Distributions Summary: Statistics and Stylized Facts

Table of standard deviation, Skewness, Sample standard deviation, Kurtosis, Excess kurtosis, Sample Skweness and Sample excess kurtosis

table.Distributions(R, scale = NA, 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)

digits

number of digits to round results to

References

Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.87

See also

StdDev.annualized
skewness
kurtosis

Author

Matthieu Lestel

Examples

data(managers)
table.Distributions(managers[,1:8])
#>                           HAM1   HAM2   HAM3    HAM4   HAM5    HAM6 EDHEC LS EQ
#> monthly  Std Dev        0.0256 0.0367 0.0365  0.0532 0.0457  0.0238      0.0205
#> Skewness               -0.6588 1.4580 0.7908 -0.4311 0.0738 -0.2800      0.0177
#> Kurtosis                5.3616 5.3794 5.6829  3.8632 5.3143  2.6511      3.9105
#> Excess kurtosis         2.3616 2.3794 2.6829  0.8632 2.3143 -0.3489      0.9105
#> Sample skewness        -0.6741 1.4937 0.8091 -0.4410 0.0768 -0.2936      0.0182
#> Sample excess kurtosis  2.5004 2.5270 2.8343  0.9437 2.5541 -0.2778      1.0013
#>                        SP500 TR
#> monthly  Std Dev         0.0433
#> Skewness                -0.5531
#> Kurtosis                 3.5598
#> Excess kurtosis          0.5598
#> Sample skewness         -0.5659
#> Sample excess kurtosis   0.6285

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

require("Hmisc")
result = t(table.Distributions(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 Distributions statistics")