[Returnanalytics-commits] r3967 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-16 14:54:33 +0200 (Sun, 16 Aug 2015)
New Revision: 3967

Added:
   pkg/Dowd/R/BoxCoxES.R
Log:
Function BoxCoxES.R added

Added: pkg/Dowd/R/BoxCoxES.R
===================================================================
--- pkg/Dowd/R/BoxCoxES.R	                        (rev 0)
+++ pkg/Dowd/R/BoxCoxES.R	2015-08-16 12:54:33 UTC (rev 3967)
@@ -0,0 +1,54 @@
+#' Estimates ES with Box-Cox transformation
+#' 
+#' Function estimates the ES of a portfolio assuming P and L data set transformed
+#' using the BoxCox transformation to make it as near normal as possible, for 
+#' specified confidence level and holding period implied by data frequency.
+#' 
+#' @param loss.data Daily Profit/Loss data
+#' @param cl Confidence Level. It can be a scalar or a vector.
+#' @return Estimated Box-Cox ES. Its dimension is same as that of cl
+#' 
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#'
+#' Hamilton, S. A. and Taylor, M. G. A Comparision of the Box-Cox 
+#' transformation method and nonparametric methods for estimating quantiles
+#' in clinical data with repeated measures. J. Statist. Comput. Simul., vol. 
+#' 45, 1993, pp. 185 - 201.
+#' 
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # Estimates Box-Cox VaR
+#'    a<-rnorm(200)
+#'    BoxCoxES(a,.95)
+#' 
+#' @import forecast
+#' 
+#' @export
+BoxCoxES <- function(loss.data, cl){
+  # Check that inputs have correct dimensions
+  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")
+  }
+  
+  if (cl.row > cl.col) {
+    cl <- t(cl)
+  }
+  
+  # Check that inputs obey sign and value restrictions
+  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")
+  }
+  # ES Estimation
+  VaR <- BoxCoxVaR(loss.data, cl) # HS VaR
+  k <- which(VaR<loss.data) # Finds indices of tail loss data
+  tail.losses <- loss.data[k] # Creates data set of tail loss observations
+  ES <- mean(tail.losses) # ES
+  return(ES) 
+}
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