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

### See Also

`VaR.CornishFisher`
`VaR.traditional`
`CVaR`
`CVaRplus`

### 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 0.9.5 Index]