MeanAbsoluteDeviation: Mean absolute deviation of the return distribution

In tsungwu/JFE: Tools and GUI for Analyzing Time Series Data of Just Finance and Econometrics

Mean absolute deviation of the return distribution

Description

To calculate Mean absolute deviation we take the sum of the absolute value of the difference between the returns and the mean of the returns and we divide it by the number of returns.

Usage

MeanAbsoluteDeviation(R, ...)

Arguments

R	an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
...	any other passthru parameters

Details

MeanAbsoluteDeviation = sum(|r-mean(r)|)/n

where n is the number of observations of the entire series, r_i is the return in month i and \overline{r} is the mean return

Author(s)

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

References

Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.62.
See also package PerformanceAnalytics.

Examples

  data(assetReturns)
	assetReturns=assetReturns["2011::2018"] #short sample for fast example
	R=assetReturns[, -29]

MeanAbsoluteDeviation(R)

tsungwu/JFE documentation built on May 10, 2022, 1:22 p.m.