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Table 6 Small sample: goodness-of-fit test 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

0.0800

0.0556

0.0769

0.1029

0.0509

0.1179

(p-value)

(0.8186)

(0.8631)

(0.9524)

(0.4670)

(0.9421)

(0.1327)

LL

8984.54

9937.79

9736.98

9151.06

8829.31

9005.51

AIC

− 17963.08

− 19869.58

− 19467.95

− 18296.11

− 17838.63

− 18005.02

ARMA–GARCH model with non-fractional GH innovations

KS

0.1960

0.2102

0.2931

0.1676

0.2679

0.0968

(p-value)

(0.0009)

(0.0000)

(0.0000)

(0.0131)

(0.0000)

(0.1959)

LL

8613.54

9568.62

9366.99

8784.29

8556.95

8637.40

AIC

− 17221.08

− 19131.23

− 18727.97

− 17562.57

− 17107.90

− 17268.80

ARMA–GARCH model with normal innovations

KS

0.5811

0.3075

0.1379

0.1341

0.1807

0.2057

(p-value)

(0.0000)

(0.0000)

(0.0730)

(0.0801)

(0.0001)

(0.0001)

LL

8269.34

9023.02

8929.74

8011.06

7804.60

7769.13

AIC

− 11.9671

− 13.2404

− 12.4082

− 12.0094

− 11.8806

− 12.1003