[Returnanalytics-commits] r3819 - in pkg/Dowd: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Jul 15 01:17:19 CEST 2015
Author: dacharya
Date: 2015-07-15 01:17:19 +0200 (Wed, 15 Jul 2015)
New Revision: 3819
Added:
pkg/Dowd/R/LogNormalVaR.R
pkg/Dowd/man/LogNormalVaR.Rd
Log:
Function LogNormalVaR added.
Added: pkg/Dowd/R/LogNormalVaR.R
===================================================================
--- pkg/Dowd/R/LogNormalVaR.R (rev 0)
+++ pkg/Dowd/R/LogNormalVaR.R 2015-07-14 23:17:19 UTC (rev 3819)
@@ -0,0 +1,115 @@
+#' VaR for normally distributed geometric returns
+#'
+#' Estimates the VaR of a portfolio assuming that geometric returns are
+#' normally 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
+#' @param 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.
+#'
+#' @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
+#'
+#' # Computes VaR given geometric return data
+#' data <- runif(5, min = 0, max = .2)
+#' LogNormalVaR(returns = data, investment = 5, cl = .95, hp = 90)
+#'
+#' # Computes VaR given mean and standard deviation of return data
+#' LogNormalVaR(mu = .012, sigma = .03, investment = 5, cl = .95, hp = 90)
+#'
+#'
+#' @export
+LogNormalVaR <- 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 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 <- 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
+
+ return (VaR)
+}
\ No newline at end of file
Added: pkg/Dowd/man/LogNormalVaR.Rd
===================================================================
--- pkg/Dowd/man/LogNormalVaR.Rd (rev 0)
+++ pkg/Dowd/man/LogNormalVaR.Rd 2015-07-14 23:17:19 UTC (rev 3819)
@@ -0,0 +1,53 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/LogNormalVaR.R
+\name{LogNormalVaR}
+\alias{LogNormalVaR}
+\title{VaR for normally distributed geometric returns}
+\usage{
+LogNormalVaR(...)
+}
+\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}
+
+\item{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 geometric returns are
+normally 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{
+# Computes VaR given geometric return data
+ data <- runif(5, min = 0, max = .2)
+ LogNormalVaR(returns = data, investment = 5, cl = .95, hp = 90)
+
+ # Computes VaR given mean and standard deviation of return data
+ LogNormalVaR(mu = .012, sigma = .03, investment = 5, cl = .95, hp = 90)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+
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