[Returnanalytics-commits] r3796 - in pkg/Dowd: R man

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

Author: dacharya
Date: 2015-07-09 16:16:46 +0200 (Thu, 09 Jul 2015)
New Revision: 3796

Modified:
   pkg/Dowd/R/HSESDFPerc.R
   pkg/Dowd/man/HSESDFPerc.Rd
Log:
Function HSESDFPerc added.

Modified: pkg/Dowd/R/HSESDFPerc.R
===================================================================
--- pkg/Dowd/R/HSESDFPerc.R	2015-07-09 14:15:13 UTC (rev 3795)
+++ pkg/Dowd/R/HSESDFPerc.R	2015-07-09 14:16:46 UTC (rev 3796)
@@ -1,82 +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)
-  
+#' @title Percentile of historical simulation ES distribution function
+#'
+#' @description Estimates percentiles of historical simulation ES 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

Modified: pkg/Dowd/man/HSESDFPerc.Rd
===================================================================
--- pkg/Dowd/man/HSESDFPerc.Rd	2015-07-09 14:15:13 UTC (rev 3795)
+++ pkg/Dowd/man/HSESDFPerc.Rd	2015-07-09 14:16:46 UTC (rev 3796)
@@ -2,7 +2,7 @@
 % Please edit documentation in R/HSESDFPerc.R
 \name{HSESDFPerc}
 \alias{HSESDFPerc}
-\title{Percentile of historical simulation VaR distribution function}
+\title{Percentile of historical simulation ES distribution function}
 \usage{
 HSESDFPerc(Ra, perc, cl)
 }
@@ -17,7 +17,7 @@
 Value of percentile of VaR distribution function
 }
 \description{
-Estimates percentiles of historical simulation VaR distribution
+Estimates percentiles of historical simulation ES distribution
 function, using theory of order statistics, for specified confidence level.
 }
 \examples{