[Returnanalytics-commits] r3847 - in pkg/Dowd: R man

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

Author: dacharya
Date: 2015-07-22 23:31:32 +0200 (Wed, 22 Jul 2015)
New Revision: 3847

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

Added: pkg/Dowd/R/NormalVaR.R
===================================================================
--- pkg/Dowd/R/NormalVaR.R	                        (rev 0)
+++ pkg/Dowd/R/NormalVaR.R	2015-07-22 21:31:32 UTC (rev 3847)
@@ -0,0 +1,118 @@
+#' VaR for normally distributed P/L
+#' 
+#' Estimates the VaR of a portfolio assuming that P/L is 
+#' normally distributed, for specified confidence level and holding period.
+#' 
+#' @param ... The input arguments contain either return data or else mean and 
+#'  standard deviation data along with the remaining arguments. 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
+#'  
+#'  hp VaR holding period in days
+#'  
+#' @return Matrix of VaR whose dimension depends on dimension of hp and cl. If 
+#' cl and hp are both scalars, the matrix is 1 by 1. If cl is a vector and hp is
+#'  a scalar, the matrix is row matrix, if cl is a scalar and hp is a vector, 
+#'  the matrix is column matrix and if both cl and hp are vectors, the matrix 
+#'  has dimension length of cl * length of hp.
+#'  
+#'  
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Computes VaR given geometric return data
+#'    data <- runif(5, min = 0, max = .2)
+#'    NormalVaR(returns = data, cl = .95, hp = 90)
+#'    
+#'    # Computes VaR given mean and standard deviation of return data
+#'    NormalVaR(mu = .012, sigma = .03, cl = .95, hp = 90)
+#'
+#'
+#' @export
+NormalVaR <- 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")
+  }
+  # 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
+  
+  return (VaR)
+}
\ No newline at end of file

Added: pkg/Dowd/man/NormalVaR.Rd
===================================================================
--- pkg/Dowd/man/NormalVaR.Rd	                        (rev 0)
+++ pkg/Dowd/man/NormalVaR.Rd	2015-07-22 21:31:32 UTC (rev 3847)
@@ -0,0 +1,50 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/NormalVaR.R
+\name{NormalVaR}
+\alias{NormalVaR}
+\title{VaR for normally distributed P/L}
+\usage{
+NormalVaR(...)
+}
+\arguments{
+\item{...}{The input arguments contain either return data or else mean and
+ standard deviation data along with the remaining arguments. 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
+
+ hp VaR holding period in days}
+}
+\value{
+Matrix of VaR whose dimension depends on dimension of hp and cl. If
+cl and hp are both scalars, the matrix is 1 by 1. If cl is a vector and hp is
+ a scalar, the matrix is row matrix, if cl is a scalar and hp is a vector,
+ the matrix is column matrix and if both cl and hp are vectors, the matrix
+ has dimension length of cl * length of hp.
+}
+\description{
+Estimates the VaR of a portfolio assuming that P/L is
+normally distributed, for specified confidence level and holding period.
+}
+\examples{
+# Computes VaR given geometric return data
+   data <- runif(5, min = 0, max = .2)
+   NormalVaR(returns = data, cl = .95, hp = 90)
+
+   # Computes VaR given mean and standard deviation of return data
+   NormalVaR(mu = .012, sigma = .03, cl = .95, hp = 90)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+