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Table 4 DCC parameter estimation results by sub-portfolio

From: Dynamic correlation network analysis of financial asset returns with network clustering

   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
  1. 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