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.