R: calculate BVaR or loss Beyond traditional mean-VaR

VaR.Beyond {PerformanceAnalytics}

R Documentation

calculate BVaR or loss Beyond traditional mean-VaR

Description

Beyond VaR purports to estimate average loss beyond VaR. Please note that your milage will vary; expect that values obtained from the normal distribution differs radically from the real situation.

BeyondVaR is described in theoretical detail in the paper: Gaussel, N., Legras, J., Longin, F., and Rabemananjara, R. "Beyond the VaR Horizon" 2001, Quants Review No. 37

Usage

VaR.Beyond(R, p=.95, modified=FALSE, add=FALSE, periods = 1)

Arguments

`R`	a vector, matrix, data frame, timeSeries or zoo object of asset returns
`p`	confidence level for calculation, default p=.99
`modified`	TRUE/FALSE default FALSE, use Cornish Fisher Expansion to take higher moments into account
`add`	TRUE/FALSE default FALSE, add VaR to BeyondVaR value for total predicted loss, like `CVaRplus`
`periods`	number of periods to calculate predicted loss beyond the VaR for, default 1

Details

We believe that the extension of Cornish-Fisher VaR as an option in the Beyond VaR calculation is unique to this library.

Value

estimated Value of single period loss Beyond traditional mean-VaR

Author(s)

Peter Carl
Brian G. Peterson

References

Gaussel, N., Legras, J., Longin, F., and Rabemananjara, R. "Beyond the VaR Horizon" 2001, Quants Review No. 37

Examples

    data(edhec)

    # first do normal Beyond VaR calc
    VaR.Beyond(edhec)

    # now use modified Cornish-Fisher VaR calc to take non-normal distribution into account
    VaR.Beyond(edhec,modified=TRUE)

[Package PerformanceAnalytics version 0.9.7 Index]