[Returnanalytics-commits] r3949 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-13 11:59:09 +0200 (Thu, 13 Aug 2015)
New Revision: 3949

Added:
   pkg/Dowd/R/NormalVaRHotspots.R
Log:
Function NormalVaRHotspot.R added.

Added: pkg/Dowd/R/NormalVaRHotspots.R
===================================================================
--- pkg/Dowd/R/NormalVaRHotspots.R	                        (rev 0)
+++ pkg/Dowd/R/NormalVaRHotspots.R	2015-08-13 09:59:09 UTC (rev 3949)
@@ -0,0 +1,72 @@
+#' @title Hotspots for normal VaR
+#' 
+#' @description Estimates the VaR hotspots (or vector of incremental VaRs) for 
+#' a portfolio assuming individual asset returns are normally distributed, for
+#' specified confidence level and holding period.
+#' 
+#' @param vc.matrix Variance covariance matrix for returns
+#' @param mu Vector of expected position returns
+#' @param positions Vector of positions
+#' @param cl Confidence level and is scalar
+#' @param hp Holding period and is scalar
+#' @return Hotspots for normal VaR
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#' 
+#' @author Dinesh Acharya
+#' 
+#' @examples
+#' 
+#'    # Hotspots for ES for randomly generated portfolio
+#'    vc.matrix <- matrix(rnorm(16),4,4)
+#'    mu <- rnorm(4,.08,.04)
+#'    positions <- c(5,2,6,10)
+#'    cl <- .95
+#'    hp <- 280
+#'    NormalVaRHotspots(vc.matrix, mu, positions, cl, hp)
+#'    
+#' @export
+NormalVaRHotspots <- function(vc.matrix, mu, positions, cl, hp){
+  
+  # Check that positions vector read as a scalar or row vector
+  positions <- as.matrix(positions)
+  if (dim(positions)[1] > dim(positions)[2]){
+    positions <- t(positions)
+  }
+  
+  # Check that expected returns vector is read as a scalar or row vector
+  mu <- as.matrix(mu)
+  if (dim(mu)[1] > dim(mu)[2]){
+    mu <- t(mu)
+  }
+  
+  # Check that dimensions are correct
+  if (max(dim(mu)) != max(dim(positions))){
+    stop("Positions vector and expected returns vector must have same size")
+  }
+  vc.matrix <- as.matrix(vc.matrix)
+  if (max(dim(vc.matrix)) != max(dim(positions))){
+    stop("Positions vector and expected returns vector must have same size")
+  }
+  
+  # Check that inputs obey sign and value restrictions
+  if (cl >= 1){
+    stop("Confidence level must be less than 1")
+  }
+  if (cl <= 0){
+    stop("Confidence level must be greater than 0");
+  }
+  if (hp <= 0){
+    stop("Holding period must be greater than 0");
+  }
+  
+  # VaR and ES estimation
+  VaR <- - mu %*% t(positions) * hp - qnorm(1 - cl, 0, 1) * (positions %*% vc.matrix %*% t(positions)) * sqrt(hp) # VaR
+  iVaR <- double(length(positions))
+  for (i in 1:length(positions)){
+    x <- positions
+    x[i] <- 0
+    iVaR[i] <- VaR + mu %*% t(x) %*% hp + qnorm(1 - cl, 0, 1) * (x %*% vc.matrix %*% t(x)) * sqrt(hp)
+  }
+  y <- iVaR
+  return(y)
+}
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