data("dy2012")
data("exampleSim")
exampleSim = exampleSim[1:600,]
exampleSim = zoo(exampleSim, order.by=tail(index(dy2012),600)) # date is required so we borrow it from dy2012

Replication of frequencyConnectedness

partition = c(pi+0.00001, pi/4, 0)
dca = ConnectednessApproach(exampleSim, 
                            nlag=2,
                            nfore=100,
                            model="VAR",
                            connectedness="Frequency",
                            Connectedness_config=list(FrequencyConnectedness=list(partition=partition, generalized=TRUE, scenario="ABS")))
## Estimating model
## Computing connectedness measures
## The VAR frequency connectedness approach is implemented according to:
##  Baruník, J., & Krehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296.
kable(dca$TABLE[,,1])
V1 V2 V3 FROM
V1 0.14 0.28 0.53 0.80
V2 0.09 2.30 4.81 4.90
V3 0.04 0.04 29.86 0.08
TO 0.13 0.31 5.34 5.79
Inc.Own 0.28 2.61 35.20 cTCI/TCI
Net -0.67 -4.59 5.26 2.89/1.93
NPDC 0.00 1.00 2.00 
kable(dca$TABLE[,,2])
V1 V2 V3 FROM
V1 8.42 10.07 80.57 90.63
V2 2.52 9.20 81.07 83.59
V3 0.27 0.22 69.57 0.49
TO 2.79 10.29 161.63 174.72
Inc.Own 11.21 19.49 231.21 cTCI/TCI
Net -87.84 -73.30 161.15 87.36/58.24
NPDC 0.00 1.00 2.00