Table A2. Estimate variance equation in...

Table A2.

Estimate variance equation in BSE

	GARCH				PARCH
	Pre-L2	Pre-L1	Post-L1	Post-L2	Pre-L2	Pre-L1	Post-L1	Post-L2
C	0.496*	0.027*	0.025*	0.024*	0.031*	0.023*	0.024*	0.023*
α_i	0.144*	0.107*	0.101*	0.099*	0.121*	0.116*	0.109*	0.107*
β_i	0.801*	0.886*	0.894*	0.896*	0.173*	0.165*	0.141*	0.164*
γ					0.880*	0.895*	0.895*	0.896*
δ					1.495*	1.274*	1.642*	1.649*
Buddha Purnima	−0.694	0.011	0.175	0.090	0.017	−0.023	−0.031	0.055
Christmas	6.249*	4.495*	−0.079	−0.033	2.784	1.975*	−0.289*	−0.029
Diwali	−0.991*	−0.264*	−0.299*	−0.127*	−0.111**	−0.230*	−0.135	−0.139**
Dussehra	−0.939*	−0.291***	−0.159	−0.100	−0.123**	−0.192**	−0.127	−0.056
Eid al fitr	−1.023*	−0.226**	−0.158	−0.077	−0.100**	−0.163**	−0.063	−0.048
Ganesh Chaturthi	−0.929*	0.028	−0.094	0.011	0.044	0.042	0.079	0.009
Good Friday	−0.353	−0.017	0.096	−0.008	−0.044	−0.031	−0.036	−0.013
Gurunanak	−0.447**	0.019	−0.052	−0.050	0.042	0.018	−0.057	−0.013
Holi	−0.303	0.051	−0.041	0.026	0.052	0.022	−0.123	0.028
Mahasahivaratri	−0.262	−0.317**	−0.142	−0.068	−0.147***	−0.237*	−0.243	−0.050
Mahavir Jayanti	−0.655	−0.104	−0.277	−0.095	0.010	−0.060	−0.080	−0.071
Muharram	−0.298	−0.029	−0.034	0.015	−0.037	−0.070	0.132	−0.027

	GARCH				PARCH
	Pre-L2	Pre-L1	Post-L1	Post-L2	Pre-L2	Pre-L1	Post-L1	Post-L2
C	0.496*	0.027*	0.025*	0.024*	0.031*	0.023*	0.024*	0.023*
α_i	0.144*	0.107*	0.101*	0.099*	0.121*	0.116*	0.109*	0.107*
β_i	0.801*	0.886*	0.894*	0.896*	0.173*	0.165*	0.141*	0.164*
γ					0.880*	0.895*	0.895*	0.896*
δ					1.495*	1.274*	1.642*	1.649*
Buddha Purnima	−0.694	0.011	0.175	0.090	0.017	−0.023	−0.031	0.055
Christmas	6.249*	4.495*	−0.079	−0.033	2.784	1.975*	−0.289*	−0.029
Diwali	−0.991*	−0.264*	−0.299*	−0.127*	−0.111**	−0.230*	−0.135	−0.139**
Dussehra	−0.939*	−0.291***	−0.159	−0.100	−0.123**	−0.192**	−0.127	−0.056
Eid al fitr	−1.023*	−0.226**	−0.158	−0.077	−0.100**	−0.163**	−0.063	−0.048
Ganesh Chaturthi	−0.929*	0.028	−0.094	0.011	0.044	0.042	0.079	0.009
Good Friday	−0.353	−0.017	0.096	−0.008	−0.044	−0.031	−0.036	−0.013
Gurunanak	−0.447**	0.019	−0.052	−0.050	0.042	0.018	−0.057	−0.013
Holi	−0.303	0.051	−0.041	0.026	0.052	0.022	−0.123	0.028
Mahasahivaratri	−0.262	−0.317**	−0.142	−0.068	−0.147***	−0.237*	−0.243	−0.050
Mahavir Jayanti	−0.655	−0.104	−0.277	−0.095	0.010	−0.060	−0.080	−0.071
Muharram	−0.298	−0.029	−0.034	0.015	−0.037	−0.070	0.132	−0.027

Notes: This table represents the estimate variance equation regression specification using the GARCH and PARCH Model. α_i represent the ARCH term that measures the impact of past innovation on current variance. β_i is the GARCH model that measures the impact of past variance on current variance. The parameter γ captures asymmetry in the model (γ < 0, γ ≠ 0 ∼ asymmetric). The power term δ capture both standard deviation (δ = 1) and conditional variance (δ = 2). Eview legacy technique was adopted to maximize the log-likelihood function of the GED. The ARCH LM test the null hypothesis of no autocorrelation up to 10 lags. The Ljung-Box Q-square statistics up to order 10 failed to rejected the null hypothesis of no autocorrelation. The null hypothesis of Wald test is H0: β1 = β2 …. = β12 = 0. *, ** and ***represent statistical significance at 1, 5 and 10% levels, respectively

Source: Table created by authors

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