G.spillover: Generalized spillover index

In Spillover: Spillover/Connectedness Index Based on VAR Modelling

Generalized spillover index

Description

Computes the generalized spillover index proposed in Diebold and Yilmaz (2012) which is based on the General Forecast Variance Decompositon introduced by Pesaran and Shin (1998).

Usage

G.spillover(x, n.ahead = 10, standardized = TRUE)

Arguments

x	Object of class ‘varest’ generated by VAR() from vars package.
n.ahead	Integer specifying the steps ahead.
standardized	A logical value indicating whether the values should be divided by the number of columns to get a percentage.

Details

This function computes the Generalized Directional Spillover Table which has as its ij^{th} entry the estimated contribution to the forecast error variance of variable i coming from innovations to variable j. The off-diagonal column sums are the Contributions to Others, while the row sums represent Contributions from Others, when these are totaled across countries then we have the numerator of the Spillover Index. Similarly, the columns sums or rows sums (including diagonal), when totaled across countries, give the denominator of the Spillover Index, which is 100%.

G.spillover is based upon the General Forecast Error Variance Decompositon introduced by Pesaran and Shin (1998) and its explicit formulation can be found in Diebold and Yilmaz (2010).

Value

A data.frame consisting of the spillover index.

Author(s)

Jilber Urbina

References

Diebold, F. X. & Yilmaz, K.(2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting.

Pesaran, M. H. and Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1):17-29.

See Also

O.spillover

Examples

# Replicating Diebold and Yilmaz (2012)
data(dy2012)
VAR_4 <- VAR(dy2012[,-1], p=4) 
G.spillover(VAR_4, standardized = FALSE)

Spillover documentation built on June 22, 2024, 12:25 p.m.