Variability Summary: Statistics and Stylized Facts

Table of Mean absolute difference, period standard deviation and annualised standard deviation

table.Variability(R, scale = NA, geometric = TRUE, digits = 4)

Arguments

R

an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns

scale

number of periods in a year (daily scale = 252, monthly scale = 12, quarterly scale = 4)

geometric

utilize geometric chaining (TRUE) or simple/arithmetic chaining (FALSE) to aggregate returns, default TRUE

digits

number of digits to round results to

References

Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.65

See also

StdDev.annualized
MeanAbsoluteDeviation

Author

Matthieu Lestel

Examples

data(managers)
table.Variability(managers[,1:8])
#>                           HAM1   HAM2   HAM3   HAM4   HAM5   HAM6 EDHEC LS EQ
#> Mean Absolute deviation 0.0182 0.0268 0.0268 0.0410 0.0329 0.0187      0.0159
#> monthly Std Dev         0.0256 0.0367 0.0365 0.0532 0.0457 0.0238      0.0205
#> Annualized Std Dev      0.0888 0.1272 0.1265 0.1843 0.1584 0.0825      0.0708
#>                         SP500 TR
#> Mean Absolute deviation   0.0333
#> monthly Std Dev           0.0433
#> Annualized Std Dev        0.1500

 # don't test on CRAN, since it requires Suggested packages

require("Hmisc")
result = t(table.Variability(managers[,1:8]))

textplot(format.df(result, na.blank=TRUE, numeric.dollar=FALSE, cdec=c(3,3,1)),
rmar = 0.8, cmar = 2,  max.cex=.9, halign = "center", valign = "top",
row.valign="center", wrap.rownames=20, wrap.colnames=10,
col.rownames=c("red", rep("darkgray",5), rep("orange",2)), mar = c(0,0,3,0)+0.1)
title(main="Portfolio variability")