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where represents
the convergence in distribution and represents
a chi-squared distribution with (k -j + 1) degrees of freedom. Cheung and Ng (1996)
recommended that this test statistic can be utilized to test empirically the
null hypothesis of no causality in mean from lag j to lag k.

Now the causality in variance test has been focused
here. Let u and v be the squares of the standardized innovations  given by Evidently both u and v are unobservable, hence their
estimates can be  utilized to test the
hypothesis of no causality in variance.

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In this case  sample cross-correlation coef?cient at lag k, can
be computed from the consistent estimates of the conditional mean and variance
of X series and Y series. This is given by  

This test statistic is to be used to test
empirically the null hypothesis for no causality in variance from lag j to lag
k.

Evidently test statistic of Cheung and Ng (1996)
(say, S1and S2) may be influenced to severe size
distortion in presence of causality in mean,  hence the weighting cross-correlation has been
incorporated in this paper as suggested by Hong (2001) into the CCF approach.
Thus,  test statistics for the causality
in mean and causality in variance are presented by   

4.
Empirical Analysis

 

4.1 Univariate Descriptive
Statistics:  Country wise descriptive
statistics have been calculated, by breaking the sample into three disjoint
portions (before, during and after) with respect to Brexit. Results are
presented in table 1 to table 3.

A significant change in descriptive
statistics for various parts of the sample is found and it indicates a strong
impact of Brexit over the selected stock markets.  Moreover, it is to be noted that The Jarque–Bera
test rejects the null hypothesis of normality in all the
cases as the p-values  are less than or equal to 0.05. Hence it
can be stated with 95% confidence, that the stock market index returns does not
fit to the normal distribution. It gives a clear  indirect indication that  autoregressive conditional heteroskedasticity
(ARCH) characteristics exist in our sample. It motivates to use econometric
models in the subsequent portion of this paper.*-

 

Table-1
: Descriptive Statistics & testing of normality  before Brexit (February  23, 2016 to 
June 23,2016)

 

India

 Japan

Russia

China

UK

Mean

0.000234

0.000585

0.001056

0.000932

0.000102

Variance

0.020035

0.034432

0.034351

0.029841

0.005645

Jarque–Bera

678.7091
 

1124.752
 

102.6660
 

1732.5

308.0233
 

Probability

0.000000

0.000000

0.000000

0.000000

0.000000

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to  the ?rst order differences in logarithmic stock
indices prices.

Table-2
: Descriptive Statistics  & testing
of normality  during  Brexit period                                                   
(June 24, 2016 to March 29, 2017)

 

India

 Japan

Russia

China

UK

Mean

0.000204

0.000513

0.001002

0.000732

0.000100

Variance

0.029035

0.044002

0.035951

0.029996

0.006641

Jarque–Bera

671.7091
 

1104.752
 

102.0060
 

1630.5

301.0233
 

Probability

0.000000

0.000000

0.000000

0.000000

0.000000

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to  the ?rst order differences in logarithmic stock
indices prices.

Table-3
: Descriptive Statistics & testing of normality  after Brexit (March
30, 2017 to  September 29,2017)

 

India

 Japan

Russia

China

UK

Mean

0.000104

0.000413

0.001001

0.000402

0.000070

Variance

0.029835

0.044092

0.039951

0.039096

0.008601

Jarque–Bera

661.7090
 

1104.702
 

101.0029
 

1530.2

301.0003
 

Probability

0.000000

0.000000

0.000000

0.000000

0.000000

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to the ?rst order differences
in logarithmic stock indices prices.

4.2 Bivariate Descriptive Statistics:  Unconditional correlations as well as partial
correlations are calculated for all possible pair of countries similarly for
the disjoint parts of the sample.  For
the partial correlations, all remaining countries are considered as control
variables. The results are presented in table 4 to table 6. Their results show
a high level dependency among the selected stock markets.

It is to be noted that all countries
are highly correlated, indicating their high dependence on each other. Thus any
significant change in the stock market of a country will definitely impact
seriously on the stock markets of other countries. After removing the influence
of other countries (which represents control variables in partial
correlations), when partial correlations have been computed, then also
significant dependencies are noticed. It represents the dynamic linkages
between international financial markets. However, the values of partial
correlations are less than the Pearson bivariate correlations. This empirical
evidence shows that interdependencies of stock markets are influenced by stock
market movements of other countries too. 

It is to be noted also that, after
Brexit, all the correlations have been reduced , indicating a possible weakness
of dependencies due to the occurrence of the international event of Brexit.
Hence there is a clear indication of the impact of Brexit on stock markets of the
selected developing countries in Asia. The subsequent part of this paper,
confirm this evidence by considering advanced econometric tools.

 

Table-4:
Unconditional correlation & Partial Correlation matrix before Brexit                                         (February  23,
2016 to  June 23,2016)

Correlations

 

India

Japan

Russia

China

UK

India

Pearson Correlation

 

 

 

 

 

Sig. (2-tailed)

 

 

 

 

 

Partial

 

 

 

 

 

Japan

Pearson Correlation

.945

 

 

 

 

Sig. (2-tailed)

.002

 

 

 

 

Partial

.794

 

 

 

 

Russia

Pearson Correlation

.800

.827

 

 

 

Sig. (2-tailed)

.003

.000

 

 

 

Partial

.765

.678

 

 

 

China

Pearson Correlation

.866

.655

.600

 

 

Sig. (2-tailed)

.005

.001

.000

 

 

Partial

.762

.611

.567

 

 

UK

Pearson Correlation

.700

.789

.866

.766

 

Sig. (2-tailed)

.002

.002

.002

.002

 

Partial

.654

.701

.694

.664

 

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to the ?rst order differences
in logarithmic stock indices prices.

Table-5:
Unconditional correlation & Partial Correlation matrix during  Brexit period 
(June 24, 2016 to March 29, 2017)

Correlations

 

India

Japan

Russia

China

UK

India

Pearson Correlation

 

 

 

 

 

Sig. (2-tailed)

 

 

 

 

 

Partial

 

 

 

 

 

Japan

Pearson Correlation

.845

 

 

 

 

Sig. (2-tailed)

.002

 

 

 

 

Partial

.694

 

 

 

 

Russia

Pearson Correlation

.750

.727

 

 

 

Sig. (2-tailed)

.003

.000

 

 

 

Partial

.660

.613

 

 

 

China

Pearson Correlation

.706

.625

.587

 

 

Sig. (2-tailed)

.005

.001

.000

 

 

Partial

.664

.600

.560

 

 

UK

Pearson Correlation

.609

.709

.766

.666

 

Sig. (2-tailed)

.002

.002

.002

.002

 

Partial

.558

.691

.624

.610

 

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to the ?rst order differences
in logarithmic stock indices prices.

Table-6:
Unconditional correlation & Partial Correlation matrix after Brexit                                            (March 30, 2017 to 
September 29,2017)         

Correlations

 

India

Japan

Russia

China

UK

India

Pearson Correlation

 

 

 

 

 

Sig. (2-tailed)

 

 

 

 

 

Partial

 

 

 

 

 

Japan

Pearson Correlation

.705

 

 

 

 

Sig. (2-tailed)

.002

 

 

 

 

Partial

.592

 

 

 

 

Russia

Pearson Correlation

.650

.627

 

 

 

Sig. (2-tailed)

.003

.000

 

 

 

Partial

.612

.573

 

 

 

China

Pearson Correlation

.636

.525

.507

 

 

Sig. (2-tailed)

.005

.001

.000

 

 

Partial

.604

.509

.510

 

 

UK

Pearson Correlation

.559

.639

.666

.506

 

Sig. (2-tailed)

.002

.002

.002

.002

 

Partial

.550

.591

.521

.530

 

Data
Source: http://?nance.yahoo.com/                                                                  
Result: Computed using SPSS and MS Excel with respect to the ?rst order differences
in logarithmic stock indices prices. 

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