Table 2: Volatility Spillover Table, Four Asset Classes
data("dy2012")
dca = ConnectednessApproach(dy2012,
nlag=4,
nfore=10,
model="VAR",
connectedness="Time",
Connectedness_config=list(TimeConnectedness=list(generalized=TRUE)))
## Estimating model
## Computing connectedness measures
## The (generalized) VAR connectedness approach is implemented according to:
## Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 5766.
SP500 
88.76 
7.29 
0.35 
3.61 
11.24 
R_10Y 
10.21 
81.45 
2.73 
5.61 
18.55 
DJUBSCOM 
0.47 
3.70 
93.69 
2.14 
6.31 
USDX 
5.69 
7.03 
1.55 
85.73 
14.27 
TO 
16.37 
18.01 
4.62 
11.36 
50.37 
Inc.Own 
105.13 
99.46 
98.31 
97.10 
cTCI/TCI 
NET 
5.13 
0.54 
1.69 
2.90 
16.79/12.59 
NPT 
3.00 
2.00 
0.00 
1.00 

dca = ConnectednessApproach(dy2012,
nlag=4,
nfore=10,
window.size=200,
model="VAR",
connectedness="Time",
Connectedness_config=list(TimeConnectedness=list(generalized=TRUE)))
## Estimating model
## Computing connectedness measures
## The (generalized) VAR connectedness approach is implemented according to:
## Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 5766.