Table A3. Estimate variance equation in...

Table A3.

Estimate variance equation in NSE

	GARCH				PARCH
	Pre-L2	Pre-L1	Post-L1	Post-L2	Pre-L2	Pre-L1	Post-L1	Post-L2
C	0.238*	1.289*	0.147*	0.039*	0.271*	0.034*	0.021*	0.026*
α_i	0.133*	0.143*	0.142*	0.125*	0.144*	0.127*	0.108*	0.110*
β_i	0.847*	0.707*	0.847*	0.858*	0.086**	0.187*	0.155	0.251*
γ					0.810*	0.869*	0.896*	0.895*
δ					1.545*	1.598*	1.615*	1.302*
Buddha Purnima	−0.009	−2.515**	1.340***	0.086	−0.287***	0.219	0.105	0.006
Christmas	6.042*	10.318*	0.024	4.865*	2.623*	6.710*	0.022	0.639*
Diwali	−0.572*	−2.250*	−0.511*	0.057	−0.546*	−0.086	−0.092	0.055
Dussehra	−0.575	−1.770***	−0.384*	−0.060	−0.394**	−0.155	−0.137***	−0.034
Eid al fitr	−0.473*	−2.048*	−0.720*	−0.134**	−0.431	−0.072	0.095	−0.087*
Ganesh Chaturthi	−0.565	−2.602*	−0.632***	0.052	−0.415*	0.104	0.113	0.049
Good Friday	0.071	−1.307	−0.235	0.112	−0.102	0.147	−0.092	0.030
Gurunanak	−0.026	−1.751**	0.181	−0.104***	−0.114	0.121	−0.030	−0.059***
Holi	−0.290	−0.443	−0.128	−0.021	−0.190	0.004	−0.043	0.017
Mahashivaratri	−0.511*	−2.175***	−0.553***	−0.051	−0.393**	−0.221*	−0.090	−0.001
Mahavir ayanti	−0.575*	−1.097	−0.658	−0.049	−0.361***	−0.173	−0.036	0.002
Muharram	−0.073	−0.119	−0.518*	−0.002	0.008	−0.162	−0.042	−0.056

	GARCH				PARCH
	Pre-L2	Pre-L1	Post-L1	Post-L2	Pre-L2	Pre-L1	Post-L1	Post-L2
C	0.238*	1.289*	0.147*	0.039*	0.271*	0.034*	0.021*	0.026*
α_i	0.133*	0.143*	0.142*	0.125*	0.144*	0.127*	0.108*	0.110*
β_i	0.847*	0.707*	0.847*	0.858*	0.086**	0.187*	0.155	0.251*
γ					0.810*	0.869*	0.896*	0.895*
δ					1.545*	1.598*	1.615*	1.302*
Buddha Purnima	−0.009	−2.515**	1.340***	0.086	−0.287***	0.219	0.105	0.006
Christmas	6.042*	10.318*	0.024	4.865*	2.623*	6.710*	0.022	0.639*
Diwali	−0.572*	−2.250*	−0.511*	0.057	−0.546*	−0.086	−0.092	0.055
Dussehra	−0.575	−1.770***	−0.384*	−0.060	−0.394**	−0.155	−0.137***	−0.034
Eid al fitr	−0.473*	−2.048*	−0.720*	−0.134**	−0.431	−0.072	0.095	−0.087*
Ganesh Chaturthi	−0.565	−2.602*	−0.632***	0.052	−0.415*	0.104	0.113	0.049
Good Friday	0.071	−1.307	−0.235	0.112	−0.102	0.147	−0.092	0.030
Gurunanak	−0.026	−1.751**	0.181	−0.104***	−0.114	0.121	−0.030	−0.059***
Holi	−0.290	−0.443	−0.128	−0.021	−0.190	0.004	−0.043	0.017
Mahashivaratri	−0.511*	−2.175***	−0.553***	−0.051	−0.393**	−0.221*	−0.090	−0.001
Mahavir ayanti	−0.575*	−1.097	−0.658	−0.049	−0.361***	−0.173	−0.036	0.002
Muharram	−0.073	−0.119	−0.518*	−0.002	0.008	−0.162	−0.042	−0.056

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 captures 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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