mctVaR_MNTS: mctVaR_MNTS

In aaron9011/temStaR-v0.90: Tempered Stable Distribution

mctVaR_MNTS

Description

Calculate the marginal contribution to VaR for the multivariate NTS market model: the random vector r is

r = \mu + diag(\sigma) X

where

X follows stdNTS_N(\alpha, \theta, \beta, \Sigma)

Usage

mctVaR_MNTS(n, eta, w, st)

Arguments

n	The target stock to calculate the mctVaR
eta	Significant level of VaR.
w	The capital allocation rate vector for the current portfolio
iCDFstd	The inverst cdf of stdNTS at the significant level eta. If NULL, the function automatically find it. The default value is NULL st$ndim : Dimension of the model. Here st$ndim=N. st$mu : \mu mean vector (column vector) of the input data. st$sigma : \sigma standard deviation vector (column vector) of the input data. st$alpha : \alpha of the std NTS distribution (X). st$theta : \theta of the std NTS distribution (X). st$beta : \beta vector (column vector) of the std NTS distribution (X). st$Rho : \rho matrix of the std NTS distribution (X), which is correlation matrix of epsilon. st$CovMtx : Covariance matrix of return data r.
st	Structure of parameters for the N-dimensional NTS distribution.

References

Kim, Y. S. (2020) Portfolio Optimization on the Dispersion Risk and the Asymmetric Tail Risk https://arxiv.org/pdf/2007.13972.pdf

Examples

library(functional)
library(nloptr)
library(pracma)
library(spatstat)
library(Matrix)
library(quantmod)
library(mvtnorm)
library(temStaR)

#Fix alpha and theta.
#Estimate alpha dna theta from DJIA and use those parameter for IBM, INTL parameter fit.
getSymbols("^DJI", src="yahoo", from = "2020-8-25", to = "2020-08-31")
prDJ <- as.numeric(DJI$DJI.Adjusted)
ret <- diff(log(prDJ))
ntsparam <-  fitnts(ret)
getSymbols("IBM", src="yahoo", from = "2016-1-1", to = "2020-08-31")
pr1 <- as.numeric(IBM$IBM.Adjusted)
getSymbols("INTL", src="yahoo", from = "2016-1-1", to = "2020-08-31")
pr2 <- as.numeric(INTL$INTL.Adjusted)

returndata <- matrix(data = c(diff(log(pr1)),diff(log(pr2))),
                     ncol = 2, nrow = (length(pr1)-1))
st <- fitmnts( returndata = returndata,
                n = 2,
                alphaNtheta = c(ntsparam["alpha"], ntsparam["theta"])  )
w <- c(0.3, 0.7)
eta <- 0.01

mctVaR_MNTS(1, eta, w, st) #MCT-VaR for IBM
mctVaR_MNTS(2, eta, w, st) #MCT-VaR for INTL

aaron9011/temStaR-v0.90 documentation built on June 1, 2025, 4:15 p.m.