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Table 6 Estimation for AR–GJR–GARCH (1,1) model

From: Tail dependence in emerging ASEAN-6 equity markets: empirical evidence from quantitative approaches

 

Indonesia

Malaysia

Philippines

Singapore

Thailand

Vietnam

c0

0.0037

0.0009

0.0026

0.0008

0.002

0.0004

(0.001)

(0)

(0.001)

(0.001)

(0.001)

(0.001)

c1

−0.0356

0.05

−0.0419

0.0387

−0.0393

0.2315

(0.044)

(0.038)

(0.039)

(0.033)

(0.037)

(0.041)

ω

0

0

0

0

0

0

(0)

(0)

(0)

(0)

(0)

(0)

α

0.1631

0.0711

0.0411

0.0253

0.0724

0.3483

(0.076)

(0.06)

(0.03)

(0.024)

(0.023)

(0.11)

β

0.7984

0.8798

0.8911

0.8696

0.9254

0.6704

(0.15)

(0.123)

(0.064)

(0.054)

(0.029)

(0.086)

γ

0.0328

0.057

0.0698

0.1705

0.0048

0.0676

(0.104)

(0.072)

(0.043)

(0.061)

(0.03)

(0.106)

Ï…

4.6782

5.3685

6.5995

6.122

5.8375

4.8907

(0.715)

(1.036)

(1.302)

(1.236)

(1.095)

(0.788)

λ

−0.1022

− 0.0942

− 0.1004

− 0.1038

− 0.2007

− 0.0331

(0.052)

(0.056)

(0.064)

(0.043)

(0.047)

(0.047)

  1. Our estimation of model AR–GJR–GARCH (1,1) model is to obtain the estimated coefficients for further following estimation. Therefore, we go through some main characteristics of this model that long run persistence of shocks exists. Moreover, the risk model is consistent and asymptotically normal