[Returnanalytics-commits] r3789 - in pkg/Dowd: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Jul 8 00:15:57 CEST 2015
Author: dacharya
Date: 2015-07-08 00:15:57 +0200 (Wed, 08 Jul 2015)
New Revision: 3789
Added:
pkg/Dowd/R/LogtESPlot2DHP.R
pkg/Dowd/man/LogtESPlot2DHP.Rd
Log:
LogtESPlot2DHP added.
Added: pkg/Dowd/R/LogtESPlot2DHP.R
===================================================================
--- pkg/Dowd/R/LogtESPlot2DHP.R (rev 0)
+++ pkg/Dowd/R/LogtESPlot2DHP.R 2015-07-07 22:15:57 UTC (rev 3789)
@@ -0,0 +1,135 @@
+#' Plots log-t ES against holding period
+#'
+#' Plots the ES of a portfolio against holding period assuming that geometric returns are
+#' Student t distributed, for specified confidence level and holding period.
+#'
+#' @param returns Vector of daily geometric return data
+#' @param mu Mean of daily geometric return data
+#' @param sigma Standard deviation of daily geometric return data
+#' @param investment Size of investment
+#' @param df Number of degrees of freedom in the t distribution
+#' @param cl ES confidence level and must be a scalar
+#' @param hp ES holding period and must be a vector
+#'
+#' @note The input arguments contain either return data or else mean and
+#' standard deviation data. Accordingly, number of input arguments is either 5
+#' or 6. In case there 5 input arguments, the mean and standard deviation of
+#' data is computed from return data. See examples for details.
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # Computes ES given geometric return data
+#' data <- runif(5, min = 0, max = .2)
+#' LogtESPlot2DHP(returns = data, investment = 5, df = 6, cl = .95, hp = 60:90)
+#'
+#' # Computes v given mean and standard deviation of return data
+#' LogtESPlot2DHP(mu = .012, sigma = .03, investment = 5, df = 6, cl = .99, hp = 40:80)
+#'
+#'
+#' @export
+LogtESPlot2DHP <- function(...){
+ if (nargs() < 5) {
+ stop("Too few arguments")
+ }
+ if (nargs() > 6) {
+ stop("Too many arguments")
+ }
+ args <- list(...)
+ if (nargs() == 6) {
+ mu <- args$mu
+ investment <- args$investment
+ df <- args$df
+ cl <- args$cl
+ sigma <- args$sigma
+ hp <- args$hp
+ }
+ if (nargs() == 5) {
+ mu <- mean(args$returns)
+ investment <- args$investment
+ df <- args$df
+ cl <- args$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 (max(cl.row, cl.col) > 1) {
+ stop("Confidence level must be a scalar")
+ }
+ hp <- as.matrix(hp)
+ hp.row <- dim(hp)[1]
+ hp.col <- dim(hp)[2]
+ if (min(hp.row, hp.col) > 1) {
+ stop("Holding period must be a vector")
+ }
+
+ # Check that hp is read as row vector
+ if (hp.row > hp.col) {
+ hp <- t(hp)
+ }
+
+ # Check that inputs obey sign and value restrictions
+ if (sigma < 0) {
+ stop("Standard deviation must be non-negative")
+ }
+ if (max(cl) >= 1){
+ stop("Confidence level(s) must be less than 1")
+ }
+ if (min(cl) <= 0){
+ stop("Confidence level(s) must be greater than 0")
+ }
+ if (min(hp) <= 0){
+ stop("Confidence level(s) must be greater than 0")
+ }
+ # VaR estimation
+ VaR <- investment - exp(((df - 2) / 2) * sigma[1,1] * sqrt(t(hp)) * qt(1 - cl[1,1], df)
+ + mu[1,1] * t(hp) %*% matrix(1, cl.row, cl.col) + log(investment)) # VaR
+
+ # ES etimation
+ n <- 1000 # Number of slices into which tail is divided
+ cl0 <- cl # Initial confidence level
+ delta.cl <- (1 - cl) / n # Increment to confidence level as each slice is taken
+ v <- VaR
+ for (i in 1:(n-1)) {
+ cl <- cl0 + i * delta.cl # Revised cl
+ v <- v + investment - exp(((df - 2) / df) * sigma[1,1] * sqrt(t(hp)) *
+ qt(1 - cl[1,1], df) + mu[1,1] * t(hp) %*%
+ matrix(1, cl.row, cl.col) + log(investment))
+ }
+ v <- v/n
+
+ # Plotting
+ plot(hp, v, type = "l", xlab = "Holding Period", ylab = "ES")
+ title("Log-t ES against holding period")
+ xmin <-min(hp)+.25*(max(hp)-min(hp))
+ 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 = ',mu[1,1]),cex=.75)
+ text(xmin,max(v)-.2*(max(v)-min(v)),
+ paste('Stdev. of daily geometric returns = ',sigma[1,1]),cex=.75)
+ text(xmin,max(v)-.25*(max(v)-min(v)),
+ paste('Degrees of freedom = ',df),cex=.75)
+ text(xmin,max(v)-.3*(max(v)-min(v)),
+ paste('Investment size = ',investment),cex=.75)
+ text(xmin,max(v)-.35*(max(v)-min(v)),
+ paste('Confidence level = ',cl,'%'),cex=.75)
+}
Added: pkg/Dowd/man/LogtESPlot2DHP.Rd
===================================================================
--- pkg/Dowd/man/LogtESPlot2DHP.Rd (rev 0)
+++ pkg/Dowd/man/LogtESPlot2DHP.Rd 2015-07-07 22:15:57 UTC (rev 3789)
@@ -0,0 +1,48 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/LogtESPlot2DHP.R
+\name{LogtESPlot2DHP}
+\alias{LogtESPlot2DHP}
+\title{Plots log-t ES against holding period}
+\usage{
+LogtESPlot2DHP(...)
+}
+\arguments{
+\item{returns}{Vector of daily geometric return data}
+
+\item{mu}{Mean of daily geometric return data}
+
+\item{sigma}{Standard deviation of daily geometric return data}
+
+\item{investment}{Size of investment}
+
+\item{df}{Number of degrees of freedom in the t distribution}
+
+\item{cl}{ES confidence level and must be a scalar}
+
+\item{hp}{ES holding period and must be a vector}
+}
+\description{
+Plots the ES of a portfolio against holding period assuming that geometric returns are
+Student t distributed, for specified confidence level and holding period.
+}
+\note{
+The input arguments contain either return data or else mean and
+ standard deviation data. Accordingly, number of input arguments is either 5
+ or 6. In case there 5 input arguments, the mean and standard deviation of
+ data is computed from return data. See examples for details.
+}
+\examples{
+# Computes ES given geometric return data
+ data <- runif(5, min = 0, max = .2)
+ LogtESPlot2DHP(returns = data, investment = 5, df = 6, cl = .95, hp = 60:90)
+
+ # Computes v given mean and standard deviation of return data
+ LogtESPlot2DHP(mu = .012, sigma = .03, investment = 5, df = 6, cl = .99, hp = 40:80)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
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