EXPLORING INTEGRATION BETWEEN SELECTED EUROPEAN STOCK MARKET INDEXES AND SENSEX
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Pranjana ? Vol 11, No 2, Jul-Dec, 2008
EXPLORING INTEGRATION BETWEEN
SELECTED EUROPEAN STOCK MARKET
INDEXES AND SENSEX
Saif Siddiqui 1
Abstract 1. Introduction
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Due to globalization, economic integration
Investors and portfolio managers are interested
among countries and their financial
markets is evident. The interdependency in understanding the intensity of stock market
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between Indian and other European stock integration for diversification motives. In an ever-
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markets has also increased. This paper changing economic environment, knowledge of
examines the relationships between
selected European stock markets and the international stock market structure is
SENSEX. It covers the recent period, 19/ important for both. An international investor who
10/1999 to 25/04/2008,using daily closing is willing to make portfolio investments in different
data of nine stock markets to investigate.
The research methodology employed
stock markets, it is important to know if
includes testing for stationarity, diversification can give some advantage or not.
implementation of the Granger Causality If stock markets of different countries move
test and Johansen Co integration test. together, then investing in different stock markets
Stock markets under study are found to be
integrated. The degree of correlation would not generate any long-term gain to portfolio
between the markets varies between low diversification. Correlation between returns of
to high. The findings also proved that stock stock indexes can be used as the main indicator
markets return are not normally distributed
and show stochastic pattern in return.
of diversification of investment
Furthermore, it provided that no stock
market is playing a very dominant role in A comprehensive study on European stock market
influencing other markets. It is concluded integration carries a lot of importance in the
that SENSEX granger cause all European
stock market indexes under study. None, present day situation when their trade and
but ATX of Austria, do the same to economy are more opened for India. Policy-
SENSEX. makers need to understand the emerging stock
Keywords: Stock Market Integration, Unit market interdependence.
Root Test, Cointegration Test, Granger A study on European stock market integration,
Causality Test
either theoretical, or empirical, carries a lot of
significance. Thus, it becomes essential to examine
the interdependence between different European
markets and their relation with India
2. Literature Review
1. Lecturer, Centre for Management Studies, Various studies undertaken in different parts of
Jamia Millia Islamia Jamia Nagar, New the world regarding linkages between the stock
Delhi, India markets are mentioned as under:
79Saif Siddiqui
Eun and Shim (1989) analyzed daily stock market returns of Australia, Hong Kong,
Japan, France, Canada, Switzerland, Germany, US and the UK. They found existence of
substantial interdependence among the national stock markets with US being the most
influential market.
Using daily and intra day price and stock returns data, Hamao, Masulis and Ng (1990)
find that there are significant spillover effects from the US and the UK stock markets to
the Japanese market but not the other way round. Rao & Naik (1990) got same result
when they attempted to examine the inter-relatedness of US, Japanese and Indian Stock
Markets. Their findings pointed out that Japanese market acts like an independent factor
in relation to the US and Indian stock markets. Fischer and Palasvirta (1990) also
found a high level of interdependence between stock markets of 23 countries , they
further concluded that US index prices lead almost every country index in the sample.
Mathur and Subrahmanyam (1990) used the concept of Granger causality to examine
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interdependencies among the stock market indices for four Nordic countries and the U.S.
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The results indicate that the Nordic stock markets are less than fully integrated. Further
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Malkamäki (1992) examines the interdependence of stock markets in Sweden, Finland
and their biggest trading partners in the period 1974–89 and finds that the Scandinavian
markets seem to be led by the German and the UK market.
Hassan and Naka (1996) investigates the dynamic linkages among the U.S., Japan,
U.K. and German stock market and found significant evidence in support of both short-
run and long run relationships among these four stock market indices. Sewell et al.
(1996) also examined five Pacific Rim countries and the US, documenting evidence of
varying degrees of market co-movements. Markellos and Siriopoulos (1997) too
examined the diversification benefits available to U.S. and Japanese investors over the
period 1974-94 in seven of the smaller European stock markets. Cointegration analysis
found no significant common trend shared between the U.S. and Japanese markets.
Palac-McMiken (1997) uses the monthly ASEAN market indices (Indonesia, Malaysia,
the Philippines, Singapore, and Thailand) between 1987 and 1995 and finds that with
the exception of Indonesia, all the markets are linked with each other. Kanas (1998)
discovered that the US stock market does not have pair wise co-integration with any of
the European markets. These results imply that there are potential benefits from diversifying
in US stocks as well as stocks in European markets. Elyasiani et al. (1998) found No
significant interdependence between the Sri Lankan market and the equity markets of the
US and the Asian markets considered. In their paper, Gerrits and Yuce (1999) test the
interdependence between stock prices in Germany, the UK, the Netherlands and the US.
Results of the tests show that the US exerts a significant impact on European markets.
Moreover, the three European markets influence each other in the short and long run..On
the other hand Christofi and Pericli (1999) investigate the short turn dynamics between
five major Latin American stock markets (Argentina, Brazil, Chile, Columbia, and Mexico)
from 1992 to 1997. They find significant first and second moment time dependencies.
Cross spectral analysis is applied by Smith (1999) to six of the G-7 markets to determine
whether frequency domain correlations have increased post-crash relative to the pre-
crash period. The results indicate that correlations have increased for most of the markets
studied. Scheicher (2001) studied the regional and global integration of stock markets
80Pranjana ? Vol 11, No 2, Jul-Dec, 2008
in Hungary, Poland and the Czech Republic. The empirical result is the existence of
limited interaction.
Kumar (2002), in his study, confirmed that stock index of Indian stock market was not
co- integrated with that of developed markets. Mishra (2002) investigated the international
integration of Indian stock market. He found no co integrating vector between BSE and
NASDAQ indices that signifies there was no long-run relationship between these two
stock exchanges. Darrat and Zhong (2002) examined the linkages between eleven
emerging Asia-Pacific markets with US and Japan. They argued that the effect of the
movements in the Japan market on the Asia-Pacific region is only transitory. Bessler and
Yang (2003) concluded that The US market is highly influenced by its own historical
innovations, but it is also influenced by market innovations from the UK, Switzerland,
Hong Kong, France and Germany. Darrat and Benkato (2003) analyzed stock returns
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and volatility relations between the Istanbul Stock Exchange (ISE) and the stock markets
in the US, the UK, Japan and Germany. They realized that the two matured markets of
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the US and the UK shoulder significant responsibility for the stability and financial health
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of smaller emerging markets like the ISE.
Hatemi and Roca (2004) examines the equity market price interaction between Australia
and the European Union . they concluded that Australia also had no causal links with
Germany and France but it had with the UK, with causality running from the UK to
Australia but not vice-versa.
After analyzing markets of 23 different countries Mukherjee and Mishra (2007) identified
increasing tendency of integration among the markets and discovered that countries of
same region are found to be more integrated than others.
3. Methodology
3.1 Sample
The present study is based on secondary data, which covers the most recent period using
daily closing figure from 19/10/1999 to 25/04/2008. Table 1 shows the general stock
indices of the countries, which make up the sample of the present study. The data is
taken from Yahoo Finance.
Table No.1: Stock Exchanges and Stock Indices under study
S. No Country Index Symbol
1 AUSTRIA ATX ATX
2 BELGIUM BEL-20 BEL
3 FRANCE CAC-40 CAC
4 GERMANY DAX DAX
5 NETHERLAND AEX AEX
6 ITALY MIBTEL MIBTEL
7 SWISZERLAND SWISS MARKET SWISS
8 UK FTSE 100 FTSE
9 INDIA SENSEX SENSEX
81Saif Siddiqui
3.2 Methodology
After the review of literature, it is evident that econometric methods are the most useful
method to analyse and interpret data. These methods were used to test correlation,
stationarity of time series, co integration and causalities between the stock markets. The
computations in present study were aided by the use of Eviews 5.1. In this study, following
test were undertaken:
Pearson correlation is used to find correlation between the stock markets returns.
Testing for stationarity (unit root test) is done by using, both the Augmented Dickey-
Fuller and the Phillips-Perron tests.
Johansen Cointegration test is used for pinpointing the long run relationships among
the markets under study.
For Causality Test, Gragner test is used, which identify that whether one series has
significant explanatory power for another series
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4. Analysis of Empirical Results
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4.1. Descriptive Statistics
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Table 2 provides summary statistics, namely sample means, minimums, maximums,
medians, standard deviations, skewness, kurtosis and the Jarque- Bera tests.
Table No 2: Characteristics of Distributions of the Stock Indices under study
ATX BEL CAC DAX AEX MIBTEL SWISS FTSE SENSEX
Mean 2334.31 3056.60 4605.50 5299.55 451.15 25170.75 6814.06 5334.69 7264.71
Median 1772.26 2934.015 4578.07 5202.48 450.145 25122 6774.25 5371.1 5356.4
Maximum 4981.87 4756.82 6922.33 8105.69 701.56 34365 9531.5 6798.1 20873.33
Minimum 1003.72 1426.59 2403.04 2202.96 218.44 15125 3675.4 3287 2600.12
Std. Dev. 1323.98 780.28 1056.12 1515.02 113.81 5040.07 1383.82 871.17 4638.94
Skewness 0.6124 0.3673 0.0841 0.0866 0.4166 0.0115 0.0346 -0.2287 1.1484
Kurtosis 1.763166 2.265991 1.966252 1.889278 2.268009 1.777839 1.951179 1.80477 3.182092
Jarque-Bera 263.1042 93.6397 95.2520 109.7292 106.8036 129.7466 95.9350 142.2098 460.9657
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Observations 2084 2084 2084 2084 2084 2084 2084 2084 2084
It is noted that standard deviation in MIBTEL’s return is highest, thus showing the highest
volatility during the period of study. SENSEX closely followed MIBTEL in terms of volatility.
FTSE, BEL and AEX are found to be least volatile during the period under consideration.
It is further noted that all but one (FTSE) shows negative skewness. The values of skewness
and kurtosis shown in the table suggest that the stock returns are not normally distributed,
which is also verified with the Jarque-Bera statistic, which is a test statistic for testing
whether the series is normally distributed. The hypothesis of normal distribution is also
rejected at the conventional 5% level
4.2. Correlation
Table 3 shows the return correlations among the various indices under study.
82Pranjana ? Vol 11, No 2, Jul-Dec, 2008
Table 3: Correlations of Returns of the Stock Indices under study
ATX BEL CAC DAX AEX MIBTEL SWISS FTSE SENSEX
ATX 1 0.8901 0.3705 0.5077 0.0622 0.5577 0.6600 0.5125 0.9276
BEL 0.8901 1 0.7143 0.7913 0.4753 0.8317 0.8975 0.8150 0.8622
CAC 0.3705 0.7143 1 0.9523 0.9373 0.9561 0.8857 0.9625 0.4571
DAX 0.5077 0.7913 0.9523 1 0.8621 0.9268 0.9212 0.9685 0.6331
AEX 0.0622 0.4753 0.9373 0.8621 1 0.8178 0.7474 0.8680 0.1866
MIBTEL 0.5577 0.8317 0.9561 0.9268 0.8178 1 0.9247 0.9531 0.5879
SWISS 0.6600 0.8975 0.8857 0.9212 0.7474 0.9247 1 0.9486 0.6963
FTSE 0.5125 0.8150 0.9625 0.9685 0.8680 0.9531 0.9486 1 0.5853
SENSEX 0.9276 0.8622 0.4571 0.6331 0.1866 0.5879 0.6963 0.5853 1
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It can be clearly seen that the correlations among the returns of the countries under study
is positive and varies from low to high. It may be seen as first indication for the existence
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of interdependency among them. The highest of correlations is between DAX and FTSE
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(over 96%) and the lowest between ATX and AEX (about 6%). The SENSEX is found to
be highly correlated with ATX and BEL. It is relatively lesser correlated with DAX, MIBTEL
and FTSE. It is further noted that SENSEX is least correlated with AEX. The correlations
need to be verified by the Granger causality test and the co-integration test also
4.3. Unit root test
A unit root test tests time series for stationarity and find out whether a time series variable
is non-stationary.The most appropriate tests are i the Augmented Dickey-Fuller(ADF) test
and Phillips-Perron (PP) test. Both tests use the existence of a unit root as the null
hypothesis.
4.3.1. Augmented Dickey-Fuller (ADF Test)
Table 4: Augmented Dickey-Fuller (ADF Test)
Level First difference
Symbol Lag length ADF statistic p-value Lag length ADF statistic p-value
ATX 0 -1.015419 0.7498 0 -46.55602 0.0001
BEL 0 -1.214439 0.6704 0 -43.75465 0.0001
CAC 0 -0.988963 0.7592 0 -48.12285 0.0001
DAX 0 -0.855045 0.8025 0 -47.69032 0.0001
AEX 0 -0.565746 0.8755 0 -47.24343 0.0001
MIBTEL 0 -0.800337 0.8184 0 -47.25663 0.0001
SWISS 0 -1.249954 0.6546 0 -45.68146 0.0001
FTSE 3 -1.139961 0.7019 2 -29.94173 0.0000
SENSEX 1 -2.005955 0.2844
Exogenous: Constant
Lag Length: Automatic based on SIC, MAXLAG=25
*MacKinnon (1996) one-sided p-values.
Deterministic terms: Intercept
83Saif Siddiqui
It appears from Table 4; the null hypothesis that there is a unit root cannot be rejected for
all the variables using intercept terms in the test equation in the level form. But inversely,
for the first differences of all the variables the null hypothesis of a unit root is strongly
rejected. So it can be said that all the variables contain a unit root, that is, non-stationary
in their level forms, but stationary in their first differenced forms.
4.3.2. Phillips-Perron Test
The Phillips-Perron test is less restrictive and provides an alternative way for checking the
stationarity of a time-series. From Table 5 same conclusions like the Dickey-Fuller tests are
drawn.
Table 5: Phillips-Perron Test
Symbol Level First difference
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Bandwidth P-P test p-value Bandwidth P-P test p-value
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statistic statistic
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ATX 3 -1.016383 0.7495 3 -46.54657 0.0001
BEL 15 -1.202172 0.6757 17 -43.72881 0.0001
CAC 20 -0.759601 0.8295 20 -48.64740 0.0001
DAX 12 -0.794634 0.8199 11 -47.70697 0.0001
AEX 15 -0.476564 0.8932 14 -47.28649 0.0001
MIBTEL 3 -0.800264 0.8184 4 -47.23183 0.0001
SWISS 20 -1.184384 0.6834 21 -45.76663 0.0001
FTSE 15 -1.201784 0.6759 14 -50.02935 0.0001
SENSEX 20 -2.172727 0.2166 24 -41.47165 0.0000
Exogenous: Constant
Bandwidth: Newey-West using Bartlett kernel
MacKinnon (1996) one-sided p-values
Deterministic terms: Intercept
The null hypothesis that there is a unit root cannot be rejected for all the variables using
intercept terms in the test equation in the level form. But, as concluded in ADF Test, for
the first differences of all the variables the null hypothesis of a unit root is strongly rejected.
It is again verified that all the variables contain a unit root, that is, non-stationary in their
level forms, but stationary in their first differenced forms. Therefore, all conditions are
present for implementing co-integration tests as series are confirmed to be non-stationary.
4.4. Co-integration test
Cointegration is an econometric property of the time series . If two or more series are
themselves non-stationary, but a linear combination of them is stationary, then the series
are said to be cointegrated.Engle & Granger (1987) also state that a linear combination
of two or more non-stationary time series can be said to be stationary. If a stationary
linear combination exists is the non-stationary time series co-integrated. It test the null
hypothesis, that there exists none co-integrating equations.
84Pranjana ? Vol 11, No 2, Jul-Dec, 2008
Table 6 A: Cointegration Tests
A: Unrestricted Co integration Rank Test (Trace)
Hypothesized Eigen Trace 0.05 Critical Prob.**
No. of CE(s) Value Statistic Value
None * 0.092764 541.1647 197.3709 0.0001
At most 1 * 0.052911 338.7678 159.5297 0.0000
At most 2 * 0.037471 225.7481 125.6154 0.0000
At most 3 * 0.029811 146.3488 95.75366 0.0000
At most 4 * 0.020094 83.42916 69.81889 0.0028
At most 5 0.009876 41.22914 47.85613 0.1815
At most 6 0.006786 20.59548 29.79707 0.3833
At most 7 0.002627 6.439407 15.49471 0.6436
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At most 8 0.000467 0.970331 3.841466 0.3246
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Trace test indicates 5 cointegrating eqn(s) at the 0.05 level
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* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Trend assumption: Linear deterministic trend
Lags interval (in first differences): 1 to 4
First part of the co-integration test (Table 6A), the trace test, indicates that there exists co-
integrating vector at 5% level. It can also reject the null hypothesis, that there exists none
co-integrating equations. Second part of the co-integration test (Table 6B), the Maximum
Eigen value test, also indicates the same result.
Table No.6 B: Unrestricted Co integration Rank Test
(Maximum Eigen value)
Hypothesized Eigenvalue Max-Eigen 0.05 Critical Prob.**
No. of CE(s) Statistic Value
None * 0.092764 202.3969 58.43354 0.0000
At most 1 * 0.052911 113.0197 52.36261 0.0000
At most 2 * 0.037471 79.39931 46.23142 0.0000
At most 3 * 0.029811 62.91965 40.07757 0.0000
At most 4 * 0.020094 42.20002 33.87687 0.0041
At most 5 0.009876 20.63366 27.58434 0.2990
At most 6 0.006786 14.15608 21.13162 0.3524
At most 7 0.002627 5.469075 14.2646 0.6818
At most 8 0.000467 0.970331 3.841466 0.3246
Max-eigenvalue test indicates 5 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
85Saif Siddiqui
Both tests indicate co-integrating equations at the 5% level. The conclusion can be made
that the time series are co-integrated. There is a long-term relationship between the variables.
It is further concluded that both test are showing same result, which indicate co-integration
indication is very strong.
4.5. Pair wise Granger Causality Tests
After testing the data for correlation to see if the time series move together and for co-
integration to see if one of the time series could be used to predict or change the other, a
Granger causality test is conducted The test for Granger Causality involves examining
whether lagged values of one series have significant explanatory power for another series.
It tests the null hypothesis of no granger causality.
It can be inferred that ATX does not Granger Cause BEL, CAC and AEX .In return these
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markets are behaving the same way. But it is noted that MIBTEL and DAX does not
Granger Cause ATX, but get caused by them. DAX, AEX, MIBTEL, SWISS and FTSE
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Granger Cause BEL, in return BEL is unable to do the same but CAC does not granger
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cause BEL. It is also seen that CAC does not Granger Cause DAX and SWISS, but they
cause CAC. AEX, FTSE and MIBTEL cause CAC and get caused. DAX Granger Cause
AEX and MIBTEL, but opposite is not true. DAX does not Granger Cause SWISS, but
SWISS causes DAX. Granger Cause FTSE opposite is also true. MIBTEL and AEX
cause ach other. SWISS Granger Cause AEX, but not get caused. FTSE does not Granger
Cause AEX, but AEX does. FTSE and MIBTEL cause each other. FTSE does not Granger
Cause SWISS, but SWISS does it.
It is amazing to conclude that SENSEX Granger Cause BEL, CAC, DAX, AEX, MIBTEL,
SWISS, FTSE but none of these markets do the same to SENSEX. With an exception,
SENSEX Granger Cause ATX and vice versa.
5. Conclusion
This study is a continuation of research on the issue of growing interdependency among
stock markets. The degree of positive correlation between the SENSEX and other European
markets indexes varies between low to high. The findings suggest that return of all stock
markets are not normally distributed and show stochastic pattern in return. The empirical
results reveal co integration among the markets under study. It is finally concluded that
SENSEX granger cause all European markets indexes under study, but none of these
indexes do the same to SENSEX.
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Appendix 1: Pair wise Granger causality tests
Null Hypothesis: Obs F-Statistic Probability
BEL does not Granger Cause ATX 2079 1.1904 0.31134
ATX does not Granger Cause BEL 1.63447 0.14752
CAC does not Granger Cause ATX 2079 0.48615 0.78683
ATX does not Granger Cause CAC 1.13006 0.34213
DAX does not Granger Cause ATX 2079 2.09619 0.06315
ATX does not Granger Cause DAX 3.97365 0.00137*
AEX does not Granger Cause ATX 2079 0.46659 0.80136
ATX does not Granger Cause AEX 1.3167 0.25398
MIBTEL does not Granger Cause ATX 2079 1.39307 0.22373
ATX does not Granger Cause MIBTEL 11.0695 1.50E-10*
SWISS does not Granger Cause ATX 2079 0.98297 0.42664
ATX does not Granger Cause SWISS 5.26569 8.20E-05*
FTSE does not Granger Cause ATX 2079 1.73354 0.1236
ATX does not Granger Cause FTSE 3.27365 0.006*
SENSEX does not Granger Cause ATX 2079 9.44738 6.40E-09*
ATX does not Granger Cause SENSEX 7.97693 1.90E-07*
88Pranjana ? Vol 11, No 2, Jul-Dec, 2008
Null Hypothesis: Obs F-Statistic Probability
CAC does not Granger Cause BEL 2079 2.09757 0.06299
BEL does not Granger Cause CAC 4.05142 0.00116*
DAX does not Granger Cause BEL 2079 15.2578 9.40E-15*
BEL does not Granger Cause DAX 1.56031 0.16805
AEX does not Granger Cause BEL 2079 2.55729 0.02577*
BEL does not Granger Cause AEX 2.19926 0.05188
MIBTEL does not Granger Cause BEL 2079 9.35767 7.90E-09*
BEL does not Granger Cause MIBTEL 6.51106 5.10E-06*
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SWISS does not Granger Cause BEL 2079 6.10485 1.30E-05*
BEL does not Granger Cause SWISS 1.32071 0.25231
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FTSE does not Granger Cause BEL 2079 6.03075 1.50E-05*
BEL does not Granger Cause FTSE 1.19199 0.31055
SENSEX does not Granger Cause BEL 2079 7.07358 1.40E-06*
BEL does not Granger Cause SENSEX 2.17397 0.05445
DAX does not Granger Cause CAC 2079 21.4634 5.40E-21*
CAC does not Granger Cause DAX 1.82909 0.10392
AEX does not Granger Cause CAC 2079 51.6124 1.50E-50*
CAC does not Granger Cause AEX 3.1231 0.00819*
MIBTEL does not Granger Cause CAC 2079 6.84086 2.40E-06*
CAC does not Granger Cause MIBTEL 10.8113 2.80E-10*
SWISS does not Granger Cause CAC 2079 3.64306 0.00277*
CAC does not Granger Cause SWISS 1.15297 0.33017
FTSE does not Granger Cause CAC 2079 7.93181 2.10E-07*
CAC does not Granger Cause FTSE 2.30867 0.042*
SENSEX does not Granger Cause CAC 2079 2.60971 0.02322*
CAC does not Granger Cause SENSEX 0.64887 0.6624
AEX does not Granger Cause DAX 2079 1.82313 0.10506
DAX does not Granger Cause AEX 7.7364 3.20E-07*
MIBTEL does not Granger Cause DAX 2079 2.05683 0.06804
DAX does not Granger Cause MIBTEL 9.64138 4.10E-09*
89Saif Siddiqui
Null Hypothesis: Obs F-Statistic Probability
SWISS does not Granger Cause DAX 2079 3.56239 0.00328*
DAX does not Granger Cause SWISS 1.88943 0.09301
FTSE does not Granger Cause DAX 2079 4.09304 0.00106*
DAX does not Granger Cause FTSE 8.05752 1.50E-07*
SENSEX does not Granger Cause DAX 2079 9.97603 1.90E-09*
DAX does not Granger Cause SENSEX 0.35787 0.87739
MIBTEL does not Granger Cause AEX 2079 4.29683 0.00068*
AEX does not Granger Cause MIBTEL 12.2642 9.70E-12*
Downloaded From IP - 115.248.73.67 on dated 4-Dec-2010
SWISS does not Granger Cause AEX 2079 3.18884 0.00715*
AEX does not Granger Cause SWISS 0.75689 0.58096
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FTSE does not Granger Cause AEX 2079 2.05981 0.06766
AEX does not Granger Cause FTSE 3.03076 0.0099*
SENSEX does not Granger Cause AEX 2079 2.75158 0.01747*
AEX does not Granger Cause SENSEX 0.59529 0.70362
SWISS does not Granger Cause MIBTEL 2079 9.12659 1.30E-08*
MIBTEL does not Granger Cause SWISS 2.32521 0.04067*
FTSE does not Granger Cause MIBTEL 2079 8.34472 8.00E-08*
MIBTEL does not Granger Cause FTSE 13.6069 4.30E-13*
SENSEX does not Granger Cause MIBTEL 2079 9.11269 1.40E-08*
MIBTEL does not Granger Cause SENSEX 1.96725 0.0805
FTSE does not Granger Cause SWISS 2079 0.69036 0.63075
SWISS does not Granger Cause FTSE 11.6471 4.00E-11*
SENSEX does not Granger Cause SWISS 2079 8.68211 3.70E-08*
SWISS does not Granger Cause SENSEX 0.17864 0.97068
SENSEX does not Granger Cause FTSE 2079 8.01038 1.70E-07*
FTSE does not Granger Cause SENSEX 0.41971 0.83529
(*) Rejection of the null hypothesis at 5% and therefore there is Granger causality
90Pranjana ? Vol 11, No 2, Jul-Dec, 2008
Expression
AMOEBIC BRANDING
Harish Bijoor 1
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In the kingdom of Protista (with cousins Flagellates, Algae and Parasite protists), of the
Phylum Protozoa (single-celled organisms), of the social class Sarcodina, lies an Eukaryota
(organisms with nucleated cells) called an Amoeba!
There is an amoeba in each one of us. And I am not talking of the one that got you
running to the 'loo' every hour last week after that binge on exciting street food!
There is an amoeba in each one of us. An amoeba we keep hidden. An amoeba we
seldom allow to flourish. An amoeba we marketing people seldom recognize in consumers.
The consumer is an amoeba.
The word amoeba in itself is an excitingly real one. A word that comes from the Greek
word “Amoibe” that very simply means change. The Amoeba is about change. All about
this one celled blobby organism surrounded by a porous cell membrane through which it
breathes. It is about this organism that is never in static form.
1. Brand-domain specialist and CEO, Consults Inc, based with UK, Hong Kong and the Indian sub-continent.
91Harish Bijoor
The Amoeba is all about this entity that has no one shape really. Try defining it in a
picture, and possibly whatever shape you draw could be right. At some time or the other,
the amoeba has been that………..or at some point of time or the other, the amoeba will
be that!
These single celled organisms then, driven by their life mission to eat, defecate and
reproduce (just as that of the human being at large) are controlled by a nucleus. The
function of growth and the function of reproduction are largely controlled by this central
nucleus.
The amoeba eats. By forming pseudopods and food vacuoles. The amoeba will surround
a particle of food and put out its pseudopods. These will then fuse all around the food
particle and lo and behold! The food vacuole has happened! The amoeba has commenced
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the process of eating! Its first life mission!
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The amoeba will eject waste similarly, using the device of a contractile vacuole! Its second
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life mission is accomplished!
The amoeba will reproduce when the nucleus tell it to. The process is that much less
exciting that what we human beings use. Alas! The poor amoeba reproduces asexually!
By a process of Binary fission, where the cytoplasm, a jelly-like series of folded membranes
that form part of its primary body, will divide. The nucleus will divide as well, by fission of
the nucleus! And a new amoeba has happened! Mission three accomplished!
This exciting creature called an amoeba is all about change. And Heraclitus was right.
The only permanent thing in our lives is change. There is nothing static around us.
Everything changes. Every moment.
The human being on the other hand seems to find it difficult to believe and breathe this
change. Despite physiology!
Human physiology tells us every moment of the day that we are changing, albeit slowly.
The body itself is in a change mode all the time. The hair on your chest is growing if you
are a man, and those tiny strands of hair on your upper lip are growing as well, dear lady!
Growing every moment. Your cells are growing till you reach the ripe old age of 18 and
your cells are dying every moment after that till you are totally and truly dead!
The lesson your body teaches you is simple. There is either growth or decay in the body.
But there is nothing static. The body grows or decays and the mind grows and decays.
There is nothing called a human being. All there is is the amoeba in our lives! Change!
Just as the body changes every moment, the mind is even faster in its change orientation.
The human mind is possibly the most maverick of them all. No computer in the world has
been invented which can replicate the process of thinking of the human mind. And even
if that is possible, with the best of Artificial Intelligence robots being experimented with,
no piece of dynamic software can match the pace of the changing terrain of mind space.
92Pranjana ? Vol 11, No 2, Jul-Dec, 2008
The mind changes its moods and decisions every nano second. Every new impulse is
enough to get the mind onto an excitingly new track of thinking. You may do nothing
about it physically, but you think exciting thoughts in your head that change all the time.
The mind is all about change as well! Maybe change that is fifty times as robust as the
change that the body goes though in our travail through life.
The mind and body is all about change then. The mind in particular is all about the
amoeba! It is never static.
In a space that is not static at all, I worry a great deal on the way we manage brands in
this world of ours.
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Remember for a start that the brand is a thought. A thought that lives in the minds of
consumers! In the dynamic and ever-changing minds of consumers!
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Brand Managers the world over learn brand management in management schools that
teach theory that is static at large. Peek keenly at the very concept of brand positioning.
Brand positioning is defined as the exact pin-pointed position a brand occupies in the
mind of a consumer! Very few definitions really add that line which says………at that
point of time! Brand positioning can never be a static state theory. The consumer is just
too dynamic for it.
Peek keenly at brand loyalty as a concept. When the only reality in markets is change, the
only reality of a concept worth its weight of the paper it is written on is the theory of
Brand Promiscuity. When there is so much change around, how can a static state concept
like brand loyalty, live, thrive and flourish?
Brand Managers study static state theory and enter the real market. Here, they face
consumers who are different animals altogether. They are completely single celled creatures
called an Amoeba! All about complete change! All the time! Brand managers therefore
do not succeed!
The environment is even more challenging today than it was in the early days of the last
century when branding really took off. The very environment we live in is one that has
seen catalysts of change coming in with every generation. Change catalysts such as the
television for one, had consumers thinking on overdrive. Television as a medium encouraged
the exploration of variety. More so, television bred promiscuity of every variety in brand
choice.
The day we live in is all about the Internet. This is the day and age when eleven year olds
are exposed to the lure of junk mail in their mailboxes. At times junk mail that lure them
on to sites that offer sleaze of every variety. This is a generation that has promiscuity
peering out of every pore. Loyalty is old hat stuff. The real world out there is about
change. Loyalty is an anti-gravity force. A force put out there by artificial man in his
93Harish Bijoor
quest to attain the status of a status quo society. But there is nothing status quo anymore!
Life is about rapid change. And the internet offers variety and lure as never before!
The point then is that in a world of consumers where change is the only big mantra, how
dare brand management practitioners practice their trade and craft with the tools of a
static state consumer society taught to them by their Kotlers and Aakers and Ries and
Trouts?
Its time we adopted a whole new framework to the concept of brand management itself.
And this is amoebic branding! A branding format that encourages the brand manager to
think as alive and keep pace with the pace that the consumers at large in the market
place adopt. Remember, brands are managed by consumers in this new era! Not by
brand managers!
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The consumer is changing. And therefore brands must. Brands must give up their age-old
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views of sticking to static-sate theories that don’t work in the marketplace anymore.
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Brands must change and morph in their offerings just as fast as the dynamic mind of the
consumer is moving.
Brands have focused far too long on the static state theories of yore. Time to think new.
Time to morph the brand in every way. Time to keep pace and even outpace the thinking
of the consumer at large. What the consumer thought yesterday is old hat and of no
consequence. What he thinks today is important. What he will think tomorrow is even
more important. Brands that are robust will be brands that scenario plan the mind of the
consumer in every possible route. These are brands that will have multiple plans ready
across multiple possibilities of routes the consumer might take tomorrow.
Amoebic branding is all about being ready. You never know where the next consumer
pseudopod (false feet in amoebic terminology) will fall. You need to be prepared in every
direction it may fall though. And that is proactive, wild, exciting and imaginative branding
at play. This is the type for branding that will pay dividends to the company that runs
brands for tomorrow.
Brand Managers worldwide have listened for far too long to theory that is read and taught
at the best of Management Universities across the world. Branding unfortunately is a
science, art and philosophy that swims in the mind space of consumers. And any science
that swims here is a tough one to be learnt and taught in static state environments. If
indeed it was that easy, every brand launched by a guy from Ivy League Institutes would
be a whopping success for sure! Sadly it isn’t. The latest statistic tells us that for every 10
brand launches in the world, one succeeds, three are just about lingering on, five are
stretcher cases, and one is dead! All in the space of 24 quick months!
There is a great degree of uncertainty in the realm of branding because the consumer is
amoebic. The only way to battle an amoebic consumer is to adopt an amoebic form of
branding as well.
94Pranjana ? Vol 11, No 2, Jul-Dec, 2008
Branding is just too rational a process today. The consumer on the other hand is completely
irrational in his purchase behavior. Brand Managers are far too rigid in their thinking. The
very education system we come out of, teaches us to be rational. Far too rational. The
two irrational streams that actually formed part of our curriculum decades ago are no
longer taught to us in our schools! Religion and Music! Two streams that teach us to be
irrational!
Amoebic branding is therefore the answer for today and tomorrow. Branding that is fast
paced, change oriented, and all about the changing consumer. For far too long we have
depended on processes that talk about B2B branding and B2C branding. Time now to
wake up and let the consumer define his brand. Let him decide how he needs his brand
to be shaped. C2C branding is all about the amoebic consumer creating his own version
of an amoebic brand. It is all about an amoeba catering to an amoeba! It is all about a
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shift in ownership as well. The brand manager is not the owner of the brand. The consumer
is. And the owner will decide how he wants his brand to be…..today and tomorrow!
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C2C branding will be about whole sets of consumers using movements such as the flash-
mobs, using the device of the SMS and indeed the internet, to spin their own versions of
brands that remain as contemporary and as alive as every thinking consumer out there.
Static state branding is therefore dead. Every theory out there which is based on static
state consumers in the marketplace is but a dead theory. Time to burn those books then.
Time to let Amoebic branding take over!
And the best part is………Amoebic branding will not be taught out of a book.
A book is static state! The consumer isn’t!
Ouch! Ouch! Ouch!
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