Return.clean: clean returns in a time series to to provide more robust risk...

In guillermozbta/portafolio-master: Econometric tools for performance and risk analysis.

Description

A function that provides access to multiple methods for cleaning outliers from return data.

Usage

Return.clean(R, method = c("none", "boudt", "geltner"), alpha = 0.01, ...)

Arguments

R	an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
method	one of "none", "boudt", which applies the function clean.boudt or "geltner" which applies the function Return.Geltnerto R
alpha	the percentage of outliers you want to clean
...	additional parameters passed into the underlying cleaning function

Details

This is a wrapper for offering multiple data cleaning methods for data objects containing returns.

The primary value of data cleaning lies in creating a more robust and stable estimation of the distribution generating the large majority of the return data. The increased robustness and stability of the estimated moments using cleaned data should be used for portfolio construction. If an investor wishes to have a more conservative risk estimate, cleaning may not be indicated for risk monitoring.

In actual practice, it is probably best to back-test the results of both cleaned and uncleaned series to see what works best when forecasting risk with the particular combination of assets under consideration.

In this version, only one method is supported. See clean.boudt for more details.

Author(s)

Peter Carl

See Also

clean.boudt
Return.Geltner

Examples

data(managers)
head(Return.clean(managers[,1:4]),n=20)
chart.BarVaR(managers[,1,drop=FALSE], show.clean=TRUE, clean="boudt", lwd=2, methods="ModifiedVaR")

guillermozbta/portafolio-master documentation built on May 11, 2019, 7:20 p.m.