Dynamic correlation network analysis of financial asset returns with network clustering

Applied Network Science

Table 4 DCC parameter estimation results by sub-portfolio

		m, n	a1	b1	b2	[b1+b2]	η
Cyclical	P1	1, 2	0.0080	0.5537	0.3523	[0.9060]	30.5
	P2	1, 2	0.0074	0.5567	0.3792	[0.9358]	19.7
	P3	1, 2	0.0093	0.3275	0.3897	[0.7172]	29.9
	P4	1, 2	0.0079	0.5476	0.3219	[0.8695]	31.6
	P5	1, 2	0.0064	0.5787	0.3240	[0.9027]	27.2
	P6	1, 2	0.0086	0.5820	0.3219	[0.9038]	22.3
	P7	1, 2	0.0079	0.5432	0.3713	[0.9145]	25.6
	P8	1, 2	0.0069	0.5542	0.3925	[0.9467]	20.9
Defensive	P9	1, 2	0.0078	0.4651	0.3815	[0.8467]	27.9
	P10	1, 2	0.0103	0.2498	0.3980	[0.6478]	30.9
	P11	1, 2	0.0070	0.4963	0.3890	[0.8853]	30.9
	P12	1, 1	0.0048	0.9400	-	[0.9400]	22.9
	P13	1, 2	0.0094	0.2627	0.5084	[0.7711]	27.1
	P14	1, 1	0.0042	0.8945	-	[0.8945]	38.5

Note: The DCC order (m, n) and parameters a ₁,b ₁, and b ₂ are defined in (6). η is the shape parameter of the Student t-copula in (10). The R (http://cran.r-project.org/) package “rmgarch” Ghalanos (2014) is used for the parameter estimation