VaR {PerformanceAnalytics} | R Documentation

## calculate various Value at Risk (VaR) measures

### Description

Calculates Value-at-Risk(VaR) for univariate, component, and marginal cases using a variety of analytical methods.

### Usage

    VaR(R = NULL, p = 0.95, ..., method = c("modified", "gaussian",
      "historical", "kernel"), clean = c("none", "boudt", "geltner"),
      portfolio_method = c("single", "component", "marginal"),
      weights = NULL, mu = NULL, sigma = NULL, m3 = NULL, m4 = NULL,
      invert = TRUE)

### Arguments

`R` | an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
`p` | confidence level for calculation, default p=.95
`...` | any other passthru parameters
`method` | one of "modified","gaussian","historical", "kernel", see Details.
`clean` | method for data cleaning through `Return.clean`. Current options are "none", "boudt", or "geltner".
`portfolio_method` | one of "single","component","marginal" defining whether to do univariate, component, or marginal calc, see Details.
`weights` | portfolio weighting vector, default NULL, see Details
`mu` | If univariate, mu is the mean of the series. Otherwise mu is the vector of means of the return series, default NULL, see Details
`sigma` | If univariate, sigma is the variance of the series. Otherwise sigma is the covariance matrix of the return series, default NULL, see Details
`m3` | If univariate, m3 is the skewness of the series. Otherwise m3 is the coskewness matrix (or vector with unique coskewness values) of the returns series, default NULL, see Details
`m4` | If univariate, m4 is the excess kurtosis of the series. Otherwise m4 is the cokurtosis matrix (or vector with unique cokurtosis values) of the return series, default NULL, see Details
`invert` | TRUE/FALSE whether to invert the VaR measure. see Details.

### Note

The option to `invert` the VaR measure should appease both academics and practitioners. The mathematical definition of VaR as the negative value of a quantile will (usually) produce a positive number. Practitioners will argue that VaR denotes a loss, and should be internally consistent with the quantile (a negative number). For tables and charts, different preferences may apply for clarity and compactness. As such, we provide the option, and set the default to TRUE to keep the return consistent with prior versions of PerformanceAnalytics, but make no value judgment on which approach is preferable.

The prototype of the univariate Cornish Fisher VaR function was completed by Prof. Diethelm Wuertz. All corrections to the calculation and error handling are the fault of Brian Peterson.

### Author(s)

Brian G. Peterson and Kris Boudt

### References

Boudt, Kris, Peterson, Brian, and Christophe Croux. 2008. Estimation and decomposition of downside risk for portfolios with non-normal returns. 2008. The Journal of Risk, vol. 11, 79-103.

Cont, Rama, Deguest, Romain and Giacomo Scandolo. Robustness and sensitivity analysis of risk measurement procedures. Financial Engineering Report No. 2007-06, Columbia University Center for Financial Engineering.

Denton M. and Jayaraman, J.D. Incremental, Marginal, and Component VaR. Sunguard. 2004.

Epperlein, E., Smillie, A. Cracking VaR with kernels. RISK, 2006, vol. 19, 70-74.

Gourieroux, Christian, Laurent, Jean-Paul and Olivier Scaillet. Sensitivity analysis of value at risk. Journal of Empirical Finance, 2000, Vol. 7, 225-245.

Keel, Simon and Ardia, David. Generalized marginal risk. Aeris CAPITAL discussion paper.

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

Martellini, L. and Ziemann, V., 2010. Improved estimates of higher-order comoments and implications for portfolio selection. Review of Financial Studies, 23(4):1467-1502.

Return to RiskMetrics: Evolution of a Standard https://www.msci.com/documents/10199/dbb975aa-5dc2-4441-aa2d-ae34ab5f0945

Zangari, Peter. A VaR Methodology for Portfolios that include Options. 1996. RiskMetrics Monitor, First Quarter, 4-12.

Rockafellar, Terry and Uryasev, Stanislav. Optimization of Conditional VaR. The Journal of Risk, 2000, vol. 2, 21-41.

Dowd, Kevin. Measuring Market Risk, John Wiley and Sons, 2010.

Jorian, Phillippe. Value at Risk, the new benchmark for managing financial risk. 3rd Edition, McGraw Hill, 2006.

Hallerback, John. "Decomposing Portfolio Value-at-Risk: A General Analysis", 2003. The Journal of Risk vol 5/2.

Yamai and Yoshiba (2002). "Comparative Analyses of Expected Shortfall and Value-at-Risk: Their Estimation Error, Decomposition, and Optimization", Bank of Japan.

### See Also

`SharpeRatio.modified`
`chart.VaRSensitivity`
`Return.clean`

### Examples

        data(edhec)

        # first do normal VaR calc
        VaR(edhec, p=.95, method="historical")

        # now use Gaussian
        VaR(edhec, p=.95, method="gaussian")

        # now use modified Cornish Fisher calc to take non-normal distribution into account
        VaR(edhec, p=.95, method="modified")

        # now use p=.99
        VaR(edhec, p=.99)
        # or the equivalent alpha=.01
        VaR(edhec, p=.01)

        # now with outliers squished
        VaR(edhec, clean="boudt")

        # add Component VaR for the equal weighted portfolio
        VaR(edhec, clean="boudt", portfolio_method="component")

[Package PerformanceAnalytics version 1.5.3 Index]