[Returnanalytics-commits] r3854 - in pkg/Dowd: R man

[noreply at r-forge.r-project.org]

Author: dacharya
Date: 2015-07-24 21:48:23 +0200 (Fri, 24 Jul 2015)
New Revision: 3854

Added:
   pkg/Dowd/R/NormalVaRPlot2DCL.R
   pkg/Dowd/man/NormalVaRPlot2DCL.Rd
Log:
Function NormalVaRPlot2DCL added.

Added: pkg/Dowd/R/NormalVaRPlot2DCL.R
===================================================================
--- pkg/Dowd/R/NormalVaRPlot2DCL.R	                        (rev 0)
+++ pkg/Dowd/R/NormalVaRPlot2DCL.R	2015-07-24 19:48:23 UTC (rev 3854)
@@ -0,0 +1,117 @@
+#' Plots normal VaR against confidence level
+#' 
+#' Plots the VaR 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 are 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 must be a vector
+#' 
+#'  hp VaR holding period and must be a scalar
+#'  
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Plots VaR against confidene level given P/L data
+#'    data <- runif(5, min = 0, max = .2)
+#'    NormalVaRPlot2DCL(returns = data, cl = seq(.85,.99,.01), hp = 60)
+#'    
+#'    # Computes VaR against confidence level given mean and standard deviation of return data
+#'    NormalVaRPlot2DCL(mu = .012, sigma = .03, cl = seq(.85,.99,.01), hp = 40)
+#'
+#'
+#' @export
+NormalVaRPlot2DCL <- 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 cl 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 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 must be greater than 0")
+  }
+  # VaR estimation  
+  cl.row <- dim(cl)[1]
+  cl.col <- dim(cl)[2]
+  VaR <- - sigma[1,1] * sqrt(hp[1,1]) * qnorm(1 - cl, 0, 1) - mu[1,1]*hp[1,1]*matrix(1,cl.row,cl.col) # VaR
+  # Plotting
+  plot(cl, VaR, type = "l", xlab = "Confidence Level", ylab = "VaR")
+  title("Normal VaR against confidence level")
+  xmin <-min(cl)+.3*(max(cl)-min(cl))
+  text(xmin,max(VaR)-.1*(max(VaR)-min(VaR)),
+       'Input parameters', cex=.75, font = 2)
+  text(xmin,max(VaR)-.15*(max(VaR)-min(VaR)),
+       paste('Daily mean L/P = ',round(mu[1,1],3)),cex=.75)
+  text(xmin,max(VaR)-.2*(max(VaR)-min(VaR)),
+       paste('Stdev. of daily L/P = ',round(sigma[1,1],3)),cex=.75)
+  text(xmin,max(VaR)-.25*(max(VaR)-min(VaR)),
+       paste('Holding period = ',hp,'days'),cex=.75)
+}

Added: pkg/Dowd/man/NormalVaRPlot2DCL.Rd
===================================================================
--- pkg/Dowd/man/NormalVaRPlot2DCL.Rd	                        (rev 0)
+++ pkg/Dowd/man/NormalVaRPlot2DCL.Rd	2015-07-24 19:48:23 UTC (rev 3854)
@@ -0,0 +1,43 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/NormalVaRPlot2DCL.R
+\name{NormalVaRPlot2DCL}
+\alias{NormalVaRPlot2DCL}
+\title{Plots normal VaR against confidence level}
+\usage{
+NormalVaRPlot2DCL(...)
+}
+\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 are 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 must be a vector
+
+ hp VaR holding period and must be a scalar}
+}
+\description{
+Plots the VaR of a portfolio against confidence level assuming that P/L are normally distributed, for specified confidence level and
+ holding period.
+}
+\examples{
+# Plots VaR against confidene level given P/L data
+   data <- runif(5, min = 0, max = .2)
+   NormalVaRPlot2DCL(returns = data, cl = seq(.85,.99,.01), hp = 60)
+
+   # Computes VaR against confidence level given mean and standard deviation of return data
+   NormalVaRPlot2DCL(mu = .012, sigma = .03, cl = seq(.85,.99,.01), hp = 40)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+