[Returnanalytics-commits] r3748 - in pkg/Dowd: R man

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

Author: dacharya
Date: 2015-06-26 20:47:29 +0200 (Fri, 26 Jun 2015)
New Revision: 3748

Added:
   pkg/Dowd/R/HSESDFPerc.R
   pkg/Dowd/man/HSESDFPerc.Rd
Log:
HSESDFPerc added.

Added: pkg/Dowd/R/HSESDFPerc.R
===================================================================
--- pkg/Dowd/R/HSESDFPerc.R	                        (rev 0)
+++ pkg/Dowd/R/HSESDFPerc.R	2015-06-26 18:47:29 UTC (rev 3748)
@@ -0,0 +1,82 @@
+#' @title Percentile of historical simulation VaR distribution function
+#'
+#' @description Estimates percentiles of historical simulation VaR distribution 
+#' function, using theory of order statistics, for specified confidence level.
+#'
+#' @param Ra Vector of daily P/L data
+#' @param perc Desired percentile and is scalar
+#' @param cl VaR confidence level and is scalar
+#' @return Value of percentile of VaR distribution function
+#' 
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#' 
+#' 
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Estimates Percentiles for random standard normal returns and given perc 
+#'    # and cl
+#'    Ra <- rnorm(100)
+#'    HSESDFPerc(Ra, .75, .95)
+#'
+#' @export
+HSESDFPerc <- function(Ra, perc, cl){
+  
+  # Determine if there are three arguments, and ensure that arguments are read as intended
+  if (nargs() < 3) {
+    stop("Too few arguments.")
+  }
+  if (nargs() > 3) {
+    stop("Too many arguments")
+  }
+  if (nargs() == 3) {
+    profit.loss <- as.vector(Ra)
+    data <- sort(profit.loss)
+    n <- length(data)
+  }
+  
+  # Check that inputs obey sign and value restrictions
+  if (n < 0) {
+    stop("Number of observations must be greater than zero.")
+  }
+  if (perc <= 0) {
+    stop("Chosen percentile must be positive.")
+  }
+  if (perc > 1) {
+    stop("Chosen percentile must not exceed 1")
+  }
+  if (cl >= 1) {
+    stop("Confidence level must be less than 1.")
+  }
+  if (cl <= 0) {
+    stop("Confidence level must positive.")
+  }
+  
+  # Derive order statistics and ensure it is an integer
+  w <- n * cl # Derive rth order statistics
+  r <- round(w) # Round r to nearest integer
+  
+  # Bisection routine
+  a <- 0
+  fa <- -Inf
+  b <- 1
+  fb <- Inf
+  eps <- .Machine$double.eps
+  while (b - a > eps * b) {
+    x <- (a + b) / 2
+    fx <- 1 - pbinom(r - 1, n, x) - perc
+    if (sign(fx) == sign(fa)){
+      a = x
+      fa = fx
+    } else {
+      b = x
+      fb = fx
+    }
+  }
+  i <- round(n * x)
+  VaR <- data[i] # Value of percentile of VaR distribution function
+  j <- i:n
+  y <- mean(data[j]) # Value of percentile of ES distribution function
+  return(y)
+  
+} 
\ No newline at end of file

Added: pkg/Dowd/man/HSESDFPerc.Rd
===================================================================
--- pkg/Dowd/man/HSESDFPerc.Rd	                        (rev 0)
+++ pkg/Dowd/man/HSESDFPerc.Rd	2015-06-26 18:47:29 UTC (rev 3748)
@@ -0,0 +1,35 @@
+% Generated by roxygen2 (4.1.1): do not edit by hand
+% Please edit documentation in R/HSESDFPerc.R
+\name{HSESDFPerc}
+\alias{HSESDFPerc}
+\title{Percentile of historical simulation VaR distribution function}
+\usage{
+HSESDFPerc(Ra, perc, cl)
+}
+\arguments{
+\item{Ra}{Vector of daily P/L data}
+
+\item{perc}{Desired percentile and is scalar}
+
+\item{cl}{VaR confidence level and is scalar}
+}
+\value{
+Value of percentile of VaR distribution function
+}
+\description{
+Estimates percentiles of historical simulation VaR distribution
+function, using theory of order statistics, for specified confidence level.
+}
+\examples{
+# Estimates Percentiles for random standard normal returns and given perc
+   # and cl
+   Ra <- rnorm(100)
+   HSESDFPerc(Ra, .75, .95)
+}
+\author{
+Dinesh Acharya
+}
+\references{
+Dowd, K. Measuring Market Risk, Wiley, 2007.
+}
+