Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: VaR
See also: VaRest Varopt VaRtimeplot

Quantlet: VaRestMC
Description: Partial Monte-Carlo method to calculate the Value at Risk (VaR) based on Delta-Gamma Approximation.

Reference(s):

Usage: VaRMC = VaRestMC(VaRdelta,VaRgamma,VaRcovmatrix, smethod, opt)
Input:
VaRdelta m x 1 vector of first derivatives, aggregated delta matrix
VaRgamma m x m Hessian matrix, aggregated gamma matrix
VaRcovmatrix m x m covariance matrix, variance-covariance matrix for the underlying portfolio
smethod Monte-Carlo sampling method for VaR. Default set at "IS". "PS": Plain vanilla sampling method "MS": Moment matching sampling method "SS": Stratified Latin Hypercube sampling method "IS": Importance sampling method
opt optional, a list with optional input. The function "VaRopt" can be used to set up this parameter. The order of the list elements is not important.
opt.alpha Significance level for VaR estimation. (Default = 0.01)
opt.days Estimation time horizon. (Default = 1 day)
opt.nsimu Number of Monte-Carlo simulations. (Default = 1000)
Output:
VaRMC Estimated VaR

Example:
library("VaR")
randomize(0)
VaRdelta=read("VaRdelta.dat")
VaRgamma=read("VaRgamma.dat")
VaRcovmatrix=read("VaRcovmatrix.dat")
opt=VaRopt("alpha",0.01,"days",1,"nsimu",1000)
VaRMC=VaRestMC(VaRdelta, VaRgamma, VaRcovmatrix,"IS",opt)
VaRMC

Result:
Contents of VaRMC
[1,]   771.73



Author: W. Haerdle, Yuze Jiang, 20021109 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006