https://github.com/cran/tawny
Tip revision: 9d58f0405779506c9b745d3de0436c45efe105e4 authored by Brian Lee Yung Rowe on 05 February 2013, 00:00:00 UTC
version 2.1.0
version 2.1.0
Tip revision: 9d58f04
divergence.Rd
\name{divergence}
\alias{divergence}
\alias{divergence.stability}
\alias{divergence.kl}
\alias{divergence_lim}
\alias{stability_lim}
\alias{plotDivergenceLimit.kl}
\alias{KullbackLeibler}
\title{ Measure the divergence and stability between two correlation matrices }
\description{
The Kullback-Leibler distance function can be used to measure the divergence
between two correlation matrices. Although originally designed for probability
density functions, the literature shows how this can be extended to
correlation matrices. By using this function, one can determine objectively
the effectiveness of a particular filtering strategy for correlation matrices.
}
\usage{
divergence(...)
divergence.kl(...)
divergence_lim(...)
stability_lim(...)
divergence.stability(...)
plotDivergenceLimit.kl(...)
}
\arguments{
\item{\dots}{ Additional parameters to pass to plot or lines }
}
\details{
divergence(h, count, window = NULL, filter, measure = 'information')
divergence.kl(sigma.1, sigma.2)
sigma.1 - The sample correlation matrix
sigma.2 - The model correlation matrix (aka the filtered matrix)
divergence_lim(m, t = NULL)
stability_lim(m, t = NULL)
divergence.stability(h, count, window, filter)
h - A zoo object representing a portfolio with dimensions T x M
count - The number of bootstrap observations to create
window - The number of samples to include in each observation. Defaults to the anylength of h.
filter - The correlation filter to measure
m - The number of assets
t - The number of samples (dates) in an observation
plotDivergenceLimit.kl(m, t.range, ..., overlay = FALSE)
t.range - A range of date samples. This can be a simple interval so long as
it matches the number of samples per asset in the measured correlation matrix.
overlay - Overlay the divergence limit plot on an existing plot
measure - The type of divergence to calculate. Possible choices are information (default) or stability.
}
\value{
A summary of the results of the divergence calculation including the mean
divergence and an effective limit based on a random matrix.
}
\author{ Brian Lee Yung Rowe}
\examples{
data(sp500.subset)
h <- sp500.subset
plotDivergenceLimit.kl(100, 80:499, col='green', ylim=c(0,55))
divergence(h, 25, filter=function(x) denoise(x, RandomMatrixDenoiser()))
divergence(h, 25, filter=function(x) denoise(x, ShrinkageDenoiser()))
}
\keyword{ ts }