MCholV: Multivariate Cholesky Volatility Model

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

Multivariate Cholesky Volatility Model

Description

Use Cholesky decomposition to obtain multivariate volatility models

Usage

MCholV(rtn, size = 36, lambda = 0.96, p = 0)

Arguments

rtn	A T-by-k data matrix of a k-dimensional asset return series.
size	The initial sample size used to start recursive least squares estimation
lambda	The exponential smoothing parameter. Default is 0.96.
p	VAR order for the mean equation. Default is 0.

Details

Use recursive least squares to perform the time-varying Cholesky decomposition. The least squares estimates are then smoothed via the exponentially weighted moving-average method with decaying rate 0.96. University GARCH(1,1) model is used for the innovations of each linear regression.

Value

betat	Recursive least squares estimates of the linear transformations in Cholesky decomposition
bt	The transformation residual series
Vol	The volatility series of individual innovations
Sigma.t	Volatility matrices

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 7)

See Also

fGarch

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