[Returnanalytics-commits] r3853 - in pkg/Dowd: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jul 24 21:47:57 CEST 2015
Author: dacharya
Date: 2015-07-24 21:47:56 +0200 (Fri, 24 Jul 2015)
New Revision: 3853
Added:
pkg/Dowd/R/NormalVaRFigure.R
pkg/Dowd/man/NormalVaRFigure.Rd
Log:
Function NormalVaRFigure added.
Added: pkg/Dowd/R/NormalVaRFigure.R
===================================================================
--- pkg/Dowd/R/NormalVaRFigure.R (rev 0)
+++ pkg/Dowd/R/NormalVaRFigure.R 2015-07-24 19:47:56 UTC (rev 3853)
@@ -0,0 +1,133 @@
+#' Figure of normal VaR and pdf against L/P
+#'
+#' Gives figure showing the VaR and probability distribution function against L/P of a portfolio assuming 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 VaR confidence level and should be scalar
+#'
+#' hp VaR holding period in days and should be scalar
+#'
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#'
+#' # Plots normal VaR and pdf against L/P data for given returns data
+#' data <- runif(5, min = 0, max = .2)
+#' NormalVaRFigure(returns = data, cl = .95, hp = 90)
+#'
+#' # Plots normal VaR and pdf against L/P data with given parameters
+#' NormalVaRFigure(mu = .012, sigma = .03, cl = .95, hp = 90)
+#'
+#'
+#' @export
+NormalVaRFigure <- function(...){
+ # Determine if there are three or four 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 scalar or a vector")
+ }
+ 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 scalar or a vector")
+ }
+
+ # Check that cl and hp are read as row and column vectors respectively
+ if (cl.row > cl.col) {
+ cl <- t(cl)
+ }
+ 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("Holding Period(s) must be greater than 0")
+ }
+
+ # Message to indicate how matrix of results is to be interpreted, if cl and hp both vary and results are given in matrix form
+ if (max(cl.row, cl.col) > 1 & max(hp.row, hp.col) > 1) {
+ print('VaR results with confidence level varying across row and holding period down column')
+ }
+
+ # VaR estimation
+ cl.row <- dim(cl)[1]
+ cl.col <- dim(cl)[2]
+ VaR <- - sigma[1,1] * sqrt(hp) %*% qnorm(1 - cl, 0, 1) - mu[1,1] * hp %*% matrix(1,cl.row,cl.col) # VaR
+
+ # Plotting
+ x.min <- -mu - 5 * sigma
+ x.max <- -mu + 5 * sigma
+ delta <- (x.max-x.min) / 100
+ x <- seq(x.min, x.max, delta)
+ p <- dlnorm(x, - mu, sigma)
+ plot(x, p, type = "l", xlim = c(x.min, x.max), ylim = c(0, max(p)*1.1), xlab = "Loss (+) / Profit (-)", ylab = "Probability", main = "Normal VaR")
+ u <- c(VaR, VaR)
+ v <- c(0, .6*max(p))
+ lines(0,0,2,.6,type="l")
+ lines(u, v, type = "l", col = "blue")
+ cl.for.label <- 100*cl
+ text(1,.95*max(p), pos = 1, 'Input parameters', cex=.75, font = 2)
+ text(1, .875*max(p),pos = 1, paste('Daily mean L/P = ', round(mu,2)), cex=.75)
+ text(1, .8*max(p),pos = 1, paste('St. dev. of daily L/P = ',round(sigma,2)), cex=.75)
+ text(1, .725*max(p),pos = 1, paste('Holding period = ', hp,' day(s)'), cex=.75)
+ text(VaR, .7*max(p),pos = 2, paste('VaR at ', cl.for.label,'% CL'), cex=.75)
+ text(VaR, .64 * max(p),pos = 2, paste('= ',VaR), cex=.75)
+}
Added: pkg/Dowd/man/NormalVaRFigure.Rd
===================================================================
--- pkg/Dowd/man/NormalVaRFigure.Rd (rev 0)
+++ pkg/Dowd/man/NormalVaRFigure.Rd 2015-07-24 19:47:56 UTC (rev 3853)
@@ -0,0 +1,42 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/NormalVaRFigure.R
+\name{NormalVaRFigure}
+\alias{NormalVaRFigure}
+\title{Figure of normal VaR and pdf against L/P}
+\usage{
+NormalVaRFigure(...)
+}
+\arguments{
+\item{...}{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 VaR confidence level and should be scalar
+
+ hp VaR holding period in days and should be scalar}
+}
+\description{
+Gives figure showing the VaR and probability distribution function against L/P of a portfolio assuming P/L are normally distributed, for specified confidence level and holding period.
+}
+\examples{
+# Plots normal VaR and pdf against L/P data for given returns data
+ data <- runif(5, min = 0, max = .2)
+ NormalVaRFigure(returns = data, cl = .95, hp = 90)
+
+ # Plots normal VaR and pdf against L/P data with given parameters
+ NormalVaRFigure(mu = .012, sigma = .03, cl = .95, hp = 90)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
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