Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach

Table 2: Averaged Joint Connectedness Table

data("bgu2021")
ejc = ConnectednessApproach(bgu2021, 
                            nlag=1, 
                            nfore=20,
                            model="TVP-VAR",
                            connectedness="Extended Joint",
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="MinnesotaPrior", gamma=0.1)))

## Estimating model

## Computing connectedness measures

## The VAR and TVP-VAR extended joint connectedness approach is implemented according to:
##  Balcilar, M., Gabauer, D., & Umar, Z. (2021). Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach. Resources Policy, 73, 102219.

kable(ejc$TABLE)

	CrudeOil	Cattle	Cocoa	Coffee	Corn	Grains	LeanHogs	Livestock	Soybeans	SoybeanOil	Sugar	Wheat	FROM
CrudeOil	72.84	1.36	1.80	2.03	2.32	2.67	0.95	1.45	3.41	7.01	2.31	1.85	27.16
Cattle	2.79	1.05	1.42	2.05	2.18	2.47	8.54	70.55	2.42	2.73	1.88	1.92	98.95
Cocoa	2.34	1.04	83.62	2.67	1.13	1.43	0.64	0.97	1.57	1.73	1.73	1.14	16.38
Coffee	2.27	1.12	2.13	76.63	2.06	2.65	0.70	1.08	2.69	2.57	4.09	2.00	23.37
Corn	2.76	1.10	1.02	2.12	2.14	38.26	0.85	1.13	18.58	9.66	2.16	20.22	97.86
Grains	2.55	0.99	1.01	2.22	29.74	0.67	0.74	1.04	19.86	10.14	2.03	29.01	99.33
LeanHogs	2.41	10.55	1.35	1.75	1.99	2.33	1.44	69.96	2.54	2.10	1.54	2.04	98.56
Livestock	2.13	46.72	1.02	1.49	1.62	1.99	37.21	0.70	2.06	2.10	1.37	1.60	99.30
Soybeans	3.75	1.14	1.30	2.91	20.18	27.97	1.01	1.34	4.16	22.90	2.38	10.96	95.84
SoybeanOil	5.91	1.14	1.38	2.34	8.07	10.79	0.74	1.19	18.40	42.63	1.99	5.42	57.37
Sugar	2.40	1.06	1.45	4.24	2.16	2.44	0.60	0.93	2.29	2.31	78.33	1.78	21.67
Wheat	2.58	1.13	1.16	2.35	23.39	43.67	0.92	1.13	11.68	7.27	2.09	2.62	97.38
TO	31.87	67.35	15.04	26.18	94.84	136.68	52.91	150.78	85.49	70.52	23.56	77.94	833.17
Inc.Own	104.72	68.40	98.66	102.80	96.98	137.35	54.36	151.47	89.65	113.15	101.90	80.56	cTCI/TCI
NET	4.72	-31.60	-1.34	2.80	-3.02	37.35	-45.64	51.47	-10.35	13.15	1.90	-19.44	75.74/69.43
NPT	9.00	1.00	4.00	8.00	6.00	11.00	0.00	2.00	5.00	10.00	7.00	3.00

dca = ConnectednessApproach(bgu2021, 
                            nlag=1, 
                            nfore=20,
                            model="TVP-VAR",
                            connectedness="Time",
                            VAR_config=list(TVPVAR=list(kappa1=0.99, kappa2=0.99, prior="MinnesotaPrior", gamma=0.1)))

## 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.

Figure 2: Dynamic Total Connectednes

PlotTCI(ejc, ca=dca, ylim=c(20,90))

Figure 3: Dynamci Net Total Directional Connectedness

PlotNET(ejc, ca=dca, ylim=c(-80,80))

Figure 4: Dynamci Net Pairwise Directional Connectedness

PlotNPDC(ejc, ca=dca, ylim=c(-10,10), selection=1)