Table 1. Pre-Estimation Analysis... | Emerald Publishing

Table 1.

Pre-Estimation Analysis

Variable	Obs.	Mean	SD	Min	Max	Dickey-Fuller	Phillips-Perron
Panel 1 - 1. Descriptive statistics and stationary test
digital^GSVI	209	0.8934456	1.048732	−2.236945	6.389808	−8.356***	−8.562***
return^VNIndex	209	0.0009842	0.0267742	−0.1025858	0.1027658	−10.203***	−10.013***
return^VN³⁰	209	0.0015518	0.0277948	−0.1057483	0.1014413	−10.333***	−10.170***
return^VN¹⁰⁰	209	0.0015375	0.0280615	−0.1006965	0.1004145	−10.199***	−10.026***

Variable	Obs.	Mean	SD	Min	Max	Dickey-Fuller	Phillips-Perron
Panel 1 - 1. Descriptive statistics and stationary test
digital^GSVI	209	0.8934456	1.048732	−2.236945	6.389808	−8.356***	−8.562***
return^VNIndex	209	0.0009842	0.0267742	−0.1025858	0.1027658	−10.203***	−10.013***
return^VN³⁰	209	0.0015518	0.0277948	−0.1057483	0.1014413	−10.333***	−10.170***
return^VN¹⁰⁰	209	0.0015375	0.0280615	−0.1006965	0.1004145	−10.199***	−10.026***

Lag/Rank	FPE	AIC	HQIC	SBIC	LL	Eigenvalue	Trace statistic	5% critical value
Panel 1–2. Optimal lag selection test and co-integration test
Model 1: return^VNIndex = f(digital^GSVI)
0	0.000804	−1.45052	−1.43741	−1.4181	153.41551		101.3143	15.41
1	0.000544	−1.83993	−1.80059*	−1.74267*	188.03613	0.28430	32.0731	3.76
2	0.000534*	−1.85918*	−1.79361	−1.69708	204.07266	0.14354
3	0.000545	−1.83917	−1.74738	−1.61223
Model 2: return^VN³⁰ = f(digital^GSVI)
0	0.000869	−1.37205	−1.35894	−1.33963	143.51846		101.3389	15.41
1	0.000595	−1.75071	−1.71137*	−1.65345*	178.00572	0.28338	32.3643	3.76
2	0.000588*	−1.76279*	−1.69723	−1.6007	194.18788	0.14474
3	0.0006	−1.74222	−1.65043	−1.51529
Model 3: return^VN¹⁰⁰ = f(digital^GSVI)
0	0.000886	−1.35264	−1.33952	−1.32022	143.51512		101.6002	15.41
1	0.000598	−1.74545	−1.70611*	−1.64819*	178.2343	0.28498	32.1618	3.76
2	0.000587*	−1.76393*	−1.69836	−1.60183	194.31521	0.14390
3	0.000603	−1.73714	−1.64535	−1.5102

Lag/Rank	FPE	AIC	HQIC	SBIC	LL	Eigenvalue	Trace statistic	5% critical value
Panel 1–2. Optimal lag selection test and co-integration test
Model 1: return^VNIndex = f(digital^GSVI)
0	0.000804	−1.45052	−1.43741	−1.4181	153.41551		101.3143	15.41
1	0.000544	−1.83993	−1.80059*	−1.74267*	188.03613	0.28430	32.0731	3.76
2	0.000534*	−1.85918*	−1.79361	−1.69708	204.07266	0.14354
3	0.000545	−1.83917	−1.74738	−1.61223
Model 2: return^VN³⁰ = f(digital^GSVI)
0	0.000869	−1.37205	−1.35894	−1.33963	143.51846		101.3389	15.41
1	0.000595	−1.75071	−1.71137*	−1.65345*	178.00572	0.28338	32.3643	3.76
2	0.000588*	−1.76279*	−1.69723	−1.6007	194.18788	0.14474
3	0.0006	−1.74222	−1.65043	−1.51529
Model 3: return^VN¹⁰⁰ = f(digital^GSVI)
0	0.000886	−1.35264	−1.33952	−1.32022	143.51512		101.6002	15.41
1	0.000598	−1.74545	−1.70611*	−1.64819*	178.2343	0.28498	32.1618	3.76
2	0.000587*	−1.76393*	−1.69836	−1.60183	194.31521	0.14390
3	0.000603	−1.73714	−1.64535	−1.5102

Note(s):

* is the suggestion of lag-order selection; *** indicates that the variable is stationary at significance levels of 1%

Source(s): the authors