VolatilitySkewness: Volatility and variability of the return distribution

In JFE: Tools and GUI for Analyzing Time Series Data of Just Finance and Econometrics

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

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)

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

References

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

Examples

  data(assetReturns)
	R=assetReturns[, -29]
  VolatilitySkewness(R, MAR=0, stat="volatility")

JFE documentation built on Aug. 28, 2023, 9:06 a.m.