Exponentially Weighted Moving-Average Volatility
Use exponentially weighted moving-average method to compute the volatility matrix
EWMAvol(rtn, lambda = 0.96)
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. |
Sigma.t |
The volatility matrix with each row representing a volatility matrix |
return |
The data |
lambda |
The smoothing parameter lambda used |
Ruey S. Tsay
Tsay (2014, Chapter 7). Multivariate Time Series Analysis with R and Financial Applications. John Wiley. Hoboken, NJ.
data("mts-examples",package="MTS")
rtn=log(ibmspko[,2:4]+1)
m1=EWMAvol(rtn)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.