VaR: 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

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

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

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

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

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.

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.

Examples

Run this code

# NOT RUN {
    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")

# }

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Description

Usage

Arguments

References

See Also

Examples