Calculates Value-at-Risk(VaR) for univariate, component, and marginal cases using a variety of analytical methods.
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)an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
confidence level for calculation, default p=.95
any other passthru parameters
one of "modified","gaussian","historical", "kernel", see Details.
method for data cleaning through Return.clean.
Current options are "none", "boudt", or "geltner".
one of "single","component","marginal" defining whether to do univariate, component, or marginal calc, see Details.
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
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
TRUE/FALSE whether to invert the VaR measure. see Details.
This function provides several estimation methods for
the Value at Risk (typically written as VaR) of a return series and the
Component VaR of a portfolio. Take care to capitalize VaR in the commonly
accepted manner, to avoid confusion with var (variance) and VAR (vector
auto-regression). VaR is an industry standard for measuring downside risk.
For a return series, VaR is defined as the high quantile (e.g. ~a 95
quantile) of the negative value of the returns. This quantile needs to be
estimated. With a sufficiently large data set, you may choose to utilize
the empirical quantile calculated using quantile. More
efficient estimates of VaR are obtained if a (correct) assumption is made on
the return distribution, such as the normal distribution. If your return
series is skewed and/or has excess kurtosis, Cornish-Fisher estimates of VaR
can be more appropriate. For the VaR of a portfolio, it is also of interest
to decompose total portfolio VaR into the risk contributions of each of the
portfolio components. For the above mentioned VaR estimators, such a
decomposition is possible in a financially meaningful way.
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.
# 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")
# }
Run the code above in your browser using DataLab