[Returnanalytics-commits] r3858 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-07-27 00:42:40 +0200 (Mon, 27 Jul 2015)
New Revision: 3858

Added:
   pkg/Dowd/R/NormalVaRPlot2DHP.R
Log:
Function NormalVaRPlot2DHP added

Added: pkg/Dowd/R/NormalVaRPlot2DHP.R
===================================================================
--- pkg/Dowd/R/NormalVaRPlot2DHP.R	                        (rev 0)
+++ pkg/Dowd/R/NormalVaRPlot2DHP.R	2015-07-26 22:42:40 UTC (rev 3858)
@@ -0,0 +1,120 @@
+#' Plots normal VaR against holding period
+#' 
+#' Plots the VaR of a portfolio against holding period 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 VaR confidence level and must be a scalar
+#' 
+#'  hp VaR holding period and must be a vector
+#'  
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Computes VaR given P/L data
+#'    data <- runif(5, min = 0, max = .2)
+#'    NormalVaRPlot2DHP(returns = data, cl = .95, hp = 60:90)
+#'    
+#'    # Computes VaR given mean and standard deviation of P/L data
+#'    NormalVaRPlot2DHP(mu = .012, sigma = .03, cl = .99, hp = 40:80)
+#'
+#'
+#' @export
+NormalVaRPlot2DHP <- 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$mu
+    investment <- args$investment
+    cl <- args$cl
+    sigma <- args$sigma
+    hp <- args$hp
+  }
+  if (nargs() == 3) {
+    mu <- mean(args$returns)
+    investment <- args$investment
+    cl <- args$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 must be less than 1")
+  }
+  if (min(cl) <= 0){
+    stop("Confidence level must be greater than 0")
+  }
+  if (min(hp) <= 0){
+    stop("Holding periods must be greater than 0")
+  }
+  # VaR estimation  
+  cl.row <- dim(cl)[1]
+  cl.col <- dim(cl)[2]
+  VaR <- - sigma[1,1] * sqrt(t(hp)) * qnorm(1 - cl[1,1], 0, 1)
+                        - mu[1,1] * t(hp) %*% matrix(1, cl.row, cl.col) # VaR
+  # Plotting
+  plot(hp, VaR, type = "l", xlab = "Holding Period", ylab = "VaR")
+  cl.label <- cl * 100
+  title("Normal VaR against holding period")
+  xmin <-min(hp)+.25*(max(hp)-min(hp))
+  text(xmin,max(VaR)-.1*(max(VaR)-min(VaR)),
+       'Input parameters', cex=.75, font = 2)
+  text(xmin,max(VaR)-.175*(max(VaR)-min(VaR)),
+       paste('Daily mean P/L = ',mu[1,1]),cex=.75)
+  text(xmin,max(VaR)-.25*(max(VaR)-min(VaR)),
+       paste('Stdev. of daily L/P = ',sigma[1,1]),cex=.75)
+  text(xmin,max(VaR)-.325*(max(VaR)-min(VaR)),
+       paste('Confidence level = ',cl.label,'%'),cex=.75)
+}