JFE (version 2.5.6)

SharpeRatio: calculate a traditional or modified Sharpe Ratio of Return over StdDev or VaR or ES

Description

The Sharpe ratio is simply the return per unit of risk (represented by variability). In the classic case, the unit of risk is the standard deviation of the returns.

Usage

SharpeRatio(R, Rf = 0, alpha = 0.05, FUN="StdDev",annualize=FALSE)

Arguments

R

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

Rf

risk free rate, in same period as your returns

alpha

Tail probability for VaR or ES, default alpha=.05

FUN

one of "StdDev" or "VaR" or "ES" to use as the denominator

annualize

if TRUE, annualize the measure, default FALSE

Author

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

Details

$$\frac{\overline{(R_{a}-R_{f})}}{\sqrt{\sigma_{(R_{a}-R_{f})}}}$$

William Sharpe now recommends InformationRatio preferentially to the original Sharpe Ratio.

The higher the Sharpe ratio, the better the combined performance of "risk" and return.

As noted, the traditional Sharpe Ratio is a risk-adjusted measure of return that uses standard deviation to represent risk.

A number of papers now recommend using a "modified Sharpe" ratio using a Modified Cornish-Fisher VaR or CVaR/Expected Shortfall as the measure of Risk.

References

Sharpe, W.F. The Sharpe Ratio,Journal of Portfolio Management,Fall 1994, 49-58.

Laurent Favre and Jose-Antonio Galeano. Mean-Modified Value-at-Risk Optimization with Hedge Funds. Journal of Alternative Investment, Fall 2002, v 5.
See also package PerformanceAnalytics.

See Also

SharpeRatio.annualized
InformationRatio
TrackingError
ActivePremium
SortinoRatio

Examples

  data(assetReturns)
	R=assetReturns[, -29]

SharpeRatio(R)