Clean Returns in a Time Series

clean returns in a time series to to provide more robust  risk estimates risk  estimates 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 object of asset returns

method one of "none", "boudt", which applies the function clean.boudt clean.boudt or  or "geltner" which applies the function Return.Geltner Return.Geltnerto to 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 i n 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. 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")