resample.target.risk: Use resampling to find an efficient portfolio
In rexmacey/TaxAwareAA: Tax Aware Asset Allocation

This function finds an efficient frontier given a target standard deviation and a set of capital market assumptions. Resampling reduces the impact of estimation error in the mean-variance process. The process starts with a set of assumptions. These are used to generate a set of random returns (a sample). The return and covariance of this sample is computed and an efficient point is created. This sampling is repeated. The average allocations of the samples is the result. This function allows the user to set a minimum threshold for an asset class. If an asset class's weight fails to meet the threshold, the class's maximum weight is set to zero. The resampling is repeated. The threshold is ignored for asset classes with minimum weights less than the threshold (for example, if one sets a 1 minimum weight to cash and a threshold of 2.5 be allowed.)

1 2	resample.target.risk(target.risk, cma.ta, n.samples = 100, thresh = 0, tol = 4e-05)

`target.risk`	The target risk for which to find an efficient portfolio.
`cma.ta`	Tax-aware capital market assumptions
`n.samples`	The number of samples for the resampling.
`thresh`	The minimum weight for an asset class (if the assumptions do not specify another minimum)
`tol`	Tolerance to pass to Optimize Target Return

A list with two items. The first item w is the weights of the efficient portfolio. The second item is mat which is the matrix of resampled solutions. Each row is a sample. The first two columns are return and risk respectively. The remainding columns are the weights. The w item is the column means of the weights.

rexmacey/TaxAwareAA documentation built on Dec. 3, 2019, 7:54 a.m.