A suite of functions for finance, including the estimation of variance matrices via a statistical factor model or Ledoit-Wolf shrinkage.
Changes in version 1.02 (2014-03-09)
o The 'slideWeight' function is added. This creates a vector suitable to use as time weights -- for example, the 'weights' argument to 'var.shrink.eqcor' or 'factor.model.stat'.
o if in 'factor.model.stat' the 'constant.returns.okay' argument is 'TRUE', then any columns with constant values now get a variance of zero rather than the default variance for an all missing column. The previous behavior could arguably be considered a bug.
o the default value of 'tol' in 'var.shrink.eqcor' is now 1e-4 instead of 1e-3. This is still a guess, but it seems to be a better guess. o equal time weights can be specified with 'weight=NULL' in 'factor.model.stat' and 'var.shrink.eqcor'. o 'cumulative.variance.fraction' is a new component of the result of 'factor.model.stat' when its output is the factor model. o 'constant.names' is a new component of the result of 'factor.model.stat' when its output is the factor model.
o The 'x' in 'factor.model.stat' is immediately coerced with 'as.matrix' to avoid subsetting problems with some data types, 'timeSeries' for instance. o A second attempt is made with 'svd' inside 'factor.model.stat' if the first attempt fails to converge. It also does a sanity check on the result of 'svd'.
Changes in version 1.01 (2012-02-12):
o The 'threeDarr' function is added. This creates three-dimensional arrays out of matrices.
o 'var.shrink.eqcor' and 'factor.model.stat' have a new argument 'verbose' that controls whether some warnings are given. Both functions can warn if there are no negative values in the input 'x' -- an indication in finance that prices rather than returns are given. Warnings in 'factor.model.stat' about constant columns in 'x' and negative specific variances are also controlled. o 'var.add.benchmark' has a new argument 'sum.to.one' that allows a "benchmark" to have weights that sum to something other than one. An example is to give portfolio weights minus benchmark weights.