Systemic risk in Chinese financial industries: a vine copula grouped CoVaR approach

Figures & data

Figure 1. The structure of the vine grouped model.

Source: Authors.

Table 1. Names and industry classifications of Chinese financial institutions.

Banking	Ping An Bank, Bank of Ningbo, Shanghai Pudong Development Bank, Hua Xia Bank, China Minsheng Bank, China Merchants Bank, Bank of Nanjing, Industrial Bank, Bank of Beijing, Bank of Communications, Industrial and Commercial Bank of China, China Everbright Bank, China Construction Bank, Bank of China, China Citic Bank
Securities	Northeast Securities, Guoyuan Securities, Sealand Securities, GF Securities, Changjiang Securities, Shanxi Securities, Citic Securities, Sinolink Securities, Southwest Securities, Haitong Securities, China Merchants Securities, Pacific Securities, Industrial Securities, Everbright Securities, Founder Securities,
Insurance	Ping An Insurance, China Pacific Insurance Company, China Life

Source: Authors.

Table 2. Descriptive statistics and correlations matrix for returns of Chinese financial industries.

	Banking	Securities	Insurance
Panel A: Descriptive statistics of return series
Mean	0.0004	0.0001	0.0005
Std. Dev.	0.0160	0.0243	0.0203
Max.	0.0830	0.0954	0.0939
Min.	−0.1051	−0.1054	−0.1053
Skewness	−0.0388	−0.0801	0.1534
Kurtosis	6.8748	3.8105	3.7190
J-B	3282.71 (0.00)	1011.01 (0.00)	967.93 (0.00)
Q(10)	39.224 (0.0000)	28.303 ( 0.0016)	36.82 (0.0000)
ARCH	445.55 (0.0000)	601.13 (0.0000)	368.27 (0.0000)
Panel B: Correlation matrix of return series
Banking	1	0.6530	0.7653
Securities	0.6530	1	0.6832
Insurance	0.7653	0.6832	1

Source: Authors.

Table 4. Estimates for the copula models for the Chinese financial industries and the financial system.

Industry	Industry	copula	par1	par1
Panel A: The dependence among the three industries
Insurance	Banking	t	0.74	3.91
Insurance	Securities	t	0.63	3.28
Insurance, Banking	Insurance, Securities	t	0.29	8.12
Panel B: The dependence between the industries and the system
Banking	System	t	0.67	4.08
Securities	System	t	0.99	2.36
Insurance	System	t	0.98	3.55

Source: Authors.

Table 3. The distributions of the residuals and parameter estimates for the marginal distribution models.

Industry	$Z_t$	$\mu$	$c_1$	$\omega$	$\alpha$	$\beta$	$\lambda$	$\nu$	LL	ARCH
Banking	sstd	0.0005	−0.0197	0.0000	0.0916	0.9074	1.1454	3.3205	4931.	0.0784
		(0.062)	(0.376)	(0.171)	(0.0001)	(0.000)	(0.0000)	(0.000)	57
Securities	ged	−0.0006	−0.0521	0.0000	0.0458	0.9512	----	1.0641	4167.	0.6435
		(0.0083)	(0.0057)	(0.507)	(0.0001)	(0.000)		( 0.000)	18
Insurance	sged	0.0007	0.0098	0.0000	0.0458	0.9404	1.0982	1.1797	4375.	0.631
		(0.0113)	(0.3697)	(0.604)	(0.0026)	(0.000)	(0.0000)	(0.000)	01

Source: Authors.

Figure 2. Time series plots of VaR and CoVaR for the three financial industries.

Source: Authors.

Table 5. VaR, CoVaR and $\Delta$ CoVaR for the three financial industries with the vine copula grouped model.

Industry	Vine structure	VaR	CoVaR	$\Delta$ CoVaR
Banking		−0.0157
Banking|Securities	(1,2)		−0.0269	−0.0094
Banking|Insurance	(1,3)		−0.0320	−0.0120
Securities		−0.0200
Securities|Banking	(1,2)		−0.0331	−0.0098
Securities|Insurance	(2,3)		−0.0416	−0.0174
Insurance		−0.0240
Insurance|Banking	(1,3)		−0.0461	−0.0149
Insurance|Securities	(2,3)		−0.0447	−0.0138

Notes: 1- Banking, 2 - Securities, 3 - Insurance.

Source: Authors.

Table 6. The CoVaR and the $\Delta$ CoVaR for the three financial industries and the financial system based on the vine copula grouped model and the vine copula model.

System|Industry	Vine copula grouped model		Vine copula model
	CoVaR	$\Delta$ CoVaR	CoVaR	$\Delta$ CoVaR
System|Banking	−0.0501	−0.0234	−0.0479	−0.0237
System|Securities	−0.0368	−0.0156	−0.0317	−0.0154
System|Insurance	−0.0404	−0.0184	−0.0405	−0.0217

Source: Authors.

Table 7. Test statistics and p values for CoVaR unconditional coverage test with the vine copula grouped model and the vine copula model.

Model	CoVaR (System|Banking)		CoVaR (System|Securities)		CoVaR (System|Insurance)
	LR	p value	LR	p value	LR	p value
Vine Copula Grouped Model	0.4040	0.5251	0.1045	0.7464	0.0371	0.8472
Vine Copula Model	1.8850	0.1698	0.1045	0.7464	0.4717	0.4922

Source: Authors.