M1and

M2 have been used in this paper to test causality empirically in mean and causality in variance among the

selected stock markets with respect to pre-Brexit and post-Brexit periods.

As

the return series in this paper are found to be heteroskedastic, so unconditional

correlations & partial

correlations are supposed to be biased

upward and do not provide a basis for examining

interdependence. Thus, stock market integration is analysed in the next

part of this paper by utilizing Johansen’s Co-integration method.

4.3 Unit root & Co-integration tests: In this sub section, the pair wise

integrations of the stock markets (to gain

an insight into the existence and extent of co-movement among selected stock

markets) have been tested. Since Brexit have shown its drastic impact on UK stock

market, so to analyse its impact on other stock markets, test of co-integration

is fully justified.

For

existence of co-integration, all the financial time series are supposed to be

integrated of the same order. Hence it

is necessary to confirm the order in which the

index returns are stationary. In

other words, a perquisite of examining co-integration between stock markets is

that all variables are non-stationary. When variable is non-stationary in time

then it is said to have a unit root. Augmented Dickey Fuller (ADF) Test is used

commonly for this purpose. table 7 represents the computations of ADF unit root

test for all the stock indices . Results show that no test statistics are statistically significant

(i.e. the existence of unit roots at level for all daily log return series).

Thus stock indexes (in logarithmic scale)

are integrated. In addition to that,

a further check of differences depicts no evidence to support the

presence of a unit root in first order differences. Thus log returns on indices

are found to be stationary at the first difference. Hence they have been

integrated in the order one.

As the financial series found to be integrated

of the same order, so we can proceed to the next step, which is to test if the

series are co-integrated. Results of bi-variate

Johansen’s Co-integration Test have been presented in table 8 and high degree

of co-integration has been observed.

Table-7:

ADF results

Countries

ADF STATISTIC

At

level

First

Difference

India

-1.800631

-40.90072

Japan

-1.810113

-34.69017

Russia

-2.061185

-43.96004

China

-1.203150

-47.08226

UK

-2.161231

-45.21125

Data

Source: http://?nance.yahoo.com/

Result: Computed using E-Views with respect to the ?rst order differences

in logarithmic stock indices prices.

MacKinnon critical values for rejection of hypothesis of a unit root.:

For the ADF test, at 1% level of

significance Critical Value is -3.4418

; 5% level of significance Critical

Value is -2.8658; 10%

level of significance Critical Value is -2.5691.

Table-8: Bivariate Johansen’s Cointegration Test

Results

Hypothesized Number of Cointegrating Equations

Trace

Statistic

p-value

Max Eigenvalue Statistic

p-value

India-Japan

None

19.897685*

0.0005

17.556685*

0.0075

At most 1

18.807622*

0.0035

16.001181*

0.0045

India-Russia

None

7.007681

0.5015

16.890023*

0.0001

At most 1

17.811185*

0.0002

16.897766*

0.0015

India- China

None

18.898541*

0.0025

17.334285*

0.0031

At most 1

17.000685*

0.0025

16.000483*

0.0022

India-UK

None

6.890185

0.7008

16.004455*

0.0001

At most 1

19.812665*

0.0011

17.891100*

0.0067

Japan-Russia

None

18.891175*

0.0079

4.001156

0.4043

At most 1

17.777685*

0.0035

17.001185*

0.0002

Japan-China

None

8.897319

0.2095

8.000083

0.3022

At most 1

17.899912*

0.0025

17.007685*

0.0003

Japan-UK

None

18.800685*

0.0003

1.111185

0.1015

At most 1

19.555685*

0.0005

18.892431*

0.0025

Russia-China

None

17.811225*

0.0015

16.894445*

0.0002

At most 1

16.135681*

0.0001

16.020185*

0.0003

Russia-UK

None

2.197005

0.4004

1.892017

0.2005

At most 1

19.100085*

0.0045

18.000032*

0.0002

China-UK

None

17.227644*

0.0035

16.123485*

0.0015

At most 1

18.006781*

0.0025

17.000185*

0.0011

Data

Source: http://?nance.yahoo.com/

Result: Computed using E-Views.

* Significant at the 5% level,

**Null Hypothesis (Ho): Series are not cointegrated.

Rejection of null hypothesis implies existence of an underlying relationship of

stock markets.

4.4

Event Study: In this section, using the method of event study, the

impact of Brexit is examined on stock market reaction for the selected

countries. The event of interest for

this paper is Brexit (On 23rd day of June in the year 2016, around 52% of the participating UK electorate

exercised their voting right to leave the EU. On 29th day of March 2017, the

government invoked Article 50 of the Treaty on the

European Union ). Note that

event is not a single date but the period 23rd day of June 2016 to 29th day of

March 2017 has been chosen as event period. The asymmetric event window

has been chosen as -3 (i.e. before) to +5 (i.e. after) days with respect to the

event period.

The

null hypothesis is as follows.

H0

: There is no significant average annual

return (AAR) during the event window caused by happening of Brexit.

To

do event study, Brown & Warner (1980,1985) mentioned three return

generating models like OLS market model or Risk-Adjusted Market

Model (Sharp, 1964) , Market Adjusted Return Model and Mean Adjusted Return Model. This paper uses

only the second one for this purpose.

This model neglects the impact caused by variance in market return in

abnormal return of the security.

Table

reports the results of the event study. It depicts t-statistics of the Market Adjusted Return Model for each day

of the event window. Comparing the

results of the test with the critical values, we conclude that

day +1 , +2 and +3 shows statistical significance; the test

statistics is very high as well as negative and it counts –4.15, -5.12, -5.34, -4.65 and -7.44 for

India, Japan, Russia, China & UK respectively for first day after the event

window. Subsequently these values have been reduced upto +3 day and become

stable there after. It indicates instant significant and negative impact of Brexit

on security prices over all stock markets selected. Note that for any index,

opening prices of all the participating securities have been considered.

Moreover, it is to be noted that the stock market of UK had been badly affected

than other countries, which is quite natural.

Table-9:

Results of Event Study

India

Japan

Russia

China

UK

t-test

(Market Adjusted Return Model)

-3

0.07

0.07

0.02

0.02

0.12

-2

0.07

0.08

0.04

0.09

0.11

-1

0.09

0.01

0.06

0.09

0.09

0

–4.15

-5.12

-5.34

-4.65

-7.44

+1

–4.15

-5.12

-5.34

-4.65

-7.44

+2

–5.17

-6.15

-5.94

-6.05

-7.94

+3

–6.05

-6.92

-6.37

-6.67

-8.37

+4

–0.15

-0.02

-0.04

0.06

0.04

+5

0.01

0.01

0.03

0.06

0.09

Data

Source: http://?nance.yahoo.com/

Result: Computed using Stata.

4.5 Wilcoxon Signed

Ranks Test: Mean return from stock markets before Brexit and after Brexit can be compared using hypothesis testing.

Since the data does not follow normal

distribution (as evidenced from The Jarque–Bera test ), so it is not

recommended to use paired t test. Thus non-parametric equivalent of it, is to

be used. Hence Wilcoxon Signed Ranks Test has been selected for this purpose.

The results are represented in table 10 & table 11.

It

is to be noted that, for all the countries, the p-values are less than 0.05. It

indicates the rejection of the null hypothesis at 5% level of significance.

Hence with 95% confidence, it has been expected that there is a significant

difference in return of stock markets between before Brexit and after Brexit

days. It indicates a serious impact of Brexit on stock markets considered. To

investigate further in detail, econometric models have been considered in later

part of this paper.

Table-10:

Ranks for Wilcoxon Signed Ranks Test

(Before and After Brexit for all countries treated individually)

Ranks

N

India_after_Brexit – India_before_Brexit

Negative Ranks

300a

a. India_after_Brexit < India_before_Brexit Positive Ranks 25b b. India_after_Brexit > India_before_Brexit

Ties

15c

c. India_after_Brexit = India_before_Brexit

Total

340

Japan_after_Brexit – Japan_before_Brexit

Negative Ranks

320d

d. Japan_after_Brexit < Japan_before_Brexit Positive Ranks 20e e. Japan_after_Brexit > Japan_before_Brexit

Ties

0f

f. Japan_after_Brexit = Japan_before_Brexit

Total

340

Russia_after_Brexit – Russia_before_Brexit

Negative Ranks

314g

g. Russia_after_Brexit < Russia_before_Brexit Positive Ranks 20h h. Russia_after_Brexit > Russia_before_Brexit

Ties

6i

i. Russia_after_Brexit = Russia_before_Brexit

Total

340

China_after_Brexit – China_before_Brexit

Negative Ranks

325j

Positive Ranks

15k

j. China_after_Brexit < China_before_Brexit Ties 0l k. China_after_Brexit > China_before_Brexit

Total

340

l. China_after_Brexit = China_before_Brexit

UK_after_Brexit – UK_before_Brexit

Negative Ranks

332m

Positive Ranks

7n

m. UK_after_Brexit < UK_before_Brexit Ties 1o n. UK_after_Brexit > UK_before_Brexit

Total

340

o. UK_after_Brexit = UK_before_Brexit

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.