[Returnanalytics-commits] r3826 - in pkg/Dowd: R man

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

Author: dacharya
Date: 2015-07-16 10:16:00 +0200 (Thu, 16 Jul 2015)
New Revision: 3826

Added:
   pkg/Dowd/R/LogNormalVaRPlot3D.R
   pkg/Dowd/man/LogNormalVaRPlot3D.Rd
Log:
Function LogNormalVaRPlot3D added.

Added: pkg/Dowd/R/LogNormalVaRPlot3D.R
===================================================================
--- pkg/Dowd/R/LogNormalVaRPlot3D.R	                        (rev 0)
+++ pkg/Dowd/R/LogNormalVaRPlot3D.R	2015-07-16 08:16:00 UTC (rev 3826)
@@ -0,0 +1,115 @@
+#' Plots log normal VaR against confidence level and holding period
+#' 
+#' Plots the VaR of a portfolio against confidence level and holding period assuming that geometric
+#'  returns are normal 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 cl VaR confidence level and must be a vector
+#' @param hp VaR 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 4 
+#'  or 5. In case there 4 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
+#' 
+#'    # Plots VaR against confidene level given geometric return data
+#'    data <- runif(5, min = 0, max = .2)
+#'    LogNormalVaRPlot3D(returns = data, investment = 5, cl = seq(.85,.99,.01), hp = 60:90)
+#'    
+#'    # Computes VaR against confidence level given mean and standard deviation of return data
+#'    LogNormalVaRPlot3D(mu = .012, sigma = .03, investment = 5, cl = seq(.85,.99,.02), hp = 40:80)
+#'
+#'
+#' @export
+LogNormalVaRPlot3D <- function(...){
+  # Determine if there are four or five arguments, and ensure that arguments are read as intended
+  if (nargs() < 4) {
+    stop("Too few arguments")
+  }
+  if (nargs() > 5) {
+    stop("Too many arguments")
+  }
+  args <- list(...)
+  if (nargs() == 5) {
+    mu <- args$mu
+    investment <- args$investment
+    cl <- args$cl
+    sigma <- args$sigma
+    hp <- args$hp
+  }
+  if (nargs() == 4) {
+    mu <- mean(args$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 (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 (min(hp.row, hp.col) > 1) {
+    stop("Holding period must be a vector")
+  }
+  
+  # Check that cl is read as row vector
+  if (cl.row > cl.col) {
+    cl <- t(cl)
+  }
+  # Check that hp is read as column vector
+  if (hp.col > hp.row) {
+    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 levels must be less than 1")
+  }
+  if (min(cl) <= 0){
+    stop("Confidence levels 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 <- investment - exp( sigma[1,1] * sqrt(hp) %*% qnorm(1 - cl, 0, 1)  + mu[1,1] * hp %*% matrix(1,cl.row,cl.col) + log(investment)) # VaR
+  # Plotting
+  persp(x=cl, y=hp, t(VaR), xlab = "Confidence Level", 
+        ylab = "Holding Period", zlab = "VaR", 
+        main = "Log Normal VaR against confidence level and holding period")
+  
+}

Added: pkg/Dowd/man/LogNormalVaRPlot3D.Rd
===================================================================
--- pkg/Dowd/man/LogNormalVaRPlot3D.Rd	                        (rev 0)
+++ pkg/Dowd/man/LogNormalVaRPlot3D.Rd	2015-07-16 08:16:00 UTC (rev 3826)
@@ -0,0 +1,47 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/LogNormalVaRPlot3D.R
+\name{LogNormalVaRPlot3D}
+\alias{LogNormalVaRPlot3D}
+\title{Plots log normal VaR against confidence level and holding period}
+\usage{
+LogNormalVaRPlot3D(...)
+}
+\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{cl}{VaR confidence level and must be a vector}
+
+\item{hp}{VaR holding period and must be a vector}
+}
+\description{
+Plots the VaR of a portfolio against confidence level and holding period assuming that geometric
+ returns are normal 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 4
+ or 5. In case there 4 input arguments, the mean and standard deviation of
+ data is computed from return data. See examples for details.
+}
+\examples{
+# Plots VaR against confidene level given geometric return data
+   data <- runif(5, min = 0, max = .2)
+   LogNormalVaRPlot3D(returns = data, investment = 5, cl = seq(.85,.99,.01), hp = 60:90)
+
+   # Computes VaR against confidence level given mean and standard deviation of return data
+   LogNormalVaRPlot3D(mu = .012, sigma = .03, investment = 5, cl = seq(.85,.99,.02), hp = 40:80)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+