[Returnanalytics-commits] r3963 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-16 14:30:52 +0200 (Sun, 16 Aug 2015)
New Revision: 3963

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

Added: pkg/Dowd/R/BoxCoxVaR.R
===================================================================
--- pkg/Dowd/R/BoxCoxVaR.R	                        (rev 0)
+++ pkg/Dowd/R/BoxCoxVaR.R	2015-08-16 12:30:52 UTC (rev 3963)
@@ -0,0 +1,75 @@
+#' Estimates VaR with Box-Cox transformation
+#' 
+#' Function estimates the VaR 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 PandLdata Daily Profit/Loss data
+#' @param cl Confidence Level. It can be a scalar or a vector.
+#' @return Estimated Box-Cox VaR. 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(100)
+#'    BoxCoxVaR(a,.95)
+#' 
+#' @import forecast
+#' 
+#' @export
+BoxCoxVaR <- function(PandLdata, 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")
+  }
+  # Transform data and obtain lambda
+  loss.data <- -PandLdata
+  loss.data <- loss.data - min(loss.data) + 1
+  loss.data <- sort(loss.data)
+  lambda <- BoxCox.lambda(loss.data, method="loglik")
+  transdat <- BoxCox(loss.data, lambda)
+  
+  # Alternative method: 
+  # for dependence only on MASS and not on forecast package (not working yet!)
+  # model <- lm(loss.data~1)
+  # boxcox <- boxcox(model,plotit=FALSE)
+  # lambda <- with(bc, x[which.max(y)])
+  # box cox transformation
+  # if(lambda == 0) {
+  #  transdat <- log(loss.data)
+  # } else {
+  #  transdat <- (loss.data^lambda -1)/lambda
+  # }
+  
+  # Estimate mean and standard deviation of transformed data
+  mu <- mean(transdat)
+  sigma <- sd(transdat)
+  VaR <- double(length(cl))
+  for(i in 1:length(cl)){
+    VaR[i] <- (1 + lambda * (mu + sigma * qnorm(cl[i]))) ^ (1 / lambda) + min(-PandLdata) - 1 # i-th VaR
+  }
+  return(VaR)
+}
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