VolatilitySkewness {PerformanceAnalytics}

Volatility and variability of the return distribution

Description

Volatility skewness is a similar measure to omega but using the second partial moment. It's the ratio of the upside variance compared to the downside variance. Variability skewness is the ratio of the upside risk compared to the downside risk.

Usage

VolatilitySkewness(R, MAR = 0, stat = c("volatility", "variability"), ...)

Arguments

R

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

MAR

Minimum Acceptable Return, in the same periodicity as your returns

stat

one of "volatility", "variability" indicating whether to return the volatility skewness or the variability skweness

...

any other passthru parameters

Details

VolatilitySkewness(R , MAR) = \frac{\sigma_U^2}{\sigma_D^2}

VariabilitySkewness(R , MAR) = \frac{\sigma_U}{\sigma_D}

where \sigma_U is the Upside risk and \sigma_D is the Downside Risk

Author(s)

Matthieu Lestel

References

Carl Bacon, Practical portfolio performance measurement and attribution, second edition 2008 p.97-98

Examples

data(portfolio_bacon)
MAR = 0.005
print(VolatilitySkewness(portfolio_bacon[,1], MAR, stat="volatility")) #expected 1.32
print(VolatilitySkewness(portfolio_bacon[,1], MAR, stat="variability")) #expected 1.15

MAR = 0
data(managers)
# print(VolatilitySkewness(managers['1996'], MAR, stat="volatility"))
print(VolatilitySkewness(managers['1996',1], MAR, stat="volatility"))