| Type: | Package |
| Title: | Computation of Volatility Impulse Response Function of Multivariate Time Series |
| Version: | 0.1.1 |
| Author: | Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut], Mr. Ankit Tanwar [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| Description: | Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi:10.1016/j.eneco.2012.03.003>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| Imports: | stats, rmgarch, mgarchBEKK, gnm, expm, BigVAR, ks, matrixcalc, matlib |
| NeedsCompilation: | no |
| Packaged: | 2025-08-29 11:53:12 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2025-08-29 13:10:08 UTC |
Volatility Impulse Response Function
Description
Provide information about the impact of independent shocks on volatility.
Usage
VIRF(data, shock)
Arguments
data |
log return multivariate time series |
shock |
shock time point from time series |
Value
virfresult |
List containing variance and covariance values |
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)