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#' Plots normal ES against confidence level
#'
#' Plots the ES of a portfolio against confidence level assuming that P/L are
#' normally distributed, for specified confidence level and holding period.
#'
#' @param ... The input arguments contain either return data or else mean and
#' standard deviation data. Accordingly, number of input arguments is either 3
#' or 4. In case there 3 input arguments, the mean and standard deviation of
#' data is computed from return data. See examples for details.
#'
#' returns Vector of daily geometric return data
#'
#' mu Mean of daily geometric return data
#'
#' sigma Standard deviation of daily geometric return data
#'
#' cl ES confidence level and must be a vector
#'
#' hp ES holding period and must be a scalar
#'
#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
#'
#' @author Dinesh Acharya
#' @examples
#'
#' # Plots ES against confidence level
#' data <- runif(5, min = 0, max = .2)
#' NormalESPlot2DCL(returns = data, cl = seq(.9,.99,.01), hp = 60)
#'
#' # Plots ES against confidence level
#' NormalESPlot2DCL(mu = .012, sigma = .03, cl = seq(.9,.99,.01), hp = 40)
#'
#' @export
NormalESPlot2DCL <- function(...){
# Determine if there are four or five arguments, and ensure that arguments are read as intended
if (nargs() < 3) {
stop("Too few arguments")
}
if (nargs() > 4) {
stop("Too many arguments")
}
args <- list(...)
if (nargs() == 4) {
mu <- args$mu
cl <- args$cl
sigma <- args$sigma
hp <- args$hp
}
if (nargs() == 3) {
mu <- mean(args$returns)
cl <- args$cl
sigma <- sd(args$returns)
hp <- args$hp
}
# Check that inputs have correct dimensions
mu <- as.matrix(mu)
mu.row <- dim(mu)[1]
mu.col <- dim(mu)[2]
if (max(mu.row, mu.col) > 1) {
stop("Mean must be a scalar")
}
sigma <- as.matrix(sigma)
sigma.row <- dim(sigma)[1]
sigma.col <- dim(sigma)[2]
if (max(sigma.row, sigma.col) > 1) {
stop("Standard deviation must be a scalar")
}
cl <- as.matrix(cl)
cl.row <- dim(cl)[1]
cl.col <- dim(cl)[2]
if (min(cl.row, cl.col) > 1) {
stop("Confidence level must be a vector")
}
hp <- as.matrix(hp)
hp.row <- dim(hp)[1]
hp.col <- dim(hp)[2]
if (max(hp.row, hp.col) > 1) {
stop("Holding period must be a scalar")
}
# Check that hp is read as row vector
if (cl.row > cl.col) {
cl <- t(cl)
}
# Check that inputs obey sign and value restrictions
if (sigma < 0) {
stop("Standard deviation must be non-negative")
}
if (max(cl) >= 1){
stop("Confidence levels must be less than 1")
}
if (min(cl) <= 0){
stop("Confidence levels must be greater than 0")
}
if (min(hp) <= 0){
stop("Holding period must be greater than 0")
}
# ES estimation
v <- NormalES(mu = mu[1,1], sigma = sigma[1,1], cl = as.vector(cl), hp = hp[1,1])
# Plotting
plot(cl, v, type = "l", xlab = "Holding Period", ylab = "ES")
title("Normal ES against holding period")
xmin <-min(cl)+.25*(max(cl)-min(cl))
text(xmin,max(v)-.1*(max(v)-min(v)),
'Input parameters', cex=.75, font = 2)
text(xmin,max(v)-.15*(max(v)-min(v)),
paste('Daily mean geometric return = ',round(mu[1,1],3)),cex=.75)
text(xmin,max(v)-.2*(max(v)-min(v)),
paste('Stdev. of daily geometric returns = ',round(sigma[1,1],3)),cex=.75)
text(xmin,max(v)-.25*(max(v)-min(v)),
paste('Holding Period = ',hp),cex=.75)
}
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