INTRODUCTION:
Major
strategic deviations have been observed over the last three decades, with
international investment limits being reduced, exchange control being nearly
eliminated, free movement of capital, people, and technology being promoted,
and the fundamental structures of most global markets being transformed. Market
developments can alter the relationship between different markets across the
world.
The
liberalization has aided market integration, which has important consequences
for investment decisions and policy. In both established and emerging
economies, the stock markets have been correlated to the dramatic rise in
uncertainty. Stock market behavior research can provide insights into the stock
market's future development. Because of the growing importance of financial
markets in the global economy, volatility spillover has gained a lot of
attention recently (Bhar and Nikolova 2007; Caporale and Spagnolo 2012;
Tiwari et al. 2013). The economic
development dynamics are unavoidable. The study of worldwide stock market
interconnectedness is receiving a lot of interest nowadays. The home country
can be connected to foreign capital markets through financial integration (Joshi et al. 2021).
The
volatility spillover plays a significant role in understanding the
co-integration of various stock indices. This study tries to explain the
volatility spillover from two different perspectives. On one side, the
spillover shows a unilateral underlying relationship between historical
volatility and the current volatility of the same markets. On the other side,
cross volatility spillover shows a unilateral causal relationship between the
historical volatility of one market with the current volatility of other
markets.
The
primary objective of the paper is to study the volatility comparison of various
markets and volatility spillover effects in Indian and other major global
indices. The purpose of the paper is to demonstrate the degree to which the
changes in the stock prices in one market/index stimulate the open prices in
other markets in the next or following trading sessions and whether the
deviations in volatility in one market are positively related to spillover in
volatility in other markets. Additionally, volatility spillover across the
markets is investigated as they are found to have significant decision-making
inferences for risk management and portfolio management.
The
literature shows that financial markets of developed countries like USA, Japan,
and Europe are integrated (Arshanapalli, Doukas, and Lang 1995; N. A. Joshi et
al. 2021; Kizys and Pierdzioch 2009; Masih and Masih 1997). The similar integration between the USA, Japanese and
Asian markets were also observed by various studies ( Anoruo et al., 2003; Arshanapalli et al.,
1995; Asgharian et al., 2013).
Further, these studies attributed to the decline of stock indices after the
United States stock market crash of October 1987, Asian Financial Crisis of
1997 and Global Financial Crisis of 2008 to co- integration and interlinkages of
stock markets. They primarily focused on the integration of the markets of
developed economies. Hamao et al. (1990) found volatility spill over from USA to UK to Japan. Koutmos and Booth(1995) found that the negative innovations in the USA, UK and Japan markets
increase the volatility in another market to trade more as compared to positive
innovations.
The
returns and volatility spill overs in East Asian markets were measured by Yilmaz (2010),
and there was a substantial difference between the returns and volatility spill
overs in the crisis and non-crisis periods. The study also found that the
volatility spill overs outweighed the return spill overs. Co-integration and
spill over of volatility between India and its Asian neighbours was studied by Mukherjee and Mishra, (2010) and found intra-day volatility spill overs. These
spill overs were found to be significant and bi-directional. The study
concluded that there was a significant flow of information to India from other
Asian markets. The spill over effect in equity market of China with other
markets of USA, Japan and group of G5 countries was studied by (Nishimura and Men 2010; Wang and Wang 2010) by using 20 years daily prices and found that
significant impact of volatility spill over from Chinese market to US and
Japanese markets.
Uyaebo et al. (2015) studied the share indices of the USA, Germany, China
and three countries of African region namely South Africa, Nigeria and Kenya by
using the daily prices for the year 2000 to 2013. They used GARCH models to
measure volatility in return for all indices respectively the volatility in the
returns of these sample markets. They found that high volatility of index
returns in developing countries like Nigeria and Kenya. Volatility clustering
in Indian markets was studied by (Goudarzi and Ramanarayanan 2010) using GARCH (1, 1) model and found significant
evidence of volatility clustering. Gupta, (2013)
also studied Indian market data using GARCH model and found the volatility
clustering along with information spill over. Apart from capital markets a high
degree of dependency was found between Indian and Chinese commodity market as
well (Maitra and Dey 2014). Long-run relation between spot and future price was tested by (Jain and Biswal 2015), and they observed the co-movement among the stock prices and presence
of informed investors in both markets and concluded that in India the cash
market is efficient than the futures market.
Tanty
and Patjoshi (2016) studied the relation between returns and volatility,
volatility clustering, leverage effect and the persistent volatility of BSE and
NSE using ARCH and GARCH model and found volatility clustering in the Indian stock
indices. Susruth (2017) studied the forecast of volatility of S&P BSE 500
index using GARCH, E-GARCH and M-GARCH model and found the existence of
volatility clustering along with leverage effect on volatility and
non-existence of risk premium in Indian market. Gakhar et al. (2017) studied
the impact of budget on CNX auto and CNX bank and concluded that there was no
impact of union budget on short-term, medium-term and long-term returns of the
indices in the sample. They also concluded that volatility of these indices did
not change much in the post-budget period as compared to pre-budget period.
Aneja
and Makkar (2017) captured stock prices behaviour and volatility in Indian
banks during the financial crisis using GARCH model and found a very highly persistent
volatility and significant negative correlation between stock prices and
volatility. Arora (2017) studied the lead-lag relationship between Nifty 50 and
midcap 50 indices using Johansen co-integration test, vector autoregressive
model, variance decomposition and impulse response function and found that there
was no long-term cointegrating relationship between these two indices and nifty
returns were prejudiced by its own lag returns. Rajamohan and Arivalagan (2017)
studied the co-movement and causal effects among Asian markets. In the same
line, Babu and Hariharan (2017) studied the cointegration among G7 countries
and found that the indices were cointegrated in the short-term as well as
long-term and the investors could diversify their investments in these
countries to reduce their risk.
Narwal
and Chhabra (2018) reviewed the literature regarding volatility indices and
found that future realized volatility can be predicted based on informal
efficiency of volatility indices and such efficiency can be used as risk
management technique by the traders trading in F&O segment. Kaushik (2018)
studied the impact of corporate governance on volatility of stock indices in
India using conditional volatility models to investigate the patterns of
returns of Indian indices and concluded that corporate governance led to better
returns of the stocks and increased investors’ trust in those listed companies.
Majumdar and Saha (2018) studied the impact of macroeconomic factors on
volatility of stock index and found that external factors like gold, CRR,
exchange rate and foreign exchange reserve explained the volatility of Indian
market to the extent of 65% and the interest factors like crude oil, inflation
and call money rate had a significant impact on the volatility of Nifty.
Unidirectional
volatility and spill overs among US, Japanese and other Asian markets was
observed by using GARCH models (N. Joshi et al. 2021; Li and Giles 2015; Mohammadi
and Tan 2015). Bi-directional volatility
spill over was found between India and Sri Lanka and unidirectional volatility
as well as spill over between China and other markets (Jebran et al. 2017). The study used E-GARCH model to measure volatility spill over
direction pre- and post-financial crises of 2007-08 among 5 Asian countries.
Similar study by Vo and Ellis (2018)
found return relationship and volatility spill over pre- and post-sub-prime
crisis of 2008 on Vietnamese market and other developed markets. This study
used VAR-GARCH-BEKK models and the results were found to be statistically
significant. Kumar and Khanna (2018) also used GARCH-BEKK model and found that past volatility had more
impact on current volatility as compared to the shocks coming to the markets
using data of four Asian markets. The majority of the studies on volatility
spill over have been done on stock indices of developed economies; whereas very
few studies have focussed on the volatility spill over on markets of developing
economies. It is imperative to investigate the spill over in developing markets
as they are more sensitive and volatile comparted to the developed markets. The
current study is an attempt to estimate the volatility spill overs on the
Indian market with major global indices.
MATERIALS
AND METHODS:
To
examine the volatility and volatility spill over, the study uses data from the
Indian index, and seven other indices across the globe. The identified indices
were: Indian index (SENSEX) and seven major indices from different regions namely
Brazil (BOVESPA), America (DJIA), Japan (NIKKEI), China (SCI), Hong Kong (HIS),
South Korea (KOSPI) and Russia (RTSI). The closing prices on daily basis of the
indices in sample were collected from the stock exchanges websites for the
period from the year 2005 to 2020.
GARCH
(1, 1) Model:
Stock index prices exhibit large volatility, leading
to time varying variances and violating the assumption of a constant variance
(homoscedasticity). GARCH models can be used to verify the volatility
clustering in such time series data. ARCH LM test was used on the ARMA (1, 1)
model to investigate the volatility clustering of the sample indices and GARCH
(1, 1) model was used to estimate the conditional volatility.
For comparing various components of conditional volatility
such as asymmetric effects of positive and negative shocks on conditional
volatility, comparing the size effects and sign effects of the shocks and
influence of conditional volatility on returns in the sample markets, T-GARCH,
E-GARCH and M-GARCH models were used respectively.
This study will support the literature by analysing
the impact of Indian market on the indices from other countries and vice-versa.
The findings of the study would strengthen the methodological acceptance of
volatility spillover between Indian market and global stock indices and vice-versa.
GARCH (1, 1) model was adopted with the following
equation. GARCH (1, 1) model checks for changes for the random walk and allows
the error term to deviate from the assumption of normally distributed,
independent and homoscedasticity. In above model (2) 
2 is conditional variance of error term 
of GARCH (1, 1) model. Parameters
,
and 
represents changing volatility. Model measure three
important things, first is change in slope and intercept of the model
represented by
, second is time varying variance of error term and
third is level of risk by measuring conditional variance of log returns.
The Threshold GARCH Model (T-GARCH):
T-GARCH is an improved version of GARCH as it has the
ability to enforce asymmetric response to different shudders. The previous
studies confirmed that asymmetric response of the index to the unforeseen
negative shudder to the time series will be the grounds for higher volatility
in comparison of a positive shudder of same extent. In this study the T-GARCH
model along with dummy representing the presence of negative shocks in the
lagged error terms used in order to analyse the volatility in the selected
stock markets.
The T-GARCH model is a modified version of GARCH model
where one additional dummy variable (γµ2t-1It-1)
is added in the model which examines the presence of possible asymmetries in
the conditional volatility. The dummy variable in the TARCH model represents
the presence of negative shocks in the system.
The Exponential GARCH Model (E-GARCH):
The E-GARCH can be generalized to explain more lags
inside the conditional variance. The non-negativity constraints at the
parameters aren't there in E-GARCH model. The ARCH term may be labelled into
independent variables which imply the sign effect of shocks on Index volatility
and the size impact of shocks at the volatility.
The second term in the E-GARCH model indicates the
impact of GARCH term (volatility persistence) on the future conditional
volatility in selected Index returns. The third term in the EGARCH model
indicates the sign effect of the ARCH (previous shock) on the conditional
volatility in the selected Index returns. The fourth term in the E-GARCH model
indicates the size effect of the ARCH term on the conditional volatility in the
selected Index returns.
The GARCH in Mean Model (M-GARCH):
The objective of adopting the M-GARCH was to
investigate the response of price discovery procedure with recognize to any
alternate in conditional volatility. The conditional volatility is observed to
be good sized and high quality if the conditional volatility is located to be
associated with returns.
The GARCH (1, 1) Model with Exogenous Variable:
For
studying the volatility spillover outcomes of SENSEX on worldwide markets,
GARCH (1, 1) model changed which included the SENSEX volatility as the
exogenous variable inside the GARCH equation (Yilmaz, 2009). For reading the
volatility spillover results of world markets on SENSEX, GARCH (1, 1) was used
which included the global market volatility as the exogenous variable within
the GARCH equation. The squared residuals of the ARMA (1, 1) had been
considered for the volatility substitutes for the sample markets. Such squared
residuals had been used as exogenous variable in model.
In the above equation, shows RESID (-1)^2, shows the
GARCH effects and the β indicates the spillover effect in the direction
of volatility international stock volatility.
RESULTS
AND DISCUSSION:
Descriptive
Analysis, Stationarity Analysis and Volatility Clustering:
The
summary statistics shown in Table – 1 indicated that the average daily return
of DJIA was highest (0.14) followed by SENSEX, SCI, BOVESPA, RTSI, and KOSPI
whereas the average daily return of NIKKEI index was lowest (0.04) among all.
As far as the fluctuations in stock indices are concerned, BOVESPA had the
highest volatility. The fact that the emerging markets are more volatile is
evident from statistics on standard deviation of daily returns in these markets.
In general, the developed market returns are less volatile with standard
deviation lesser than the emerging markets. Negative skewness shows that the
tail on the left side of the chance density function is longer than the right
side, on the other hand, positive skewness shows that the tail on the right
side is longer as compared to the left side. Kurtosis explains the distribution
compared with normal distribution, whether it leptokurtic or platykurtic
distribution. The all the selected indices except SENSEX are having platykurtic
distribution whereas SENSEX is having leptokurtic distribution. The
stationarity test estimates of ADF test shows that all the variables follow I
(1) level of integration.
Volatility
clustering was found in each of the indices under the study at different
levels. As the p – value of F statistics and observed R – squared was less than
1%, it indicated the existence of volatility clustering in the stock markets.
The development process of these indices and mysterious behavioural aspects of
stockholders may be the explanations behind different levels of volatility
clustering.
For
investigating the conditional volatility in the sample indices, GARCH (1, 1)
model was adopted. The results of the GARCH (1, 1) analysis are shown in the
Table - 2. The results shown that the p – value of the co-efficient of ARCH and
GARCH was found to be less than 1%. These results show that there is a
significant impact of residuals and GARCH term at 1st lag. The findings also
show that the sum total of both independent terms was less than 1 but the projected
decaying rate of volatility in the sample indices is different. Hence, the H01:
There are no ARCH or GARCH errors, was rejected.
Table
2: GARCH (1, 1) Analysis for Conditional Volatility
|
Index
|
Intercept
|
GARCH (-1)
|
RESID (-1)^2
|
Decaying Rate
|
|
SENSEX
|
2.03E-06
(0.0000) **
|
0.922
(0.0000) **
|
0.093
(0.0000) **
|
2.3%
|
|
BOVESPA
|
7.81E-06
(0.0000) **
|
0.903
(0.0000) **
|
0.113
(0.0000) **
|
3.12%
|
|
DJIA
|
2.63E-06
(0.0000) **
|
0.881
(0.0000) **
|
0.153
(0.0000) **
|
2.19%
|
|
HIS
|
2.86E-06
(0.0000) **
|
0.919
(0.0000) **
|
0.104
(0.0000) **
|
1.51%
|
|
KOSPI
|
1.32E-06
(0.0000) **
|
0.933
(0.0000) **
|
0.099
(0.0000) **
|
1.02%
|
|
NIKKEI
|
10.04E-06
(0.0000) **
|
0.863
(0.0000) **
|
0.183
(0.0000) **
|
3.83%
|
|
SCI
|
7.67E-06
(0.0000) **
|
0.823
(0.0000) **
|
0.162
(0.0000) **
|
9.72%
|
|
RTSI
|
8.02E-06
(0.0000) **
|
0.931
(0.0000) **
|
0.102
(0.0000) **
|
1.59%
|
Source:
Results in E-Views, ** Significant at 1% level
The
decaying rate of volatility was found to be highest in SCI (9.72%), followed by
NIKKEI (3.83%) and SENSEX (2.3%) and least was found in case of RTSI (1.59%).
These results were supported by the previous studies like Karmakar (2005),
Kumar & Dhankar (2009) and Gupta et al. (2013).
Asymmetric
Volatility:
The
analysis of asymmetric volatility indicated in Table – 3 shown that the p-value
of slope co- efficient of ARCH term, GARCH term and the dummy variable was
found to be significant. Therefore, H02: There is no
asymmetric effect of shocks on conditional volatility, was rejected. These
results indicated that volatility in the sample markets have a significant
persistence level, it is affected by the unpredicted shocks and the
stakeholders had asymmetric response to negative shocks as well as positive
shocks.
Table
3: T-GARCH Analysis for Asymmetric Effect in Conditional Volatility
|
Index
|
Intercept
|
RESID (-1) ^ 2
|
GARCH (-1)
|
RESID (-1) ^ 2*RESID (-1) < 0
|
|
SENSEX
|
2.13E-06
(0.0000) **
|
0.059
(0.0000) **
|
0.918
(0.0000) **
|
0.124
(0.0000) **
|
|
BOVESPA
|
7.12E-06
(0.0000) **
|
0.041
(0.0000) **
|
0.913
(0.0000) **
|
0.131
(0.0000) **
|
|
DJIA
|
2.43E-06
(0.0000) **
|
-0.012
(0.0000) **
|
0.243
(0.0000) **
|
0.891
(0.0000) **
|
|
HIS
|
4.01E-06
(0.0000) **
|
0.064
(0.0000) **
|
0.913
(0.0000) **
|
0.096
(0.0000) **
|
|
KOSPI
|
1.68E-06
(0.0000) **
|
0.043
(0.0000) **
|
0.937
(0.0000) **
|
0.127
(0.0000) **
|
|
NIKKEI
|
9.05E-06
(0.0000) **
|
0.073
(0.0000) **
|
0.853
(0.0000) **
|
0.178
(0.0000) **
|
|
SCI
|
7.29E-06
(0.0000) **
|
0.173
(0.0000) **
|
0.801
(0.0000) **
|
-0.058
(0.0000) **
|
|
RTSI
|
6.01E-06
(0.0000) **
|
0.027
(0.0000) **
|
0.951
(0.0000) **
|
0.104
(0.0000) **
|
Source:
Results in E-Views, ** Significant at 1% level
E-GARCH
(1, 1) Model:
The
E-GARCH model indicated the effect of volatility persistence on imminent
conditional volatility in the returns of the indices (Table – 4). Hence, H03:
There is no effect of volatility persistence on imminent conditional
volatility, was rejected. The results indicated that the conditional volatility
of the sample indices had inverse relation with the sign of shock. The same
relation was indicated by the coefficient of slope.
Table
4: E-GARCH Analysis for Persistence in Conditional Volatility
|
Index
|
Intercept
|
GARCH Term
|
Sign Effect of ARCH Term
|
Size Effect of ARCH Term
|
|
SENSEX
|
-0.303
(0.0000)
**
|
0.201
(0.0000)
**
|
-0.091
(0.0000)**
|
0.993
(0.0000)**
|
|
BOVESPA
|
-0.342
(0.0000)
**
|
0.175
(0.0000)
**
|
-0.096
(0.0000)**
|
0.989
(0.0000)**
|
|
DJIA
|
0.406
(0.0000)
**
|
0.172
(0.0000)
**
|
-0.181
(0.0000)**
|
0.986
(0.0000)**
|
|
HIS
|
-0.329
(0.0000)
**
|
0.193
(0.0000)
**
|
-0.074
(0.0000)**
|
0.992
(0.0000)**
|
|
KOSPI
|
-0.252
(0.0000)
**
|
0.179
(0.0000)
**
|
-0.083
(0.0000)**
|
0.996
(0.0000)**
|
|
NIKKEI
|
-0.631
(0.0000)
**
|
0.260
(0.0000)**
|
-0.125
(0.0000)**
|
0.971
(0.0000)**
|
|
SCI
|
-0.923
(0.0000)
**
|
0.271
(0.0000)
**
|
0.033
(0.0000)**
|
0.912
(0.0000)**
|
|
RTSI
|
-0.201
(0.0000)
**
|
0.105
(0.0000)
**
|
-0.092
(0.0000)**
|
0.996
(0.0000)**
|
Source:
Results in E-Views, ** Significant at 1% level
M-GARCH
(1, 1) Model:
The
results of the M-GARCH model indicated in Table – 5 depicted that the slope
coefficient of the M-GARCH model equation was insignificant. Therefore, it can
be inferred that there was no significant impact of conditional volatility of
the indices returns on the conditional returns of these indices. Therefore, H04was
not rejected. The results have shown that in high volatile periods, selected
indices did not provide high returns as expected as per risk return trade-off
theory. No relationship was found between the conditional volatility and
conditional returns of these indices.
Table
5: M-GARCH Analysis for Impact of Conditional Volatility on Conditional Returns
|
Index
|
Intercept
|
GARCH Term
|
|
SENSEX
|
0.004
(0.0023)
**
|
0.885
(0.663)
|
|
BOVESPA
|
-0.005
(0.8453)
|
3.551
(0.0000)
|
|
DJIA
|
0.000
(0.0005)
|
2.529
(0.203)
|
|
HIS
|
0.005
(0.189)
|
0.735
(0.728)
|
|
KOSPI
|
0.003
(0.418)
|
0.784
(0.739)
|
|
NIKKEI
|
0.000
(0.315)
|
4.011
(0.076)
|
|
SCI
|
0.003
(0.631)
|
-0.947
(0.601)
|
|
RTSI
|
0.615
(0.718)
|
-0.006
(0.101)
|
|
Source: Results in
E-Views
|
Volatility
Spillover:
The
p-value of SENSEX volatility as an exogenous variable was found to be
significant for all Global indices used in the study. It can be inferred from
Table – 6 that there has been the existence of volatility spillover at a significant
level from SENSEX to Global Indices.
The
p-value of Global index volatility as an exogenous variable was found to be
significant for SENSEX. It can be inferred from these results in Table – 7 that
there has been the existence of volatility spillover at a significant level
from Global Indices to SENSEX.
Table
6: Volatility Spillover from SENSEX to Global Indices
|
Index
|
Intercept
|
RESID(-1)^2
|
GARCH
(-1)
|
Volatility Spillover
|
|
BOVESPA
|
1.63E-06
(0.0000)**
|
0.130
(0.0000)**
|
0.906
(0.0000)
**
|
0.085
(0.0000)**
|
|
DJIA
|
2.34E-06
(0.0000)**
|
0.158
(0.0000)**
|
0.836
(0.0000)
**
|
0.042
(0.0000)**
|
|
HIS
|
3.02E-06
(0.0000)**
|
0.092
(0.0000)**
|
0.901
(0.0000)
**
|
0.033
(0.0000)**
|
|
KOSPI
|
1.96E-06
(0.0000)**
|
0.117
(0.0000)**
|
0.849
(0.0000)
**
|
0.057
(0.0000)**
|
|
NIKKEI
|
9.03E-06
(0.0000)**
|
0.179
(0.0000)**
|
0.824
(0.0000)
**
|
0.029
(0.0000)**
|
|
SCI
|
6.01E-06
(0.0000)**
|
0.156
(0.0000)**
|
0.783
(0.0000)
**
|
0.101
(0.0000)**
|
|
RTSI
|
7.59E-06
(0.0000)**
|
0.093
(0.0000)**
|
0.939
(0.0000)
**
|
0.027
(0.0000)
**
|
Source: Results in E-Views, ** Significant
at 1% level
Table
7: Volatility Spillover from Global Indices to SENSEX
|
Index
|
Intercept
|
RESID
(-1)^2
|
GARCH (-1)
|
Volatility Spillover
|
|
BOVESPA
|
9.04E-06
(0.0000)
**
|
0.109
(0.0000)
**
|
0.911
(0.0000)
**
|
0.023
(0.0000)
**
|
|
DJIA
|
2.12E-06
(0.0000)
**
|
0.079
(0.0000)
**
|
0.861
(0.0000)
**
|
0.049
(0.0000)
**
|
|
HIS
|
2.47E-06
(0.0000)
**
|
0.088
(0.0000)
**
|
0.893
(0.0000)
**
|
0.063
(0.0000)
**
|
|
KOSPI
|
2.03E-06
(0.0000)
**
|
0.091
(0.0000)
**
|
0.843
(0.0000)
**
|
0.048
(0.0000)
**
|
|
NIKKEI
|
1.49E-06
(0.0000)
**
|
0.103
(0.0000)
**
|
0.894
(0.0000)
**
|
0.007
(0.0000)
**
|
|
SCI
|
1.45E-06
(0.0000)
**
|
0.112
(0.0000)
**
|
0.879
(0.0000)
**
|
0.014
(0.0000)
**
|
|
RTSI
|
1.76E-06
(0.0000)
**
|
0.108
(0.0000)
**
|
0.909
(0.0000)
**
|
0.015
(0.0000)
**
|
Source:
Results in E-Views, ** Significant at 1% level
CONCLUSION:
The
purpose of this paper is to study the volatility comparison and volatility
spillover effects in India and major global indices using ARCH LM test and
GARCH (1, 1) models. Furthermore the TGARCH, EGARCH and GARCH in mean or M
GARCH models were applied on the residuals of the ARMA (1,1) model on the
selected Indian and global stock indices for comparing the different components
of the conditional volatility lying in the selected Indian stock index and
global stock indices.
TGARCH
Model estimates shows that the market have symmetric volatility due to positive
as well as negative shocks in the system. The estimates also asserts that there
exists significant asymmetric effect in the conditional volatility of returns
of the selected indices. Moreover, the volatility in index returns due to the
positive shock is significantly different from the volatility in index returns
as a result of negative shocks. The results indicated that except SCI the slope
coefficient of all other global indices was positive. This indicates that the
impact of negative news is found to be significantly higher as compared to
positive news. The estimates of EGARCH Model also confirms, that there exists
significant volatility persistence in the conditional volatility in returns of
all selected indices.
The
GARCH-in-Mean Model is applied in the study in order to examine that how the
price discovery process in the all-selected indices returns response to any
change in conditional volatility. It can be concluded in the study that there
exists no significant impact of conditional volatility in the all-selected
Index returns on the conditional returns of the all-selected index returns. In
high volatile periods, all selected index returns are not providing high
returns as expected as per risk return trade-off theory. There is no
relationship found between conditional volatility in the all-selected index
returns and the conditional returns of the all-selected index returns.
In
the study the volatility spillover between the Indian stock market and selected
international stock markets are studied. In this study the researcher tries to
investigate the presence of volatility spill over and dynamic conditional
correlation between India and global indices. Volatility spillover was found to
be in existence from Indian stock market to global indices and vice-versa. The
coefficients were found to be positive which indicated the positive impact of
volatility of one market on the other.
Table 8: Summary of Hypotheses Developed
|
Hypothesis
Statement
|
Overall
Results for Null Hypothesis
|
|
H01:
There are no ARCH or GARCH errors.
|
Rejected**
|
|
H02:
There is no asymmetric effect of negative and positive shocks on conditional
volatility.
|
Rejected**
|
|
H03:
There is no effect of volatility persistence on imminent conditional
volatility.
|
Rejected**
|
|
H04:
There is no significant impact of conditional volatility of the indices
returns on the conditional returns.
|
Not
Rejected
|
|
H05
(a): There is no volatility spillover from SENSEX to Global Indices.
|
Rejected**
|
|
H05 (b):
There is no volatility spillover from Global Indices to SENSEX.
|
Rejected**
|
|
Note: **
Significant at 1% level
|
RESEARCH
IMPLICATIONS:
These
findings have substantial inferences and repercussions for portfolio managers,
analysts and investors for investment assessments and decisions regarding asset
allocations. The findings show that more consideration should be given to
co-integration among markets and their volatility movements. Higher volatility
will lead to higher level of fretfulness among market participants and
investors, which will push them to be more risk-averse. Singhal and Ghosh
(2016) suggested that investors tend to diversify their investment portfolio
and hedging to maximize returns and minimize risks.
The
results of the study also have pertinent effects for policy makers with respect
to Indian stock market and the foreign countries. Market traders, hedgers and
portfolio managers will be capable of understanding the interrelation of
volatility association among the stock indices. According to Xuan, Vinh and
Ellis (2018), “globalization and financial integration is the outgoing trend
to promote further international connectedness.” The limitation of the study
is that it is based on the daily closing price data and seasonal anomalies were
ignored. Further Research can be conducted by taking into consideration other
indices as well.
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