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Table 6 DCC-GARCH model estimation results

From: Half-day trading and spillovers

 

\( {R}_{m,t}^c \)

\( {R}_{a,t}^c \)

\( {R}_{d,t}^c \)

Mean equation

γi, 11

0.017 (0.700)

− 0.165*** (− 7.071)

1.063 × 10− 3 (0.044)

γi, 12

0.280*** (10.620)

− 0.044** (− 2.443)

0.238*** (7.130)

γi, 21

− 0.026 (− 1.103)

9.005 × 10− 3 (0.371)

− 0.010 (− 0.602)

γi, 22

−0.042* (− 1.814)

−0.048** (− 2.098)

−0.044* (− 1.931)

Variance equation

α1

0.044*** (2.693)

0.077*** (5.202)

0.062*** (4.145)

α2

0.186*** (6.794)

0.182*** (6.872)

0.183*** (6.829)

β1

0.949*** (48.420)

0.923*** (67.550)

0.938*** (69.440)

β2

0.776*** (30.100)

0.778*** (31.160)

0.777*** (30.700)

θ1

4.783 × 10− 5 (0.005)

0.042* (1.928)

2.829 × 10− 3 (0.242)

θ2

0.842 (0.304)

0.499** (2.478)

0.848*** (16.810)

  1. Notes. This table shows the estimation results of the DCC-GARCH model. The maximum likelihood estimation is applied, and the estimation method is the two-step approach. The results are converged within 100 iterations. The sample period spans from January 1, 2010, to March 31, 2020. \( {R}_{m,t}^c \), \( {R}_{a,t}^c \), and \( {R}_{d,t}^c \) denote different \( {R}_{i,t}^c \) in the mean equation of the DCC-GARCH model. The estimations of the constants in the mean and variance equations are omitted. The t-statistics of the elements are shown in parenthesis. One, two and three asterisks (*), respectively, indicate that the t-values are significant at the 0.1, 0.05, and 0.01 level. Some less relevant parameter estimates are omitted