VIRF (version 0.1.1)

VIRF: Volatility Impulse Response Function

Description

Provide information about the impact of independent shocks on volatility.

Usage

VIRF(data, shock)

Value

virfresult

List containing variance and covariance values

Arguments

data

log return multivariate time series

shock

shock time point from time series

References

Anthony, N.R. and Ahammad, S.M. 2016. Investigating the interdependency of agricultural production volatility spillovers between Bangladesh, India, and Pakistan. Review of Urban and Regional Development Studies, 28, 32 to 54 Jin, X., Lin, S.X. and Tamvakis, M. 2012. Volatility transmission and volatility impulse response functions in crude oil markets.Energy Economics, 34, 2125 to 2134

Examples

k=3 #number of series
p=6 # maximum lag order
ns=100 #number of simulations
B=matrix(0,nrow=k,ncol=p*k)
A1<- matrix(c(.4,-.02,.01,-.02,.3,.02,.01,.04,.3),ncol=3,nrow=3)
A2 <- matrix(c(.2,0,0,0,.3,0,0,0,.13),ncol=3,nrow=3)
B[,1:k]=A1
B[,(4*k+1):(5*k)]=A2
A <- BigVAR::VarptoVar1MC(B,p,k)
Y <-BigVAR::MultVarSim(k,A,p,.1*diag(k),ns)
lr<-VIRF(Y,40) # Y: multivariate time series data, shock time point: 40
print(lr)