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)
Return.clean
. Current options are "none",
"boudt", or "geltner".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.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, Lionel, and Volker Ziemann. Improved Forecasts of Higher-Order Comoments and Implications for Portfolio Selection. 2007. EDHEC Risk and Asset Management Research Centre working paper.
Return to RiskMetrics: Evolution of a Standard
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.
SharpeRatio.modified
chart.VaRSensitivity
Return.clean
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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