Specific risk Summary: Statistics and Stylized Facts

Source: R/table.SpecificRisk.R

Table of specific risk, systematic risk and total risk

table.SpecificRisk(Ra, Rb, Rf = 0, digits = 4)

Arguments

Ra

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

Rb

return vector of the benchmark asset

Rf

risk free rate, in same period as your returns

digits

number of digits to round results to

References

Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.76

Author

Matthieu Lestel

Examples


data(managers)
table.SpecificRisk(managers[,1:8], managers[,8])
#>                   HAM1   HAM2   HAM3   HAM4   HAM5   HAM6 EDHEC LS EQ SP500 TR
#> Specific Risk   0.0664     NA 0.0946 0.1521     NA     NA          NA     0.00
#> Systematic Risk 0.0586 0.0515 0.0836 0.1032 0.0477 0.0486      0.0503     0.15
#> Total Risk      0.0886     NA 0.1262 0.1838     NA     NA          NA     0.15

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

require("Hmisc")
result = t(table.SpecificRisk(managers[,1:8], managers[,8], Rf=.04/12))

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 specific, systematic and total risk")