EWMAvol: Exponentially Weighted Moving-Average Volatility

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

Exponentially Weighted Moving-Average Volatility

Description

Use exponentially weighted moving-average method to compute the volatility matrix

Usage

EWMAvol(rtn, lambda = 0.96)

Arguments

rtn	A T-by-k data matrix of k-dimensional asset returns, assuming the mean is zero
lambda	Smoothing parameter. The default is 0.96. If lambda is negative, then the multivariate Gaussian likelihood is used to estimate the smoothing parameter.

Value

Sigma.t	The volatility matrix with each row representing a volatility matrix
return	The data
lambda	The smoothing parameter lambda used

Author(s)

Ruey S. Tsay

References

Tsay (2014, Chapter 7). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.

Examples

data("mts-examples",package="MTS")
rtn=log(ibmspko[,2:4]+1)
m1=EWMAvol(rtn)

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