[Returnanalytics-commits] r3929 - pkg/Dowd/R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Aug 9 01:05:41 CEST 2015
Author: dacharya
Date: 2015-08-09 01:05:41 +0200 (Sun, 09 Aug 2015)
New Revision: 3929
Added:
pkg/Dowd/R/tVaRPlot2DHP.R
Log:
Function tVaRPlot2DHP added
Added: pkg/Dowd/R/tVaRPlot2DHP.R
===================================================================
--- pkg/Dowd/R/tVaRPlot2DHP.R (rev 0)
+++ pkg/Dowd/R/tVaRPlot2DHP.R 2015-08-08 23:05:41 UTC (rev 3929)
@@ -0,0 +1,136 @@
+#' Plots t VaR against holding period
+#'
+#' Plots the VaR of a portfolio against holding period assuming that P/L are
+#' t- 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 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.
+#'
+#' returns Vector of daily P/L data data
+#'
+#' mu Mean of daily P/L data data
+#'
+#' sigma Standard deviation of daily P/L data data
+#'
+#' df Number of degrees of freedom in the t distribution
+#'
+#' 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
+#' data <- runif(5, min = 0, max = .2)
+#' tVaRPlot2DHP(returns = data, df = 6, cl = .95, hp = 60:90)
+#'
+#' # Computes VaR given mean and standard deviation of return data
+#' tVaRPlot2DHP(mu = .012, sigma = .03, df = 6, cl = .99, hp = 40:80)
+#'
+#'
+#' @export
+tVaRPlot2DHP <- 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$mu
+ df <- args$df
+ cl <- args$cl
+ sigma <- args$sigma
+ hp <- args$hp
+ }
+ if (nargs() == 4) {
+ mu <- mean(args$returns)
+ df <- args$df
+ cl <- args$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")
+ }
+ df <- as.matrix(df)
+ df.row <- dim(df)[1]
+ df.col <- dim(df)[2]
+ if (max(df.row, df.col) > 1) {
+ stop("Number of degrees of freedom must be a scalar")
+ }
+
+ # 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 (df < 3) {
+ stop("Number of degrees of freedom must be at least 3 for first two moments
+ of distribution to be defined")
+ }
+ 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("Confidence level(s) must be greater than 0")
+ }
+ # VaR estimation
+ cl.row <- dim(cl)[1]
+ cl.col <- dim(cl)[2]
+ VaR <- (-sigma[1,1] * sqrt(t(hp)) %*% sqrt((df - 2) / df) %*% qt(1 - cl, df)) + (- 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 <- 100 * cl[1,1]
+ title("t 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)-.15*(max(VaR)-min(VaR)),
+ paste('Daily mean L/P data = ',-mu[1,1]),cex=.75)
+ text(xmin,max(VaR)-.2*(max(VaR)-min(VaR)),
+ paste('Stdev. of daily L/P data = ',sigma[1,1]),cex=.75)
+ text(xmin,max(VaR)-.25*(max(VaR)-min(VaR)),
+ paste('Degrees of freedom = ',df),cex=.75)
+ text(xmin,max(VaR)-.3*(max(VaR)-min(VaR)),
+ paste('Confidence level = ',cl.label,'%'),cex=.75)
+}
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