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Table 3 Estimates of multivariate VAR-AGARCH model for the Bitcoin, Ethereum, and Litecoin

From: Discovering interlinkages between major cryptocurrencies using high-frequency data: new evidence from COVID-19 pandemic

 

Pre COVID-19

COVID-19

Coefficient

P value

Coefficient

P value

Panel A. Mean equation

\(\mu_{1}\)

 − 0.000

0.380

0.000

0.349

\(\emptyset_{11}\)

 − 0.171a

0.000

 − 0.093b

0.019

\(\emptyset_{12}\)

 − 0.018

0.501

0.076c

0.052

\(\emptyset_{13}\)

0.102c

0.072

0.083

0.150

\(\mu_{2}\)

 − 0.000

0.277

0.000c

0.052

\(\emptyset_{21}\)

0.067 a

0.000

0.018

0.499

\(\emptyset_{22}\)

 − 0.081a

0.000

 − 0.221a

0.000

\(\emptyset_{23}\)

0.182a

0.000

0.132c

0.094

\(\mu_{3}\)

 − 0.000

0.323

0.000

0.249

\(\emptyset_{31}\)

0.017c

0.071

0.015

0.509

\(\emptyset_{32}\)

0.026c

0.052

0.043

0.223

\(\emptyset_{33}\)

 − 0.225a

0.000

 − 0.188a

0.000

Panel B. Variance equation

\(c_{1}\)

0.000a

0.000

0.000b

0.012

\(c_{2}\)

0.001a

0.000

0.001a

0.000

\(c_{3}\)

0.000a

0.000

0.001a

0.000

\(a_{11}\)

0.073b

0.018

 − 0.029c

0.070

\(a_{12}\)

0.014b

0.019

0.042a

0.000

\(a_{13}\)

0.027a

0.000

 − 0.002c

0.055

\(a_{21}\)

0.011c

0.068

0.061a

0.000

\(a_{22}\)

0.045a

0.000

0.150a

0.000

\(a_{23}\)

0.060a

0.000

0.015c

0.061

\(a_{31}\)

0.002

0.293

 − 0.065a

0.000

\(a_{32}\)

0.117a

0.001

0.083a

0.000

\(a_{33}\)

0.181a

0.000

0.063a

0.000

\(b_{11}\)

0.880a

0.000

0.995a

0.000

\(b_{12}\)

 − 0.144a

0.000

0.037a

0.000

\(b_{13}\)

 − 0.129a

0.000

0.025b

0.040

\(b_{21}\)

 − 0.058b

0.032

 − 0.035c

0.052

\(b_{22}\)

1.256a

0.000

0.874a

0.000

\(b_{23}\)

 − 0.269a

0.000

 − 0.019c

0.094

\(b_{31}\)

 − 0.153

0.113

0.027c

0.079

\(b_{32}\)

1.028a

0.000

0.069a

0.000

\(b_{33}\)

0.860a

0.000

0.938a

0.000

\(d_{1}\)

 − 0.035b

0.043

0.041b

0.018

\(d_{2}\)

 − 0.016c

0.061

0.047a

0.001

\(d_{3}\)

 − 0.017c

0.082

0.034a

0.006

Panel C: Constant correlations

\(p_{21}\)

0.783a

0.000

0.860a

0.000

\(p_{31}\)

0.644a

0.000

0.802a

0.000

\(p_{32}\)

0.691a

0.000

0.831a

0.000

Panel D: Robustness tests

Log L

11,912.2

 

24,025.6

 

AIC

 − 20.972

 

 − 20.392

 

SIC

 − 20.850

 

 − 19.918

 

\(Q_{1}\)(20)

42.173a

0.003

36.776b

0.012

\(Q_{2}\)(20)

45.954a

0.000

36.444b

0.013

\(Q_{3}\)(20)

22.238

0.245

25.542

0.181

\(Q_{1}^{2}\)(20)

2.610

0.991

18.776

0.352

\(Q_{2}^{2}\)(20)

4.859

0.988

14.444

0.415

\(Q_{3}^{2}\)(20)

5.166

0.981

10.542

0.480

  1. # of lags for VAR is decided using SIC and AIC criteria. JB, Q(20), and Q2(20) indicate the empirical statistics of Jarque–Bera test for normality, Ljung–Box Q statistics of order 20 for autocorrelation applied to the standardized residuals and squared standardized residuals, respectively. BTC, Bitcoin; ETH, Ethereum; LTC, Litecoin. Variable order is the Bitcoin (1), Ethereum (2), and Litecoin (3). In the mean equations, \(\mu\) denotes the constant terms, whereas \(\emptyset_{12}\) denotes the return spillover from Bitcoin to Ethereum. In the variance equation, 'c' denotes the constant terms, 'a' denotes the ARCH terms, and 'b' denotes the GARCH terms. In the variance equation, \(a_{12}\) indicates the shock spillover from Bitcoin to Ethereum, whereas \({\text{b}}_{12}\) denotes the long-term volatility spillover from Bitcoin to Ethereum. \(d_{1}\) is the asymmetric effect of the Bitcoin
  2. a,b,cIndicate the statistical significance at 1%, 5% and 10% respectively