Distributions Summary: Statistics and Stylized Facts
Source:R/table.Distributions.R
table.Distributions.RdTable of standard deviation, Skewness, Sample standard deviation, Kurtosis, Excess kurtosis, Sample Skweness and Sample excess kurtosis
References
Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.87
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
# \donttest{
# 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")
# }