EWMAvol {MTS}

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)