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), 57-66.
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), 57-66.