking benefit of the gradual lifting of the restrictions on the
capital flows and the relaxation of exchange controls in many countries abroad, thus
allocating a higher share of their funds to international stocks. An important aspect of
international investing is the degree of correlation of the smaller regional markets with
the major US and European markets, whose investors are trying to diversify
internationally their portfolios. As summarized in academic literature (Cheung, 1992,
Speidell, 2004) the less than perfect correlation adds diversification benefits to the
investors.
This paper examines the dependence of the ASE with the major European as well as the
US stock exchange. We adopt the index FTSE Euro 100 (FTSE) as a proxy for the
European stock exchanges and the S&P500 (S&P) for the US. FTSE Euro 100 includes
the 100 most highly capitalized blue chip companies resident and incorporated within
countries participating in the EMU. We use a SVAR model in which we incorporate
structural restrictions related to the interdependence among the different markets.
Variations of VAR models have increasingly been among the most popular statistical
instrument for carrying out such studies. Berument (2005) found that the Turkish stock
exchange is affected by US for up to four days. Cha (2000) suggested that stock
exchanges in Hong Kong, Korea, Singapore and Taiwan are affected by US and Japan,
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with the degree of relation increasing since the outbreak of the Asian financial crisis in
1997. Ghosh (1999) used an Error Correction Model (ECM) to find that Indonesia,
Philippines and Singapore are linked mostly with the stock exchange of Japan, whereas
Hong Kong, India, Korea and Malaysia mostly with the US. Finally Cheung (1992),
through a single equation model, found that US stock exchange led most of the Asia-
Pacific markets in the period 1978 – 1988.
2. Methodology & Results
Similar to Cha (2000) and Cheung (1992), we use weekly data for the three indices of
ASE, FTSE and S&P from January 1999 up to July 2005, a total of 341 observations.
According to Veraros et al (2004), the use of weekly as opposed to daily data is
preferred when referring to ASE time series, particularly before year 2000, since daily
data could be distorted by the existence of restrictive rules in the change of share prices
(initial daily limit of +/- 8.0% to be expanded later on). This problem is even more
acute when comparing ASE time series with other stock exchanges which do not bear
such restrictions in daily volatility. In addition, the use of weekly data mitigates the
problem of correcting the daily data for non-coinciding closed market dates. No Greek
stock participates in the particular FTSE index, thus eliminating the possibility of
multicollinearity due to structural causes.
The visual observation of the three time series as well as their autocorrelation graphs
for k=52 suggest that the data are non-stationary. Accordingly, we use the Augmented
Dickey-Fuller (ADF) test to check for the existence of unit roots.
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Given that the autocorrelations graphs have indicated the existence of non-stationary
data we reduce the testing procedure table suggested by Dickey and Fuller (1981) down
to three alternative scenarios:
1. unit root with drift and time trend
2. unit root with drift
3. unit root
In order to choose the optimum lag length we adopt the Schwert formula included in
Harris (1995): l = int{12(T/100)1/4}. In our case, this suggests 16 lags
The results of the ADF are presented in Table 1:
Insert Table 1
The conclusions are identical for all three indices. The existence of unit root cannot be
rejected even at 10% significance level, whereas both drift and time trend seem to have
zero effect.
We then move into testing the ADF at the first difference level (Table 2). Given our
findings so far, we focus on first order unit root without a drift and time trend:
Insert Table 2
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For all three indices the null assumption of the existence of unit root is rejected at the
1% level of significance. The autocorrelations graphs also suggest that the data are
stationary at first difference level. Therefore the conclusion is that all three indices are
I(1).
Subsequently, we examine the cointegration properties of the three time series. As
described in Table 3, our Johansen Test (again for 16 lags) suggests that they are C(1).
Insert Table 3
For our model specification we favor a choice of a dynamic system as opposed to a
single equation model. We base our analysis on measuring the effect of a shock
introduced in our system to ASE behavior. This is performed through variance
decomposition and impulse diagrams. We also assume that whereas the ASE (domestic
market) is affected by both FTSE and S&P (international markets), the small size of the
domestic market does not allow for any effect on the international markets. This
necessitates the imposing of structural restrictions on our model’s specification.
Similar to Berument (2005) we use a SVAR model which allows for the required
structural decompositions. To overcome the problem of unit roots in the time series we
use the returns of the indices which are a kind of normalized first differences of the
absolute values, thus transforming the series from I(1) into stationary. Given the
required non singular property of the matrix including the restrictions for our
factorization we develop two separate pairs of equations, in the first we combine ASE
with FTSE and in the second ASE with S&P. The third possible pair of S&P with
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FTSE is irrelevant to our analysis. After experimentation we set lags equal to three,
accounting for 3 weeks or 21 days in total.
The general structure of the model is:
A( L) y (t ) = ε (t ) (1)
where
A(L) is an m x m matrix polynomial in the lag operator L,
y(t) is the m x 1 observations vector, and
ε(t) is the m x 1 vector of structural disturbances.
In order to incorporate the restriction of the non effect of ASE on the international
market we further specify the general model as follows
y (t ) A ( L) 0 ε (t )
y (t ) = 1 , A( L) = 11 , ε (t ) = 1 (2)
y 2 (t ) A21 ( L) A22 ( L) ε 2 (t )
y1 represents ASE and y2 either FTSE or S&P. Effectively, we introduce a block
exogeneity in our model by setting A12(L) equal to zero.
Our variance decomposition is presented in Table 4.
Insert Table 4
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We observe that FTSE has a more significant effect on ASE than S&P. We also
observe that whereas FTSE reaches its top effect on week 2 and therefore remains
stable, S&P tops its effect in week 4, thus suggesting a longer lag in its interaction with
ASE.
The impulse graphs (Figures 1 and 2) reconfirm the above observations.
Insert Figures 1 & 2
Again, S&P seems to have an effect lower in magnitude and higher in lag order than
FTSE.
3. Conclusions
We have used a SVAR model to measure the extent of which the ASE is affected by the
other European stock exchanges (proxied by FTSE Euro 100) and the US stock market.
Our analysis indicates a somewhat stronger effect of the European market, whereas the
US seems to have a higher time lag. The higher and more immediate effect of the
European markets should be expected as Greece is part of the EMU, having adopted
Euro as its currency. Besides, Greek stocks participate in various European indices,
albeit with a small weight, which are treated as benchmarks for international investing,
thus increasing the correlation between ASE and the European stock exchanges.
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References
Berument, H., Ince O. (2005) Effect of S&P500’s return on emerging markets: Turkish
experience, Applied Financial Economics Letters 1, 59-64.
Cha, B., Oh, S. (2000) The relationship between equity markets and the Pacific Basin’s
emerging equity markets, International Review of Economics and Finance 9, 299-322.
Cheung, Y.L., Mak, S.C. (1992) The international transmission of stock market
fluctuation between the developed markets and the Asian-Pacific markets, Applied
Financial Economics 2, 43-47.
Dickey, D.A., Fuller, W.A. (1981) Likelihood ratio statistics for autoregressive time
series with a unit root, Econometrica 49, 1057-1072.
Ghosh, A., Saidi, R., Johnson, K.H. (1999) Who moves the Asia-Pacific stock markets
– US or Japan? Empirical evidence based on the theory of cointegration, The Financial
Review 34, 159-170.
Harris, R. (1995) Using cointegration analysis in econometric modeling, Prentice Hall
1st edition.
Patterson, K. (2000) An introduction to applied econometrics: a time series approach,
Palgrave 1st edition.
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Speidell, L., Xing, H. (2004) One world – The case of global portfolio, Journal of
Investing 13, 5-13.
Veraros, N., Kasimati, E., Dawson, P. (2004) The 2004 Olympic Games announcement
and its effect on the Athens and Milan stock exchanges, Applied Economics Letters 11,
749-753.
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Table 1
Augmented Dickey-Fuller Test of Unit Roots on levels
Unit root with drift & Unit root with drift Unit root
time trend
ASE
Unit root ADF: -1.476 ADF: -1.422 ADF: -0.6592
10% critical: -3.135 10% critical: -2.571 10% critical: -1.616
Drift p-value: 0.231 p-value: 0.2032
Time trend p-value: 0.485
FTSE
Unit root ADF: -1.673 ADF: -1.2752 ADF: -0.424
10% critical: -3.135 10% critical: -2.571 10% critical: -1.616
Drift p-value: 0.119 p-value: 0.227
Time trend p-value: 0.274
S&P
Unit root ADF: -0.854 ADF: -1.277 ADF: -0.451
10% critical: -3.135 10% critical: -2.571 10% critical: -1.616
Drift p-value: 0.523 p-value: 0.222
Time trend p-value: 0.853
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Table 2
Augmented Dickey-Fuller Test of Unit Roots on first differences
ASE FTSE S&P
ADF: -3.462 -3.639 -4.582
1% critical: -2.572 -2.572 -2.572
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Table 3
Johansen Test of level of cointegration
Likelihood 5 Percent 1 Percent Hypothesized
Eigenvalue Ratio Critical Critical No. of CE(s)
Value Value
0.056660 27.95945 24.31 29.75 None *
0.027266 9.060908 12.53 16.31 At most 1
0.000321 0.104146 3.84 6.51 At most 2
*(**) denotes rejection of the hypothesis at 5%(1%) significance level
L.R. test indicates 1 cointegrating equation(s) at 5% significance level
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Table 4
Variance Decomposition of one SD innovation in FTSE and S&P
Effect of one SD change of FTSE on ASE Effect of one SD change of S&P on ASE
Period S.E. ASER FTSER S.E. ASER SPR
1 0.038023 100.0000 0.000000 0.038115 100.0000 0.000000
2 0.038693 96.70793 3.292074 0.038508 98.07588 1.924119
3 0.039209 96.73709 3.262913 0.039150 97.23717 2.762825
4 0.039293 96.74192 3.258079 0.039293 96.91122 3.088777
5 0.039305 96.73636 3.263637 0.039302 96.91247 3.087528
6 0.039316 96.73782 3.262180 0.039318 96.90302 3.096984
7 0.039316 96.73710 3.262898 0.039319 96.90159 3.098406
8 0.039317 96.73710 3.262902 0.039320 96.90130 3.098705
9 0.039317 96.73709 3.262915 0.039320 96.90088 3.099119
10 0.039317 96.73709 3.262914 0.039320 96.90088 3.099123
11 0.039317 96.73708 3.262918 0.039320 96.90086 3.099141
12 0.039317 96.73708 3.262918 0.039320 96.90085 3.099145
13 0.039317 96.73708 3.262918 0.039320 96.90085 3.099145
14 0.039317 96.73708 3.262918 0.039320 96.90085 3.099146
15 0.039317 96.73708 3.262918 0.039320 96.90085 3.099146
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.012
.008
.004
.000
-.004
-.008
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Fig. 1. Response of ASE to Cholesky One S.D. FTSE Innovation
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.012
.008
.004
.000
-.004
-.008
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Fig. 2. Response of ASE to Cholesky One S.D. S&P Innovation
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