Site Loader
Rock Street, San Francisco

 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.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

 

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

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

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

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

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

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.

 

Post Author: admin

x

Hi!
I'm Dora!

Would you like to get a custom essay? How about receiving a customized one?

Check it out