Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions

data("acg2020")
dca = ConnectednessApproach(acg2020, 
                            nlag=1, 
                            nfore=12,
                            window.size=200,
                            model="TVP-VAR",
                            connectedness="Time",
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.96, prior="BayesPrior")))

## Estimating model

## Computing connectedness measures

## The TVP-VAR connectedness approach is implemented according to:
##  Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.

DCA = list()
WINDOW.SIZE = c(50, 100, 200)
for (i in 1:length(WINDOW.SIZE)) {
  DCA[[i]] = suppressMessages(ConnectednessApproach(acg2020, 
                              nlag=1, 
                              nfore=12,
                              window.size=WINDOW.SIZE[i]))
}

Figure 7: Dynamic Total Connectedness

PlotTCI(dca, ca=DCA, ylim=c(20,80))

Figure 8: Net Total and Net Pairwise Directional Connectedness Measures

PlotNET(dca, ca=DCA, ylim=c(-20,20))

PlotNPDC(dca, ca=DCA, ylim=c(-20,20))