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Table 5 Goodness-of-fit tests for ARMA–GARCH models

From: ARMA–GARCH model with fractional generalized hyperbolic innovations

 

IBM

Johnson & Johnson

Oracle

Apple

Amazon

CVS

ARMA–GARCH model with fractional GH innovations

KS

0.0758

0.0670

0.1042

0.1207

0.0795

0.0630

(p-value)

(0.8432)

(0.6967)

(0.6776)

(0.3669)

(0.7269)

(0.9627)

LL

16689.24

18226.48

17715.06

16857.67

16235.18

16986.63

AIC

− 33372.48

− 36446.95

− 35424.13

− 33709.35

− 32464.37

− 32167.25

ARMA–GARCH model with non-fractional GH innovations

KS

0.1990

0.1789

0.2299

0.1560

0.1967

0.1410

(p-value)

(0.0007)

(0.0000)

(0.0002)

(0.0646)

(0.0017)

(0.0899)

LL

16174.77

17708.04

17198.05

16336.22

15715.95

15563.07

AIC

− 32343.54

− 35410.08

− 34390.09

− 32666.44

− 31425.91

− 31120.15

ARMA–GARCH model with normal innovations

KS

0.1542

0.4748

0.3089

0.1763

0.1391

0.2047

(p-value)

(0.0168)

(0.0000)

(0.0000)

(0.0383)

(0.0815)

(0.0098)

LL

15643.38

18121.03

17071.17

15968.61

16156.68

15146.00

AIC

− 12.1975

− 13.2808

− 12.5111

− 11.7028

− 11.8407

− 11.8328