STOCK MARKET ASYMMETRY & INVESTORS' SENSATION ON PRIME MINISTERIAL TENURE: INDIAN EVIDENCE
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Stock Market Asymmetry & Investors’ Sensation on
Prime Ministerial Tenure: Indian Evidence
Dr. Subrata Roy *
Dr. Indrajit Ghosal **
ABSTRACT
This study empirically examines the growth of return, volatility shocks, market efficiency and in-
vestors’ sentiment on Prime Ministers during their administration as a Prime Minister. Thus, vari-
ous volatility forecasting measures are applied. It is observed that BSE return doesn’t follow ran-
dom walk and inefficient during their tenures as a Prime Minister. ARCH measure confirms about
volatility clustering. According to the EGARCH measure leverage effect doesn’t exists but presence
of this effect based on TARCH during the tenure of few Prime Ministers. Finally, the investors are
trustful to those Prime Ministers who are elected from Indian National Congress according to the
growth of return.
Keywords: BSE, GARCH, Indira Gandhi, Rajiv Gandhi, Narendra Modi.
Introduction
The stock market movement around the globe partly depends on presidential or prime minister
Election. The role of a particular political party is important during their election campaign and
after their wining for stock market growth. Generally, the investors have a positive emotion or
faith on a particular political party or a leader which he /she assumes that this leader is trustable
for the growth of the stock market if he/she becomes the prime Minister. During the tenure of a
Prime Minister, the stock market experiences several confrontations. Since independence, India
has experienced fifteen Prime Ministers (Nerendra Modi presently holding PM office). But Indian
stock market (BSE) releases its value publicly from 3rd April 1979 and thus, we have seen thirteen
Prime Ministers from that date. During this period, the stock market faces diverse information
asymmetry.
At this ground, the present study tries to examine the growth of stock market, market efficiency
and various asymmetric effects during their tenures. Although, this study is restricted to those
* Associate Professor, Department of Commerce
School of Commerce & Management Sciences
Mahatma Gandhi Central University, Motihari, East Champaran, Bihar, India - 845401
** Assistant Professor, Department of Information Technology
Amity University, Patna – 801503
Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020 ◆ 27Prime Ministers who have completed their PM offices at least three years. The study is designed as follows: section 2 describes the objective. Section 3 deals with data and study period. Section 4 provides methodology. Section 5 analyses the result and finally, section 6ends with a conclusion and recommendation. Literature Review: The financial time series like stock and exchange rate tends to occur in volatility clusters due to changes in the market. This type of phenomenon is first observed by Mandelbrot (1963) and Fama (1965), and further described by Baillie et al., (1996), Chou (1988) and Schwert (1989). Various models are employed to study this phenomenon. The empirical applications of the autoregressive conditional heteroskedasticity (ARCH) model introduced by Eagle (1982) or its extension gener- alised by Bollerslev (1986) in GARCH model and its various extensions (EGARCH, TARCH, PARCH etc) by Engle et al. (1987), Glosten et al. (1993), Nelson (1991) tries to forecast stock returns’ volatility. Besides that, it is often observed in the stock returns that volatility is found to be higher after getting bad news (negative shocks) rather than good news (positive shocks) of the same mag- nitude. Hence, volatility is affected asymmetrically by the positive and negative shocks. This phe- nomenon is called leverage effect which is pointed out by Black (1976) that means changes in stock prices tend to be negatively correlated with the changes in volatility (see Christie 1982 and Nelson 1991). Engle & Ng (1993) explain news impact curve (IMC) with asymmetric response to both good and bad news. To test the leverage effect, many nonlinear extensions of the GARCH model are developed. Similarly, Threshold ARCH (TARCH), Threshold GARCH (TGARCH) and PARCH which are independently developed by Zakoian (1994) and Glosten, Jagannathan and Runkle (1993). Beside these, large numbers of recent studies have examined different aspects of volatility forecasting (see Longin 1997, Gazda & Vyrost 2003, Chen & Lian 2005, Brandt & Jones 2006, Engle, Focardi & Fabozzi 2007, Chang Su 2010, Goudarzi 2011, Ameur & Senanedsch 2014 etc.) and depicted many evidences by using a range of volatility measures. Risk-return relationship is another property widely examined in the past. In general, it is assumed that the risk-return trade off is based on the unconditional distribution of return. In 1980, Merton (cited in Karmaker, 2007) criticizes about the failure of the previous studies in respect of changes in the level of risk when return is forecasted. Thus, it is essential to consider heteroskedasticity by using realized returns. Here, GARCH-M model allows conditional variance to affect the mean (expected return). Generally, the GARCH model is based on the implicit assumption that the average risk premium is constant for the sample peri- od. The GARCH-M model lightens up this assumption by allowing the velocity feedback effect to become operational (Karmakar 2007). The evidences of risk-return relation are mixed with posi- 28 ◆ Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020
tive, negative or zero relation (see French et. al. 1987, Campbell & Hentschel 1992, Nelson 1991,
Glosten et. al. 1993, Baillie & DeGennaro 1990, Chan et. al. 1992, Poon & Taylor 1992, Balaban &
Bayar 2005 etc.).
Lot of studies examines the various diverse asymmetric effects on stock markets around the globe.
But, research on this topic in poor and developing countries are very less. So, further research is
needed in this counter to know the dynamics on information asymmetry. With this notion, the
present study seeks to examine the growth of return, asymmetric effects, market efficiency and
investors sentiment on the Prime Ministers. It is quite uncommon to analyse those aspects during
the tenure of the Prime Ministers. At this ground, the present study is little different and that adds
value in the existing literature.
Objective of the study:
More specifically, the following objectives are achieved:
1. To examine the growth of BSE
2. To analyse the diverse asymmetric effect and market efficiency
3. To observe the investors’ sentiment on Prime Minister
Data & Study Period:
The study considers the daily closing prices of Bombay Stock Exchange (BSE) that covers the tenure
of six full-time Prime Ministers. The raw date is converted into a series of continuously compound-
ed percentage returns. During (from 14th January 1980 to 30th May 2019) these periods there
were six full-time PMs including Narendra Modi who completes his first tenure as Prime Minister
in India on 30th May 2019 but the second term is not consider here. Although, there are twelve
PMs during this study period but most of them don’t complete their five years tenure so exclude
them. Here, four Prime Ministers (Indira Gandhi, Rajiv Gandhi, P. V. Narasimha Rao & Dr. Manmo-
han Singh) are from Indian National Congress (INC) and the remaining two are (Atal Bihari Vajpay-
ee & Narendra Modi) from Bharratiya Janata Party (BJP).The daily closing index price is obtained
from the official website of Bombay Stock Exchange (BSE) and the information regarding Prime
Minister is obtained from the Prime Minister’s office.
Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020 ◆ 29Methodology:
The daily return of the BSE index is computed as under:
Rt = log(It/It-1)
Where, It is the index value at the current period t and It-1 is the price at the previous period.
Here, Jarque-Bera test statistic is used to observe whether the time series data is normally distributed or
not. The J-B test statistic as follows:
S 2 K 32
J B n
6 24
(1)
Where, n is the number of observations, S is the skewness and K is the kurtosis. To test normality, the
following hypothesis is formulated:
H0: Return series is normally distributed
Ha: Return series is not normally distributed
It is assumed that the time series data is stationary that means its mean, variance and co-variance are time
invariant. To test stationary, a non-parametric approach proposed by Phillips and Perron (1988) is applied
and based on the following statistic:
2
~ T ( f 0 0 )( se(ˆ ))
t t 0 1/ 2
f0 2 f0 s (2)
Where, ̂ is the estimate and tα is the t ratio of α, se(ˆ ) is the coefficient of standard error and s is the
standard error of the test regression. γ0 is a consistent estimate of the error variance (computed as, (T-
K)s2/T where k is the number of regressors), f0 is an estimator of the residual spectrum at frequency zero.
Here, the testable hypothesis for unit root may be written as under:
H0: Return series is non stationary or unit root
Ha: Return series is stationary
KPSS test seems more reliable, as it nuances the type of nonstationary. The ADF and PP test only determine
whether a time series is stationary or not, implicitly assuming in their null-hypothesis that the time series
contains a unit-root. Thus, in case the ADF and PP test states that the time series is nonstationary, while the
KPSS does not advocate this hypothesis, it is likely that the time series is level or trend stationary, rather
than being non-stationary (Pfaff, 2008). The KPSS is therefore more delicate in distinguishing (non)
stationary, and seems most appropriate and adequate for further analysis. Kwiatkowski et al. (1992)
propose the following model:
y t = ξ t + rt + e t (3)
rt = rt-1 + μt
Here, rt corresponds with a random walk process and for the error term, et, is assumed that it is
independent and identically distributed (i.i.d) with 0 mean constant standard deviation. The initial value of
rt, which is r0, is the level of the time series and is fixed. The null hypothesis, H0, posits that et is stationary
meaning that yt is level-stationary in case ξ is 0 and trend-stationary otherwise.
H0: Return series is stationary
Ha: Return series is non-stationary or has a unit root
Now the growth model is developed to estimate the rate of growth of return of BSE as well as growth of
BSE index in value during the tenure of the full-time Prime Ministers. Here, the basic model is as under:
Log(Rit) = αi + β(T) + ei (4)
Where, Rit is the return of the BSE at time t which is converted to log, T is the time period (duration of each
PM) and e is the error term with its usual assumptions.
30 ◆ Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020
41Equation 4 is a semi-log model because only dependent variable is in the log shape. After estimating
equation 4, the residuals are considered for testing like serial or auto-correlation test, heteroskedasticity
test, stationarity test and normality test to make the semi-log model suitable. For testing stationarity,
correlogram analysis is used and the testable hypothesis is as under:
H0: Residual is stationary
Ha: Residual is not stationary
Generally, the prices of stocks swing widely for an extended time period followed by a period of relative
calm which looks like a stepping of a drunker person meaning that it follows random walk or non-
stationary. To capture such varying variance Autoregressive Conditional Heteroskedasticity (ARCH) model is
applied (Engle 1982). Although, heteroskedasticity has no autoregressive structure that means
heteroskedasticity observed over different periods is auto-correlated meaning that presence of ARCH
effect.
To test ARCH effect the following regression equation (OLS) is formulated and estimated after making the
series stationary by taking the first difference:
Ri,t =β1 + β2Ri,t-1 + β3Ri,t-2 + .................... + βpRi,t-p + et (5)
2
It is assumed that et~N(0, α0 + α1μ t-1). Here, the variance of e at time t depends on squared distributions at
time t-1 that causes serial correlation and thus, the variance of ‘e’ not only depends on one lagged squared
disturbance term but also on several lagged squared disturbance terms which may be written as under:
Var(μt) = σ2t = α0 + α1e2t-1 + α2e2t-2 + ..................... + αpe2t-p (6)
Equation 6is the ARCH model of order p and the ARCH effect is tested by examining the validity of the null
hypothesis H0: α1 = α2 = .........= αp = 0. To test this Engle proposed to run the auxiliary regression (Regressed
Squared Standardized Residuals on a constant) at p lags.
e 2 t 0 1e 2 t 1 2 e 2 t 2 .......... p e 2 t p (7)
If there is no ARCH effect in the residuals then ARCH model is mis-specified. After testing for unit root and
ARCH effect then GARCH model may be applied.
The ARCH specification looks like a moving average specification as compared to the auto-regression and
thus, considers lagged conditional variance as autoregressive term in the ARCH model (Bollerslev, 1986).
The GARCH model is based on the assumption that changes of variances depend on the lagged variances of
the capital assets. Unexpected swings of stock prices generate more volatility in the upcoming periods. The
GARCH (p, q) model may be written as follows:
p q
2 t 0 i e 2 t i i 2 t i vi (8)
i 1 i 1
Where, α0 is the mean. p is the degree of ARCH process (lagged terms of squared errors) and q is the degree
of GARCH process (lagged terms of conditional variances) and vi is the random error. Here, ARIMA
technique is applied to determine the degree of p and q (see Box-Jenkins, 1970). The most widespread
GARCH(1,1) model can be written as:
2 t 0 1e 2 t 1 1 2 t 1 vt (9)
As the variance is expected to be positive then it is assumed that the regression coefficient α0, β1 and α1will
be always positive, while the stationarity of the variance is preserved, if the coefficients β1 and α1 are
smaller than 1. Here, (α1+β1) expresses the influence of variability of index or return from the previous
period on the current value of the variability which is usually close to 1.
The GARCH models are unable to observe frequently asymmetric effects when a different volatility is
recorded systematically. According to the martingale model, decrease and increase of return / index price
may be treated as bad and good news respectively. If decrease (negative shocks) in return is accompanied
Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020 ◆ 31
42by an increase in volatility which is greater than the volatility induced by an increase in return is called
leverage effect measured by EGARCH and TGARCH models.
Let Rt is the return of BSE index at time t. (10)
Rt = σ΄It-1 + ξt
ξt = σtZt (11)
Zt/ Ωt-1 ~ Ѱ(0,1,ѵ) (12)
The conditional variance may be written as follows:
p
et 1 q r
et 1
log i
2
j log( 2
t 1 ) k vt
i 1 t 1 j 1 k 1 t 1
(13)
Equation 10 indicates that conditional variance is an exponential function of the BSE returns or values that
automatically ensures its positive character. Where, 2 t is the conditional variance. Zt is the standardized
residual and ѵ denotes a vector of parameters that specifies the probability density function. ω, α, β and γ
are the parameters to be estimated. Here, α represents the symmetric effect or ARCH effect. β measures
the persistence in conditional volatility or GARCH effect. An asymmetric effect is indicated by the non-zero
value of γ and the presence of leverage effect is given by its negative value. When γ ˂ 0 then positive shocks
(good news) generate less volatility than the negative shocks (bad news) and when γ ˃ 0 then positive
innovations are more destabilizing than the negative innovations.
The positive and negative shocks in stock market have diverse effects on volatility and to deal with
this event, Glosten, Jagannathan and Runkle (1993) and Zakoian (1994) independently introduce the
Threshold GARCH or TGARCH model1 that captures the possible asymmetric shocks to volatility by adding
an additional term. The TGARCH(1,1) model is expressed as under:
p q r
2 t 0 i e 2 t i j 2 t j k e 2 t k I t k (14)
i 1 j 1 k 1
Where, (a) It-1 = 1, if et-1 ˂ 0 or negative (bad news)
(b) It-1 = 0, if et-1 ˃ 0 or positive (good news)
The leverage effect is captured by γ coefficient.
The study also uses dummy variable to capture investors’ sentiment on Prime Minister regarding growth of
BSE return that may be shown as under:
RBSE = α + β1IG1BSE+ et (15)
RBSE = α + β1RG2BSE + et (16)
RBSE = α + β1NR3BSE + et (17)
RBSE = α + β1AV4BSE + et (18)
RBSE = α + β1MS5BSE + et (19)
RBSE = α + β1NM6BSE + et (20)
Where, RBSE represents return of BSE index
IG1BSE = 1 if the investors positive sentiment on Indira Gandhi for BSE growth
= 0 otherwise
RG2BSE = 1 if the investors positive sentiment on Rajiv Gandhi for BSE growth
= 0 otherwise
NR3BSE = 1 if the investors positive sentiment on P.V. N. Rao for BSE growth
= 0 otherwise
32
1 ◆ Indian this
Alternatively Journal of Accounting (IJA) Volume: 52 (2) December 2020
model is called GJR (Glosten, Jagannathan & Runkle, 1993) or TGARCH model.
43AVsentiment
AV4BSE = 1 if the investors positive 4BSE = 1 if the
on investors positive
A. B. Vajpayee for sentiment
BSE growthon A. B. Vajpayee for BSE growth
= 0 otherwise = 0 otherwise
MSsentiment
MS5BSE = 1 if the investors positive 5BSE = 1 if the investors
on Dr. M. Singh positive
for BSEsentiment
growth on Dr. M. Singh for BSE growth
= 0 otherwise = 0 otherwise
NMsentiment
NM6BSE = 1 if the investors positive 6BSE = 1 if the
on investors
Narendra positive
Modi forsentiment
BSE growth on Narendra Modi for BSE growth
= 0 otherwise = 0 otherwise
Result & Discussion: Result & Discussion:
The
The descriptive statistics of BSE is descriptive
presented statistics
in table 1. It of BSE is presented
is observed that theindaily
tablemean
1. It is observed
return of BSEthat
is the daily mean re
quite stable during the tenure ofquite stableMinisters.
the Prime during theThe tenure of the
highest Prime
mean Ministers.
return The highest
is provided mean return is provided
during the
tenure of P.V. Narasimha Rao from tenure
Indianof P.V. Narasimha
National congress Rao(INC)
fromandIndian
the National congress
lowest return (INC) and
is provided the lowest return is p
during
the occupancy of Narendra Modithe fromoccupancy
BharotioofJanata
Narendra
PartyModi
(BJP).from
The Bharotio
BSE’s riskJanata Party (BJP).
has reached The BSE’s risk has reach
to highest
level during
level during the tenure of Dr. Monmohan the and
Singh tenure of Dr.
lowest Monmohan
during the timeSingh and lowest
of Narendra duringBihari
ModiAtl the time of Narendra Mo
Vajpayee. The JB statistics of theVajpayee. The JB statistics
return distribution duringof the
the returnofdistribution
tenure during theare
the Prime Ministers tenure of the Prime Ministe
very large
and the probabilities of obtaining andsuchthestatistics
probabilities
underofthe
obtaining
normalitysuch statistics under
assumption the normality
are significantly zeroassumption are signifi
meaning that
meaning that rejection of null hypothesis (H0: rejection
Normally of null hypothesis (H0: Normally distributed).
distributed).
Table 1 Descriptive Statistics of Table 1 Descriptive Statistics of BSE Return
BSE Return
Prime Minister OB Prime Minister
Mean Max OB
Mean
Std. Max
Min Skew. Min
Kurt. Std.
J-B Skew. Ku
Dev Dev
Indira Gandhi 906 Indira Gandhi
0.0922 906 0.0922
11.0530 -7.2100 1.1257 11.0530
0.8834 -7.2100
17.1613 1.1257
7688.360.8834 17.1
(0.000)
Rajiv Gandhi 1063 Rajiv Gandhi
0.1057 1063 0.1057
9.1329 -5.8220 1.6586 9.1329
0.3238 -5.8220
4.531 1.6586
122.47 0.3238 4.5
(0.000)
P.V. Narasimha Rao 1065 P.V. Narasimha
0.1172 13.1353Rao-12.7680
1065 0.1172
1.0140 13.1353
0.3072 -12.7680
9.0564 1.0140
1644.430.3072 9.0
(0.000)
Atal Bihari 1540 Atal Bihari8.2541 -11.1385
0.0320 1540 0.0320
1.7334 8.2541
-0.2489 -11.1385
6.0108 1.7334
597.59 -0.2489 6.0
Vajpayee Vajpayee (0.000)
Dr. Manmohan 2494 Dr. Manmohan
0.0767 2494 0.0767
17.3393 -10.9564 1.5766 17.3393
0.3152 -10.9564
12.1145 1.5766
8674.080.3152 12.1
Singh Singh (0.000)
Narendra Modi 1503 Narendra 8.9748
0.0292 Modi 1503 0.0292
-13.1525 1.1134 8.9748
-1.2828 -13.1525
25.8057 1.1134
32983.55-1.2828 25.8
(0.000)
Probabilities in parenthesis; Probabilities in parenthesis;
Source: Author’s own calculation Source: Author’s own calculation
Table 2 stationarity
Table 2 provides information regarding provides information
and market regarding
efficiency.stationarity and market
It is observed that theefficiency.
computedIt is observed tha
ADF and PP test statistics in levelADF and(absolute
forms PP test statistics in level
value) during forms
the tenure(absolute value)
of all the Prime during the tenure
Ministers are of all the Prime M
higher than the critical value andhigher than the
statistically critical value
significant at fiveand statistically
percent significant
level that means at five percent
rejection of nulllevel that means rej
hypothesis
hypothesis (H0: δ = 0 or ρ = 1) and thus, the (H 0: δseries
time = 0 ordon’t
ρ = 1)appear
and thus, the time
to have series
a unit rootdon’t appear
and don’t to have a unit root and
follow
random walk and thus, inefficient random
at theirwalk
weakand thus,Based
forms. inefficient
on KPSSat their
test weak forms.
statistics, theBased on KPSSalso
LM-statistics test statistics, the LM
reject the null hypothesis as thereject the nullare
LM-statistics hypothesis
lower thanas the
the LM-statistics are lower
asymptotic critical valuethan the percent
at one asymptotic critical value a
level of significance and may be level of significance
said that the returnsand maythe
during be tenure
said that ofthe
thereturns
PMs of during thefollow
BSE don’t tenure of the PMs of BSE d
random walks and inefficiency israndom
seen at walks
weak and
forms inefficiency
during their is seen at weak forms during their occupancy.
occupancy.
Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020 ◆ 33Table 2 Test
Table of Stationarity
2 Test &Table
of StationarityMarket Efficiency
& Market
2 Test
Table 2Efficiency
of Stationarity
Test & Market
of Stationarity Efficiency
& Market Efficiency
Prime Minister
Prime Minister PrimePrime
Minister Unit
UnitRoot Test
Root Test Unit Root Test Test
Minister Unit Root
ADF Test
ADF Test PP Test
PP Test
ADF Test Test KPSS Test
KPSS Test
PP Test KPS
Table 2 Test of Stationarity & Market Efficiency ADF PP Test
Indira Gandhi
Prime Table 2 Test of Stationarity & Market
Minister Efficiency
-16.77177** Unit-282.7538**
Root Test 0.1810
Indira GandhiPrime Minister IndiraIndira
Gandhi-16.77177**
Gandhi -282.7538**
Unit-16.77177**
Root -16.77177**
Test 0.1810
-282.7538**
-282.7538** 0.
Rajiv Gandhi
Rajiv Gandhi -19.91093**
Rajiv RajivADF
Gandhi Test
-19.91093** -206.7896**
PP Test
-206.7896**
-19.91093** KPSS0.2403
Test
0.2403
Gandhi
ADF Test -19.91093**
PP Test KPSS -206.7896**
Test -206.7896** 0.
P.V.P.V.
Indira Narasimha
Gandhi
Narasimha Rao -16.13296**
-16.77177** -147.4540**
-282.7538** 0.2620
0.1810
Indira GandhiRao P.V. Narasimha
P.V. -16.13296**
Rao Rao
Narasimha
-16.77177** -147.4540**
-16.13296**
-16.13296**
-282.7538** 0.18100.2620
-147.4540**
-147.4540** 0.
AtalAtal
Rajiv Bihari
Gandhi Vajpayee
Bihari Vajpayee -18.44720**
-19.91093**
Atal Bihari -18.44720**
Vajpayee -385.3635**
-206.7896**
-385.3635**
-18.44720** 0.1145
0.2403
0.1145
-385.3635** 0.
Rajiv Gandhi Atal Bihari Vajpayee
-19.91093** -18.44720**
-206.7896** 0.2403 -385.3635**
Dr. Dr.
P.V. Manmohan
Narasimha
Manmohan Singh
Rao Singh -22.95915**
-16.13296**
-22.95915** -562.8931**
-147.4540**
-562.8931** 0.1733
0.2620
0.1733
P.V. Narasimha Rao Dr. Manmohan
Dr. Manmohan SinghSingh
-16.13296** -22.95915**
-22.95915**
-147.4540** -562.8931**
0.2620 -562.8931** 0.
Atal Bihari Modi
Narendra Vajpayee -18.44720**
-13.9228** -385.3635**
-40.3029** 0.1145
0.0419
Narendra ModiVajpayee Narendra
Atal Bihari Narendra-13.9228**
Modi
-18.44720**
Modi -40.3029**
-13.9228**
-385.3635**
-13.9228** 0.1145 0.0419
-40.3029** 0.
Dr. Manmohan Singh
** Significance at 5% level -22.95915** -562.8931** 0.1733 -40.3029**
Source:Dr. Manmohan Singh -22.95915** -562.8931** 0.1733
** Significance at 5% level ** Significance at 5% level
** Significance at 5% level
Author’s own calculation
Narendra Modi
Source: Author’s own calculation Source:-13.9228** Author’sAuthor’s
Source: own calculation -40.3029**
own calculation 0.0419
Narendra Modi
** Significance at 5% level
-13.9228** -40.3029** 0.0419
TheThegrowth ratecalculation
** Significance
growth ofatreturn
rate
5% level of BSE (in percentage) during the tenure of the prime Ministers is depicted in
of calculation
return of BSE (in percentage) during theof(in tenure
The growth rate ofrate return of BSE (inofpercentage)
percentage) the prime during Ministers
the tenure theis tenure
depicted
of theofprime in Ministers is de
Source: Author’s own
Source: Author’s own The growth of return BSE during the prime Ministers
table three.
table three. It is seen
It isreturnthat
seen that the time
the coefficients are not significant as the probabilities values are higher
The growth rate of of BSE (intime
table three.
table coefficients
percentage) It is seen
three. It isare
during that
seen not
thethe significant
that time
tenurethe coefficients
time
of theascoefficients
the
prime probabilities
are not
Ministers notisvalues
aresignificant significant
depicted arethe
as higher
inasprobabilities
the probabilitiesvaluesvalu
ar
thanthanfiveThe growth(in
percent
fiveIt percent
rate of return of BSE
parenthesis)
(in parenthesis) of (in the
all percentage)
ofpercent
all PMs
the PMs and during
andthe the tenure ofof
percentage
the percentage
thegrowth
of
prime Ministers
growth rate is is depicted
negative in
during the
table three.
table
is seen that
three. It is seen
the time thancoefficients
that14
five
than
theth time five (innot
percent
are parenthesis)
(in
not parenthesis)
significant ofasallthethe allPMs theand
of probabilities PMsrate
the and isthe
values negative
percentage
arepercentage
are higherduring
of growth ofthe rate israte
growth negative
is neg
th coefficients areuntil significant th as the probabilities values
th sthigher
tenure of Indira Gandhi (From January 1980 to 24 March
th 1984), Rajiv Gandhi (31
th October
st 1984 (31st O(3
thantenure
five than of Indira
percent (in Gandhi
parenthesis) (From
tenure
of 14all
tenure
nd five percent (in parenthesis) of all the PMs and
January
oftheIndira
PMs
of 1980
Gandhi
and
Indira to(From
the
Gandhi until 24
percentage14 March
(From January
st the percentage of growth 14ofth 1984),
growth 1980rate
January Rajiv
to until
1980 is Gandhi
24
negative
to until
thrate is negative during the
(31
March
24 th
duringOctober
1984),
March the 1984Gandhi
Rajiv
1984), Rajiv Gandhi
to until of2tenure
to until December
2nd ofDecember 1989),1989), P.V.
to th Narasimha
P.V.
until th2
nd
Narasimha
DecemberRao Rao(21 (21
1989),June
st th 1991 to until 16 May
Juneth 1991
P.V. Narasimhato until 16th(21
Rao May st1996)st and
1996)
June st1991 and Dr.
toDr.Manmohan
Manmohan
until 16 th
untilMay 16th1996) and Dra
st
tenure Indira Gandhi
Indira (From
Gandhi 14 January
to until 1980
2 nd to
Decemberuntil 24 March
1989), P.V. 1984),
Narasimha Rajiv Gandhi
Rao (21 (31 June October
1991 1984
to May 1996)
rd th (From 14 January 1980 to until 24 March 1984), Rajiv Gandhi (31 October 1984
Sing
to until(23 nd May
Sing2 (23 rd 2004 to 25 May
December
May nd 2004 1989),
to 25 th 2014)rdwhord
P.V. May
Sing Narasimha
(232014) May who elected
Rao
2004 (21
electedto
stfrom
June
25 th INC.thSimilarly, the
stfrom 1991
May INC.2014)to until
Similarly,
who 16 th growth rate of return during the
May
ththe
elected 1996)
growth
from and
rate
INC. Dr.
of Manmohan
return
Similarly, during
the the
growth rate of retu
to until 2 December 1989), P.V. th Sing (23 May
Narasimha Rao2004 (21 June to 25nd1991 May 2014)
to until 16who Mayelected
1996) and from Dr. INC.
ManmohanthSimilarly, the growth rate o
tenure
Sing tenure of
rd
(23 Sing Atal
May of(23 Bihari
2004
Atalrd Bihari Vajpayee
to2004 th
25Vajpayee
May (19
th2014)(19 March
who
th
March 1998
elected1998 to until
from
tofrom 22
INC.
until 22 May
Similarly,
nd th
May 2004)ththe andgrowthNarendrarate Modi
of ndreturn (26 May
duringth 2014
the2014
th
May to 25tenureMay of
tenure
2014) Atal ofBihari
who Atal
elected Vajpayee
Bihari Vajpayee
INC.(19 (192004)
March
Similarly, 1998
March
the and1998
growth toNarendra
until
rate toof22
until Modi
return May
22during (26
nd2004)
Maythe May
and
2004) Narendra
and NarendraModi Mo(26
to 30
tenure May
of Atal
th
to 30tenure 2019)
Bihari
Mayof2019) who are
Vajpayee elected
who Vajpayee
areto(19 th th
elected
30(19from
March fromBJP
1998 is
BJP positive.
to until
iswho
positive. 22Although,
nd
May
Although, the
2004) thepercentage
and Narendra
percentage rate of
Modi
rate return
(26 th
ofAlthough,
return during
May 2014
during their
their rate ofrate
toMay 2019) are
whoelected from BJP isNarendra
positive. Although, the2014 percentage return
th th nd th
Atal Bihari 30 MarchMay 1998
2019) to until 22are May
elected 2004) and
from BJP Modi
is positive. (26 May the percentage of
to 30 th
May 2019) who are 2 2 from BJP is positive. Although,
elected the percentage rate of return during their
tenure is tonot30 lucrative.
th
May
tenure is not lucrative. The 2019) The
who R arevalue
elected
R value during
from
during the
BJP tenure
is
the positive.
tenure of
The Rof all
2 the
Although, 2PMs
allRthevaluethe is not
percentage
PMsduring isthe satisfactory
nottenure rate of
satisfactory and
return and the
during
theF statistics
their
2 tenure is not
tenure islucrative.
not lucrative. Thevalue during
2 during the tenure of all the PMs is not satisfactory and the F statistics
the tenure of all the of allPMstheFisPMs statistics
notissatisfactory and the
not satisfactory an
tenure
are are is tenure
not lucrative.
insignificant. is The
not The R The
residuals
lucrative. value
areR not
value serially
during correlated
the tenure of during
all the the
PMs tenure
is not of Indira
satisfactory Gandhi,
and the F Atal
statisticsBihari
insignificant. The residuals are
arenot
are insignificant. serially
insignificant. Thecorrelated
residuals
The residuals during
are not arethe not tenure
serially serially of Indira
correlated
correlated Gandhi,
during the
during Ataltenure
theBihari of Indira
tenure Gandhi,
of Indira GandAt
are insignificant.
Vajpayee areand The residuals
Nerendra
insignificant. Modi
The are
but
residuals notare serially
heteroskedasticity
not correlated
serially correlated during
problem during is thefound
the tenure
tenure in of Indira
the
of Indira
residuals Gandhi,
Gandhi, during
Atal Atal the
BihariBihari
tenure of
Vajpayee and Nerendra Modi but
Vajpayee heteroskedasticity
Vajpayeeand Nerendra
and Nerendra Modi problem is found
but heteroskedasticity
Modi but in the residuals
heteroskedasticity problem during
problem is found the tenure
in
is found of
theinresiduals duringduth
the residuals
Vajpayee
all the and Nerendra
Prime
Vajpayee Ministers
and NerendraModi
except butRajiv
Modi heteroskedasticity
butGandhi. The
heteroskedasticity problem
residuals problemare isalso
isfound not
found in the
innormally residuals
the residuals during
distributed
during the
theduring
tenuretenure of of
the
all the Prime Ministers except all theRajiv Gandhi.
allPrime
the Ministers
Prime The residuals
Ministersexceptexcept are also
Rajiv Gandhi.
Rajiv not normally Thedistributed
The residuals
Gandhi. are also
residuals during
are not
also the
normally
not normally distributed dur
distribute
all the Prime
tenure of allthe Ministers
the Prime(JB
PMs except
Ministers
statistics). RajivBut
except Gandhi.
Rajiv
the The The
Gandhi.
residuals residuals
residuals
are foundareare also
to alsobe not
not normally
normally during
stationary distributed
distributed the during
during
tenure the oftheIndira
tenure of the PMs (JB statistics). tenure But
of thetheofPMsresiduals
the(JB are found
statistics). But to bethe stationary
residuals during
are found the totenure
be of
beIndira
stationary duringduring
the tenure
tenure oftenure the PMs (JBPMs
of Bihari
the statistics). But tenure
(JB statistics). theButresiduals
the residuals PMs
are (JB
found
are statistics).
found totobebestationary But
stationary the during
residuals
during the are
thetenure
tenurefound ofto
of Indira Indira stationary the te
Gandhi
Gandhi and Atal
and Atal BihariVajpayee.
Vajpayee.
Gandhi and Atal Bihari Vajpayee.
Gandhi and Ataland
Gandhi BihariAtal Vajpayee. Gandhi and Atal Bihari Vajpayee.
TableTable3 Growth
3 Growth rate ofBihari
rate Return
of
Vajpayee.
Return of BSE
Table of 3BSEGrowth rate of Return of BSEof BSE
Table 3 Growth rate
Table 3Coefficient
Growth of rate
Return of BSE
of Growth
Return Table
of BSE 3RGrowth
2 rate of Return
Prime Minister
Prime Minister Coefficient Growth R 2 F-statF-stat Breuch- Breuch- R2 Breuch-Breuch- J-B Q-Stat
Prime MinisterPrime Minister Coefficient Prime
Coefficient Minister
Prime
Growth Minister
Growth
2 Coefficient
R R F-stat2
Coefficient
F-stat Growth
Breuch- Growth
Breuch- R2 Breuch-
Breuch- F-statF-stat J-B J-B
J-BBreuch- Breuch- Q-Stat Q-Stat Q-Stat
Breuch- Breuch- J-
% % Godfrey
%Godfrey Godfrey Pagan-
Pagan- Godfrey (Stationary)
(Stationary)
Pagan-
% % Godfrey % Pagan-
Pagan- Godfrey (Stationary)
(Stationary) Pagan-
LM LM
LMTest
Test
LM
TestTest Godfrey
Godfrey
GodfreyGodfrey LM Test Godfrey
LM Test Godfrey
(Ser. (Ser.Cor.)Cor.) Test Test Test (Ser. Cor.)
(Ser.
(Ser. Cor.)
Cor.) Test (Ser. Cor.) Test Test
(Hetero.)
(Hetero.)
(Hetero.)
(Hetero.) (Hetero.)(Hetero.)
Indira
Indira Gandhi
Indira Indira
GandhiGandhi Gandhi -0.000144 -0.000144 -0.000144
-0.000144
Indira -0.014
-0.014
Gandhi -0.014
-0.014 0.0011
0.0011 0.0011
0.0011 1.01466
1.01466
-0.000144 1.01466
1.01466 0.3626
0.3626
0.3626
-0.014 0.3626
0.0011 5.1577**
5.1577**
5.1577**
5.1577**
1.01466 7668.233**
7668.233**
7668.233**7668.233**
0.3626 Insignificant
Insignificant
Insignificant
Insignificant
5.1577** 7668.2
Indira Gandhi -0.000144 -0.014 0.0011 1.01466 0.3626 5.1577** 7
(0.3141)
(0.3141) (0.3141)
(0.3141) (0.3141)
(0.3141)
(0.3141)
(0.3141) (0.3141) (0.8342)
(0.8342)
(0.8342) (0.8342) (0.0231)
(0.0231)
(0.0231)
(0.0231)
(0.3141) (0.0000)(0.0000)
(0.0000) (0.0000)
(0.3141) (0.3141)(0.8342) (0.8342) (0.0231) (0.0231) (0.00
RajivRajiv
Rajiv Gandhi
GandhiRajiv Gandhi -0.000197
Gandhi -0.000197 -0.000197
-0.000197
Rajiv -0.019
-0.019
Gandhi -0.019
-0.019 0.0013
0.0013 0.0013
0.0013 1.4152
1.4152
-0.000197 1.4152
1.4152-0.019 32.0795**
32.0795**
32.0795** 32.0795**0.0013 2.9751
2.9751
2.9751 2.9751
1.4152 121.9854**
121.9854**
121.9854**121.9854**
32.0795** Sig. Sig. Sig.Sig. 121.98
(0.2345)Rajiv Gandhi -0.000197
(0.2345) (0.0000) -0.019
(0.0000) 0.0013
(0.0847) 1.4152
(0.0000) 32.0795**2.9751 2.9751 1
(0.2345)
(0.2345)(0.2345) (0.2345)(0.2345)
(0.2345) (0.2345) (0.0000)
(0.0000) (0.0847)
(0.0847)
(0.0847)
(0.2345) (0.0000)
(0.0000) (0.0000)
(0.0000) (0.0847) (0.00
P.V. P.V. Narasimha Rao -0.000324 -0.032 0.00252.6105 (0.2345)
2.6105 31.3032** 31.3032** 44.2694** 44.2694** (0.2345)
1635.526** (0.0000) Sig. Sig. (0.0847)
P.V.Narasimha
Narasimha
P.V. Narasimha
Rao
RaoRao -0.000324-0.000324
-0.000324
P.V.
-0.032
-0.032
Narasimha -0.032
0.0025
Rao0.0025 0.0025
-0.000324 2.6105 2.6105-0.032 31.3032**
31.3032**0.0025 44.2694**
44.2694**
2.6105
1635.526**
1635.526**
1635.526**
31.3032** Sig.Sig. 1635.5
44.2694**
(0.1065) (0.1065)P.V. Narasimha Rao -0.000324
(0.1065)
(0.1065) -0.032
(0.0000)
(0.0000) 0.0025
(0.0000)
(0.0000) 2.6105
(0.0000)
(0.0000) 31.3032** 44.2694** 1
(0.1065)
(0.1065) (0.1065)(0.1065)
(0.1065) 4.9964 (0.0000)
(0.0000) 19.1055** (0.0000)
(0.0000)
(0.1065) (0.0000)
(0.0000)
(0.0000) (0.0000)
Atal Bihari Atal Bihari
Vajpayee Vajpayee 0.000056 0.000056 0.006 0.006 0.0002 0.0002 (0.1065)
0.31780.3178 4.9964 19.1055** (0.1065)
603.0378**
603.0378** (0.0000)
Insignificant
Insignificant (0.0000) (0.00
AtalAtal
Bihari Vajpayee
Bihari Vajpayee (0.5730) 0.000056
0.000056
Atal Bihari 0.006 0.006
Vajpayee 0.0002
0.00020.000056 0.3178 0.31780.006 4.9964 4.9964
0.0002 19.1055**
19.1055**
0.3178 603.0378**
603.0378**
4.9964 Insignificant
Insignificant
(0.5730)Atal Bihari Vajpayee 0.000056
(0.5729)
(0.5729) (0.0822)
(0.0822) 0.006 0.0002
(0.0000)
(0.0000) 0.3178
(0.0000)
(0.0000) 4.996419.1055** 19.1055** 603.03 6
(0.5730)
(0.5730) (0.5730)(0.5729)
(0.5729) (0.0822)
(0.0822) (0.0000)
(0.0000)
(0.5729) (0.0000)
(0.0000)
(0.0822) (0.0000) (0.00
Dr. Manmohan Dr. Manmohan -0.000045 -0.000045 -0.005-0.005 0.0004 0.00041.0647 1.0647 16.1025**
(0.5730) 16.1025** 6.8703**
6.8703** (0.5729) 8708.093**
8708.093** (0.0822) Sig. Sig. (0.0000)
Dr. Manmohan -0.000045 -0.005-0.005 0.0004 1.0647 16.1025** 6.8703** 8708.093** Sig.Sig. 8708.0
SinghDr. Manmohan
Singh -0.000045
(0.3020) Dr. Manmohan
(0.3020)Dr. Manmohan 0.0004
-0.000045 1.0647-0.005
(0.3022)
-0.000045
(0.3022) 16.1025**
(0.0003)
(0.0003) -0.005 0.0004 6.8703**
(0.0088)
0.0004
(0.0088)1.0647 (0.0000)
1.0647 8708.093**
16.1025**
(0.0000) 16.1025**6.8703** 6.8703** 8
SinghSingh Narendra Modi (0.3020)
(0.3020)
-0.000020
Singh 0.006 0.0006(0.3020)(0.3022)
(0.3022)
0.0989 (0.0003)
(0.0003)
2.6363 (0.0088)
23.5660** (0.0088)
(0.3022) 32830.86** (0.0000)
(0.0000)
(0.0003) Sig.
Narendra Modi -0.000020 Singh 0.006 0.0006 0.0989 (0.3020) 2.6363 23.5660**(0.3022) 32830.86** (0.0003) Sig. (0.0088) (0.00
(0.0088)
Narendra
NarendraModi Modi -0.000020 (0.5964)
-0.000020
Narendra 0.006 0.006 0.0006
Modi Modi -0.000020 0.0006 0.0989
(0.7531)
0.0989 2.6363
(0.2676) 2.6363 23.5660**
(0.0000)
23.5660** 32830.86**
(0.0000) 32830.86** Sig.Sig. 32830
(0.5964) Narendra -0.0000200.006
(0.7531) (0.2676) 0.006 0.0006 (0.0000)
0.0006 0.0989 0.0989 (0.0000)2.6363 2.636323.5660** 23.5660** 3
** (0.5964)
Significance at 5% level
(0.5964) (0.5964)(0.7531)
(0.7531) (0.2676)
(0.2676) (0.0000)
(0.0000)
(0.7531) (0.0000)
(0.0000)
(0.2676) (0.0000)
** Significance at 5% level
Source:
** Significance Author’s
at 5% levelown calculation
(0.5964) (0.7531) (0.2676) (0.0000) (0.00
Source:
**Author’s ownat
Significance calculation
5% level ** Significance at 5% level
Detection Detection
of ARCH
Source: Author’s ofEffect:
ARCH Effect:
own calculation
** Significance at 5% level
Source: Author’s own calculation Source:Source:
Author’sAuthor’s
own calculation
Detection
Volatility Volatility
of ARCH
clustering clustering
Effect:
of daily ofreturn
daily return is examined
is examined by calculation
own applying
by applying ARCHtest.
ARCH test.Here,
Here, AR(1)
AR(1)model
model is is
used
usedto to
Detection of
generate ARCH
squared Effect: Detection
residuals for testing of
Detection ARCH
ARCH of Effect:
ARCH
effect. It Effect:
is found (Table 4) that both the F-statistics and LM
Volatility
generate clustering
squared
Volatility of daily
residuals
clustering return
for
of daily testing
returnis examined
Volatility ARCH
isclustering
examined byof
effect. applying
It daily
by isapplying
found ARCH istest.
(Table
ARCH
return 4) Here,
that
test.
examined AR(1)
both
Here, themodel
by AR(1) is used
F-statistics
model
applying ARCH to LM
and
is used
test. to
Here, AR(1) model is use
Volatility clustering of daily return is examined by applying ARCH test. Here, AR(1) model
generate squared
generate residuals
squared forgenerate
residuals testing
for ARCH
testing effect.
ARCH It isItfound
effect. (Table
isforfound 4) that
(Table both
4)ARCH
that Itthe
both F-statistics
isthe (Tableand LMLM
(IJA)squared
34 ◆ Indian Journal of Accountinggenerate residuals
squared
Volume: 52 (2)residuals
December testing
for ARCH
testing
2020 effect.
effect. It F-statistics
foundis found 4)and
that
(Table
45 4)both
that the
bothF-statistic
the F-st
45
4545statistics are statistically significant at five percent level during the tenure of the Prime Ministers that
statistics are
statistics are statistically
statistically significant
significant at at five
five percent
percent levellevel during
during the
the tenure
tenure of of the
the Prime
Prime
means presence of ARCH effect in return series. It is also observed that the return volatility is lower during
statistics areofstatistically significant means
at means
five presence
presence
percent of
level of ARCH effect
ARCH effect
during in
in ofreturn
return series.
series. It is also
It is alsothatobserved that the return
observed that the return vola vola
the regime Indira Gandhi (6.0951) who was elected fromthe
INCtenure
as compared the Prime
to theMinisters
other PMs and higher
means the regime
theseries.
regime of
It of Indira
Indira Gandhi
Gandhithat (6.0951)
(6.0951) who was elected
who volatility
was elected from INC
fromduring as compared to
INC as compared to the oththe ot
duringpresence
the time of ofARCH effect in return
P.V. Narasimha Rao (7.7133) iswho
also observed
was elected from theINC return
and then Dr.isMonmohan
lower Sing
the regime of Indira Gandhi (6.0951) who during
during the
wasthe timefrom
time
elected of P.V.
of P.V. Narasimha
INCNarasimha
as compared Rao
Rao (7.7133)
to(7.7133) who
the otherwho PMswaswas elected
andelected from INC
higher from INC and
and then
then Dr
Dr
(7.0310) who was elected from INC and so on based on AIC criterion. But in a nutshell, the return volatility
during the time of P.V. Narasimha Rao(7.0310) (7.0310) who
(7.7133) who was was elected
was elected from
electedfrom
fromINC INC
INCand and
andthenso on
so on based
Dr.based
Monmohan on AIC
on AICSingcriterion. But in a nutshell,
criterion. But in a nutshell,
is almost same according to AIC criteria.
(7.0310) who was elected from INC and isisso
almost same
on based
almost same according
onaccording to AIC
AIC criterion.
to AIC criteria.
Butcriteria.
in a nutshell, the return volatility
isTable
almost sameofaccording
4 Test to AIC criteria.
ARCH Effect
Table 44 Test
Table Test ofof ARCH
ARCH Effect
Effect
Table 4 TestPrimeof ARCH Effect
Minister F-Stat. Obs*R2 AIC
Prime Minister
Prime Minister F-Stat.
F-Stat. Obs*R22
Obs*R
Indira GandhiPrime Minister 4.7561**
F-Stat. Obs*R4.7538**
2 6.0951
Indira Gandhi
Indira Gandhi 4.7561**AIC
4.7561** 4.7538**
4.7538**
Indira Gandhi (0.0232)
4.7561** 4.7538**(0.0109) 6.0951
(0.0232)
(0.0232) (0.0109)
(0.0109)
Rajiv Gandhi 6.3019**
(0.0232) 6.2764**
(0.0109) 6.1746
Rajiv Gandhi
Rajiv Gandhi 6.3019**
6.3019** 6.2764**
6.2764**
Rajiv Gandhi (0.0122)
6.3019** 6.2764**(0.0122) 6.1746
(0.0122)
(0.0122) (0.0122)
(0.0122)
P.V. Narasimha Rao 105.4367**
(0.0122) 96.0869**
(0.0122) 7.7133
P.V. Narasimha
P.V. Narasimha Rao Rao 105.4367**
105.4367** 96.0869**
96.0869**
P.V. Narasimha Rao (0.0000)
105.4367** 96.0869**(0.0000) 7.7133
(0.0000)
(0.0000) (0.0000)
(0.0000)
Atal Bihari Vajpayee 204.5026**
(0.0000) 180.7093**
(0.0000) 6.5219
Atal Bihari Vajpayee
Atal Bihari Vajpayee 204.5026**
204.5026** 180.7093**
180.7093**
Atal Bihari Vajpayee (0.0000)
204.5026** 180.7093**(0.0000) 6.5219
(0.0000)
(0.0000) (0.0000)
(0.0000)
Dr. Manmohan Singh 35.1090**
(0.0000) 34.6487**
(0.0000) 7.0310
Dr. Manmohan
Dr. Manmohan Singh Singh 35.1090**
35.1090** 34.6487**
34.6487**
Dr. Manmohan Singh (0.0000)
35.1090** 34.6487**(0.0000) 7.0310
(0.0000)
(0.0000) (0.0000)
(0.0000)
Narendra Modi 65.3867**
(0.0000) 62.7374**
(0.0000) 6.3944
Narendra
Narendra Modi
Modi 65.3867**
65.3867** 62.7374**
62.7374**
Narendra Modi (0.0000)
65.3867** 62.7374**(0.0000) 6.3944
(0.0000)
(0.0000) (0.0000)
(0.0000)
** Significance at 5% level (0.0000)
** Significance at 5% level
(0.0000)
Source: Author’s own calculation ** Significance at 5% level
** Significance at 5% level
Source:Author’s
Source: Author’sown
owncalculation
calculation
The estimated
Source: Author’s own outcome
calculation of
GARCH model is presented in table 5. It is observed that the lagged squared
The estimated
The estimated outcome
outcome of of GARCH
GARCH model
model isis presented
presented in in table
table 5.
5. ItIt isis observed
observed that
that the
the
The estimated
residuals outcomeof
‘coefficients of the
GARCHreturnmodel is presented
during the tenure in table
of the5.Indian
It is observed that the lagged
Prime Ministers squared
are significant as the
residuals ‘coefficients residuals
residuals ‘coefficients
‘coefficients of the
of thePrime
return
return during the
during the tenure
tenure ofof the
the Indian Prime
Indian Prime Ministers
Ministers are
are
probability values areofless
the than
return during
five the tenure
percent that meansof theexistence
Indian Ministers
of volatility are significant
clustering (ARCH as Effect)
the that
probability values are volatility
less than of fiverisk probability
probability
percent values are
valuesexistence less
are less of than
than five percent
five percent that means
that(ARCH
means existence
existence of
that ofthe volatility clusterin
volatility clustering
also confirms about whichthat means
is affected significantly volatility
by the pastclustering
squared Effect)
residuals during
also confirms also
also confirms
confirms about
about volatility
volatility of
of risk
risk which
which is
is affected
affected significantly
significantly by
by the past
the past squared
squared r
tenure of theabout
Primevolatility
Ministers.of risk which isthe
Similarly, affected significantly
coefficients of theby the past
lagged squared residuals
conditional variance during
in the the
tenure of thevariance
Prime Ministers. tenure
tenure of the
of the Prime
Prime Ministers.
Ministers. Similarly, the
Similarly, the coefficients
coefficients of the
of the lagged
lagged conditional
conditional varia
varia
conditional equationSimilarly,
(GARCHthe coefficients
Effect) of the
are significant lagged
as theconditional
probabilityvariance
values arein the
less than five
conditional variance equation (GARCH conditional
conditional
Effect)ofare variance equation
variance equation
significant (GARCH Effect)
(GARCH Effect)are
as the probability are significant
areless
significant as
than fiveasarethe probability values aa
the probability values
percent meaning that the past volatilities BSE returns during the tenurevaluesof the Prime Ministers’
percent meaning that the past volatilitiespercent
percent meaning
of BSEmeaning
returns that the
that the past
thepast volatilities
volatilities of BSE
of BSE returns during
returns during the
the tenuretenure ofof the
the Prime
Prime
significantly influence current returns. Moreover, theduring
summation tenure
of ARCHof the
andPrime
GARCH Ministers’
effectsaremeasure the
significantly influence current returns. significantly
significantly influence
influence current
current returns.
returns. Moreover,
Moreover, the
the summation
summation of
of ARCH
ARCH and
and GARCH
GARCH
shock persistence which is very closeMoreover, the summation
to unity during their tenure.of ARCH and GARCH effects measure the
shock persistence which is very close shock
shock
to unity persistence
persistence
during their which
which
tenure. is very
is very close
close toto unity
unity during
during their
their tenure.
tenure.
Table 5 Estimation of GARCH
Table 5 Estimation of GARCH Table 5 Estimation
Table 5 Estimation of GARCH of GARCH
GARCH = C(3) + C(4)*RESID(-1)^2GARCH + C(5)*GARCH(-1)
GARCH == C(3)
C(3) ++ C(4)*RESID(-1)^2
GARCH = C(3) + C(4)*RESID(-1)^2 + C(5)*GARCH(-1) C(4)*RESID(-1)^2 ++ C(5)*GARCH(-1)
C(5)*GARCH(-1)
Prime Minister C(3) Prime Minister
C(4) C(5)
C(3) C(4) +
C(4) C(5) AIC
C(5) C(4)
Prime Minister C(3) Prime Minister
C(4) C(5) C(3) C(4) +C(4)
C(5) AICC(5) C(4)
Indira Gandhi
Indira Gandhi
0.2006**
0.2006**Indira
0.2194**
Indira0.2194**
Gandhi 0.6539**
0.2006**
0.6539**
0.8733
0.2194**
0.8733
2.9629
0.6539**
2.9629 0.8
Gandhi 0.2006** 0.2194** 0.6539** 0.8
(0.0000)
(0.0000) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000)
RajivGandhi
Rajiv Gandhi 0.0558**
0.0558** Rajiv 0.0609**
0.0609**
Gandhi 0.9205**
0.9205**
0.0558** 0.98140.9814
0.0609** 3.74943.7494
0.9205** 0.9
Rajiv Gandhi 0.0558** 0.0609** 0.9205** 0.9
(0.0003)
(0.0003) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0003) (0.0000) (0.0000)
(0.0003) (0.0000) (0.0000)
P.V.Narasimha
P.V. NarasimhaRao
Rao 0.0579**
0.0579** P.V. 0.1088**Rao 0.8777**
0.1088**
Narasimha 0.8777**
0.0579** 0.98650.9865
0.1088** 3.85253.8525
0.8777** 0.9
P.V. Narasimha Rao 0.0579** 0.1088** 0.8777** 0.9
(0.0016)
(0.0016) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0016) (0.0000) (0.0000)
(0.0016) (0.0000) (0.0000)
AtalBihari
Atal BihariVajpayee
Vajpayee 0.1629**
0.1629** 0.1651**
0.1651**
Atal Bihari
Bihari Vajpayee
Vajpayee 0.7881**
0.7881**
0.1629** 0.95320.9532
0.1651** 3.75873.7587
0.7881** 0.9
Atal 0.1629** 0.1651** 0.7881** 0.9
(0.0000)
(0.0000) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000)
Dr.Manmohan
Dr. ManmohanSingh
Singh 0.0275**
0.0275** Dr. 0.0924**Singh0.8965**
0.0924**
Manmohan 0.8965**
0.0275** 0.98890.9889
0.0924** 3.36573.3657
0.8965** 0.9
Dr. Manmohan Singh 0.0275** 0.0924** 0.8965** 0.9
(0.0000)
(0.0000) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000)
NarendraModi
Narendra Modi 0.0053**
0.0053** 0.1061**
0.1061**
Narendra Modi
Modi 0.8697**
0.8697**
0.0053** 0.97580.9758
0.1061** 2.58642.5864
0.8697** 0.9
Narendra 0.0053** 0.1061** 0.8697** 0.9
(0.0004)
(0.0004) (0.0000)
(0.0000) (0.0000)
(0.0000)
(0.0004) (0.0000) (0.0000)
**Significance
** Significanceatat5%5%level
level
(0.0004) (0.0000) (0.0000)
**Significance
** Significanceat
at5%5%level
level
Source:
Source:Author’s
Author’sownowncalculation
calculation
Source: Author’s own calculation
Source: Author’s own calculation
Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020 ◆ 35
46 46Table 6 presents the estimated result of EGARCH model. It is observed that the constant terms in
variance equation are statistically significant and the persistence of conditional volatility (volatil-
ity clustering) exists in return during the tenure of the Prime Ministers as depicted by the proba-
bility
Tablevalues.
6 presentsThetheGARCH
estimatedcoefficients
result ofTableof6 the
EGARCH return
presents
model. It isseries
the estimated
observedduring
result their
that the tenure
of constant
EGARCH are in
model.
terms statistically
is observed sig-
It variance that the constant te
nificant
equationmeaning that significant
are statistically past shocks persistence
andequation
the persistence significantly
are statistically influences
significant
of conditional and the
volatility current
persistence
(volatility returns. The study
of conditional
clustering) exists volatility (volatility c
also examines
in return during existence
the tenure of ofthe PrimeinMinisters
leverage return
effectduring
on the tenure
return
as depicted duringof the
by the Prime
their Ministers
tenure.
probability values.The asgamma
The depicted by the probability values. Th
GARCH coefficients
ofcoefficients
BSE return of the returnthe
during series
occupancy coefficients
during theiroftenure of
arethe
the Prime return series
statistically
Ministers during
significant
are their
zerotenure
nonmeaning thatare
meaning paststatistically
shocks
that significant meaning tha
presence
ofpersistence
asymmetric significantly
effect in influences persistence
current
the volatilities. Here,significantly
returns. The
thestudy influences
also examines
γ coefficients current
BSE returns.
ofexistence The
of leverage
return duringstudy alsoon
effect
their examines
regime existence of le
return during and
theirsignificant.
tenure. The gamma return during their tenure. The gamma coefficients of BSE return during the occupancy of t
are positive It maycoefficients
be said that of BSE return during
leverage effectthe occupancy
doesn’t of the
exist in thePrimereturn during
Ministers are non zero meaning that presence Ministers of are non zeroeffect
asymmetric meaning thatvolatilities.
in the presence of asymmetric
Here, the γ effect in the volatilities. Here
the Prime Ministers’ tenure that implies presence of positive correlation between the past re-
coefficients
coefficients of BSE return during their regime of BSE return
are positive during their
and significant. regime
It may are that
be said positive and significant.
leverage effect It may be said th
turn and future volatility of the return or in other words, positive shocks (good news) generates
doesn’t exist in the return during the Prime doesn’t exist in tenure
Ministers’ the returnthatduring
impliesthe Prime Ministers’
presence of positivetenure that implies presence of posi
correlation
less volatility than negative shocksbetween (bad news).
thereturnThus,
past return return of
and future the BSE
volatility is less volatile induring the positive shock
between the past return and future volatility of the or in other words, positiveofshocks
the return
(goodornews) other words,
administration of thethan
generates less volatility PMs. generates
negative shocks (badless volatility
news). Thus,than
returnnegative
of the BSEshocks (bad
is less news).during
volatile Thus, the
return of the BSE is less vol
administration of the PMs. administration of the PMs.
Table 6 Estimation of EGARCH model Table 6 Estimation of EGARCH model
LOG(GARCH)
LOG(GARCH) = C(3) + C(4)*ABS(RESID(-1)/@SQRT(GARCH(-1))) = C(3) + C(4)*ABS(RESID(-1)/@SQRT(GARCH(-1)))
+ C(5)*RESID(-1)/@SQRT(GARCH(-1) + C(5)*RESID(-1)/@SQRT(GARCH(-1) + C(6)*
+ C(6)*LOG(GARCH(-1))
Prime Minister C(3) Prime Minister
C(4) C(5) C(3) C(4)
C(6) C(5)
AIC C(6)
Indira Gandhi -0.1915** Indira0.3014**
Gandhi -0.1915**
0.0457** 0.3014**
0.8812** 0.0457**
2.9511 0.8812*
(0.0000) (0.0000) (0.0000)
(0.0029) (0.0000) (0.0029) (0.0000
Rajiv Gandhi -0.0787** Rajiv 0.1464**
Gandhi -0.0787**
-0.0190 0.1464**
0.9654** -0.0190
3.7432 0.9654*
(0.0000) (0.0000) (0.0000)
(0.1480) (0.0000) (0.1480) (0.0000
P.V. Narasimha Rao -0.1445** P.V. Narasimha
0.2112** Rao -0.1445**
0.0312** 0.2112**
0.9808** 0.0312**
3.8562 0.9808*
(0.0000) (0.0000) (0.0000)
(0.0316) (0.0000) (0.0316) (0.0000
Atal Bihari Vajpayee -0.1517** Atal Bihari Vajpayee
0.2949** -0.1517**
-0.1301** 0.2949**
0.9155** -0.1301**
3.7356 0.9155*
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000) (0.0000
Dr. Manmohan Singh -0.1376** Dr. Manmohan
0.1947** Singh -0.1376**
-0.0787** 0.1947**
0.9781** -0.0787**
3.3567 0.9781*
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000) (0.0000
Narendra Modi -0.0919** Narendra Modi
0.1078** -0.0919**
-0.1446** 0.1078**
0.9742** -0.1446**
2.5275 0.9742*
(0.0000) (0.0000) (0.0000)
(0.0000) (0.0000) (0.0000) (0.0000
** Significance at 5% level ** Significance at 5% level
Source: Author’s own calculation Source: Author’s own calculation
Table 7 presents the outcome of TGARCH Table 7 presents
estimation. the the
Here, outcome
constantof TGARCH estimation.during
term is significant Here,thethe constant term is significant
tenure7ofpresents
Table the PrimetheMinisters.
outcome The GARCH tenure
of TGARCH of estimation.
the Prime
coefficients Ministers.
Here, The
are statistically GARCH
significant
the constant coefficients
that term isare
confirms statisticallyduring
about
significant significant that confi
previous
the tenurereturn volatility
of the Prime significantly
Ministers. previous
influence return coefficients
current
The GARCH volatility
returns. The significantly
γare
values influence
during thecurrent
statistically tenure returns. Thecon-
of the that
significant Prime γ values during the ten
Ministers
firms aboutare previous
not zero that meansvolatility
return Ministers
presence are not zero
of asymmetric
significantly that in
shocks
influence means
the presence
return.
current aof
fallasymmetric
Ifreturns. in return shocks induring
The γisvalues the return. If a fall in r
accompanied
the tenure ofbythe an increase in volatilityaccompanied
Prime Ministers greater
are notthan
by anvolatility
zerothe
increaseinduces
that means
in volatility
by angreater
presence increase
of
than the volatility
in return
asymmetric then induces
shocks in the
by an increase i
leverage effect exist or in other words, leverage effect exist or in other words, leverage
means negative correlation between past returnseffect means negative correlation between
return. If a fall in return is accompanied by an increase in volatility greater than the volatility in-
and future volatility of returns (bad news andhas
future
morevolatility
impact on of returns
volatility(bad news has
of return thanmore
goodimpact
news).on volatility of return than good
Here,
duces by an increase in return then leverage effect exist or in other words, leverage effect means
the C(5) coefficients during the time of the AtalC(5) coefficients
Bihari Vajpayee,during the time of
Dr. Monmohan Ataland
Sing Bihari Vajpayee,
Narendra ModiDr.areMonmohan Sing and Nare
negative correlation between pastpositive returns and future
and statistically volatility
significant of returns (bad news has more
positive and statistically significant as the probabilities values are less thanas thepercent
five probabilities values are less
that means than five percent tha
impact on volatility of return than good news).
leverage effect
leverage effect exists during their administration. Here,
Thus,exists
it may the
during
be saidC(5)
their coefficients
thatadministration.
negative newsThus, during the time
it mayimpact
has more of Atalnegative news h
be said that
Bihari Vajpayee,
on conditional Dr. Monmohan
volatility of return thanSingthe and Narendra
on conditional
positive news Modi
volatility
during are
oftheir
return positive
thanasthe
tenure and statistically
positive
compared news
to during
other significant
Primetheir tenure as compared t
asMinisters.
the probabilities values are lessMinisters. than five percent that means leverage effect exists during
their administration. Thus, it may be said that negative news has more impact on conditional vol-
atility of return than the positive news during their tenure as compared to other Prime Ministers.
36 ◆ Indian Journal of Accounting (IJA) Volume: 52 (2) December 2020You can also read
