comVol: Common Volatility

In MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Common Volatility

Description

Compute the principal volatility components based on the residuals of a VAR(p) model.

Usage

comVol(rtn, m = 10, p = 1, stand = FALSE)

Arguments

rtn	A T-by-k data matrix of k-dimensional asset returns
m	The number of lags used to compute generalized cross-Kurtosis matrix
p	VAR order for the mean equation
stand	A logical switch to standardize the returns

Details

Perform a VAR(p) fit, if any. Then, use the residual series to perform principal volatility component analysis. The ARCH test statistics are also computed for the sample principal components

Value

residuals	The residuals of a VAR(p) fit
values	Eigenvalues of the principal volatility component analysis
vectors	Eigenvectors of the principal volatility component analysis
M	The transformation matrix

Author(s)

Ruey S. Tsay and Y.B. Hu

References

Tsay (2014, Chapter 7)

Examples

data("mts-examples",package="MTS")
zt=diffM(log(qgdp[,3:5]))
m1=comVol(zt,p=2)
names(m1)

MTS documentation built on April 11, 2022, 5:07 p.m.