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Financial Innovation

Table 5 Estimation results of DCC- and CDCC-FIAPARCH models

From: Dynamic connectedness and hedging opportunities of the commodity and stock markets in China: evidence from the TVP-VAR and cDCC-FIAPARCH

 

CSI300

coppEr

Gold

PTA

Soybean

Panel a: Estimates of the AR (1)-FIAPARCH process

Cst(M)

0.0168

 − 0.0038

0.0188

 − 0.0135

 − 0.0033

 

0.0203

0.0163***

0.0128

0.0225

0.0182

AR(1)

0.0268

 − 0.0449***

 − 0.0212

0.0059

 − 0.0206

 

0.0176

0.0169

0.0181

0.0192

0.0198

Cst(V)

19.1523

3.1218***

3.5413**

4.3194

2.1998***

 

13.0280

1.0456

1.3768

2.0432

0.6174

d-Figarch

0.6609 ***

0.3812***

0.4720***

0.3331 ***

0.2435***

 

0.1269

0.0475

0.0481

0.0721

0.1056

ARCH

0.1893 ***

0.1624 *

0.2855 ***

0.1579

0.4001***

 

0.0659 ***

0.0945

0.0682

0.4109

0.1317

GARCH

0.7716

0.4394 ***

0.6506***

0.4143

0.5888 ***

 

0.0905

0.1193

0.0746

0.4585

0.1915

APARCH (Gamma)

0.0883

0.1189**

 − 0.0216

 − 0.0324

0.0796

 

0.0800

0.0533

0.0648

0.0667

0.1890

APARCH (Delta)

1.7475 ***

1.7930 ***

1.7069 ***

1.7162***

1.5128 ***

 

0.1967

0.1254

0.1589

0.3248

0.2164

Panel b. Estimates of the DCC process

cDCC-FIAPARCH

DCC − FIAPARCH

    

α

0.0135 ***

 

0.0117 ***

  

std.Error

0.0032

 

0.0021

  

β

0.9794***

 

0.9832***

  

std.Error

0.0070

 

0.0038

  

df

6.0606***

 

6.0282***

  

std.Error

0.2070

 

0.2009

  

Panel c. Diagnostics

AIC

15.0562

 

15.0642

  

SC

15.1440

 

15.1520

  

log likelihood

 − 28,260.1

 

 − 28,275.262

  

Hosking (20)

582.373

 

582.112

  

Hosking2 (20)

534.001

 

523.562

  

McLeod-Li (20)

582.132

 

581.871

  

McLeod-Li2 (20)

534.154

 

523.758

  
  1. ***, **, *Represent significance at the 1% level, 5% level, and 10%, respectively
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