var.shrink.eqcor: Ledoit-Wolf Shrinkage Variance Estimate

Description Usage Arguments Value Details Warning Research Issues Revision Author(s) References See Also Examples


Returns a variance matrix that shrinks towards the equal correlation matrix – a Ledoit-Wolf estimate.


var.shrink.eqcor(x, weights = seq(0.5, 1.5, length = nt), shrink = NULL, 
	center = TRUE, vol.shrink = 0, sd.min = 20, = 0.9, 
	tol = 1e-4, compatible = FALSE, verbose=2)



required. A numeric matrix. The rows are observations and the columns are the variables. In finance, this will be a matrix of returns where the rows are times and the columns are assets. For the default value of weights the most recent observation should be the last row. The number of columns may exceed the number of rows, and missing values are accepted. A column may even have all missing values.


a numeric vector giving the observation weights, or NULL.

Equal weights can be specified with NULL or with a single positive number.

Otherwise, this must be a vector of non-negative numbers that is as long as the number of observations (rows of x).


either NULL meaning the shrinkage will be estimated, or a single number meant to be in the range of zero to one.


either a single logical value, or a numeric vector as long as the number of columns of x. This gives the expected value of each column. A value of TRUE means the (weighted) mean of the columns of x is used. A value of FALSE means zero is used.


a number between zero and one (inclusive) that says how much to shrink the standard deviations towards the mean standard deviation.


a single integer giving the minimum number of observations needed in a column of x for the estimated standard deviation to be used.

a single number in the range of zero to one (inclusive) that says what quantile of the (used) standard deviations to use as the estimate of standard deviation for columns without enough (or any) data.


a single number indicating how large the smallest eigenvalues of the answer should be. All eigenvalues are made to be no smaller than tol times the maximum eigenvalue.


a logical value. If TRUE, then the variance is scaled by the number of observations rather than one less than the number of observations, and there will be a warning if weights are not all the same. This makes the results compatible with Ledoit-Wolf computations and with the cov.shrink function in package tawny. This also makes the variances smaller, which is not necessarily a good thing.


a number indicating the level of warning messages desired. This currently controls only one warning:

If at least 1, then a warning will be issued if all the values in x are non-negative. In finance this is an indication that prices rather than returns are input (an easy mistake to make).


an estimate of the variance matrix of x. The sample variance is shrunk towards equal correlation.

This has two additional attributes:


the estimated or input amount of shrinkage towards the equal correlation matrix from the sample variance.


the date and time at which the computation was done.


Time weights are quite helpful for estimating variances from returns. The default weighting seems to perform reasonably well over a range of situations. However, time weighting was not studied for this estimator.


The default value for weights assumes that the last row is the most recent observation and the first observation is the most ancient observation.

Research Issues

The method of handling missing values used in the function has not been studied (at all).

The method of boosting the result away from singularity is completely unstudied. For optimization it is wise to move away from singularity, just how to do that best seems like a research question. The method used boosts the smallest eigenvalues, it might be better to increase the diagonal.


This help was last revised 2014 March 09.


Burns Statistics


Olivier Ledoit and Michael Wolf (2004) "Honey, I shrunk the sample covariance matrix". The Journal of Portfolio Management, volume 30, number 4.

See Also

factor.model.stat, cov.wt, slideWeight.


## Not run: 
var1 <- var.shrink.eqcor(return.matrix)

var.unweighted <- var.shrink.eqcor(return.matrix, weights=1)

## End(Not run)

BurStFin documentation built on May 29, 2017, 9:54 p.m.

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