[Returnanalytics-commits] r3970 - pkg/Dowd/R

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

Author: dacharya
Date: 2015-08-20 00:32:58 +0200 (Thu, 20 Aug 2015)
New Revision: 3970

Modified:
   pkg/Dowd/R/AdjustedNormalVaRHotspots.R
Log:
Error message to be displayed changed.

Modified: pkg/Dowd/R/AdjustedNormalVaRHotspots.R
===================================================================
--- pkg/Dowd/R/AdjustedNormalVaRHotspots.R	2015-08-19 22:32:33 UTC (rev 3969)
+++ pkg/Dowd/R/AdjustedNormalVaRHotspots.R	2015-08-19 22:32:58 UTC (rev 3970)
@@ -1,85 +1,85 @@
-#' @title Hotspots for VaR adjusted by Cornish-Fisher correction
-#' 
-#' @description Estimates the VaR hotspots (or vector of incremental VaRs) for a
-#' portfolio with portfolio return adjusted for non-normality by Cornish-Fisher 
-#' corerction, for specified confidence level and holding period.
-#' 
-#' @param vc.matrix Variance covariance matrix for returns
-#' @param mu Vector of expected position returns
-#' @param skew Return skew
-#' @param kurtosis Return kurtosis
-#' @param positions Vector of positions
-#' @param cl Confidence level and is scalar
-#' @param hp Holding period and is scalar
-#' 
-#' @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)
-#'    skew <- .5
-#'    kurtosis <- 1.2
-#'    positions <- c(5,2,6,10)
-#'    cl <- .95
-#'    hp <- 280
-#'    AdjustedNormalVaRHotspots(vc.matrix, mu, skew, kurtosis, positions, cl, hp)
-#'    
-#' @export
-AdjustedNormalVaRHotspots <- function(vc.matrix, mu, skew, kurtosis, 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
-  z <- qnorm(1 - cl, 0 ,1)
-  sigma <- positions %*% vc.matrix %*% t(positions)/(sum(positions)^2) # Initial standard deviation of portfolio returns
-  adjustment <- (1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) * (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
-  VaR <- - mu %*% t(positions) * hp - (z + adjustment) * sigma * (sum(positions)^2) * sqrt(hp)
-  
-  # VaR
-  x <- double(length(positions))
-  sigma <- double(length(positions))
-  iVaR <- double(length(positions))
-  for (i in 1:length(positions)){
-    x <- positions
-    x[i] <- 0
-    sigma[i] <- x %*% vc.matrix %*% t(x)/sum(x)^2 # standard deviation of portfolio returns
-    iVaR[i] <- VaR + mu %*% t(x) %*% hp + (z + adjustment) * sigma[i] * (sum(x))^2 * sqrt(hp) # Incremental VaR
-  }
-  y <- iVaR
-  return(y)
-  
+#' @title Hotspots for VaR adjusted by Cornish-Fisher correction
+#' 
+#' @description Estimates the VaR hotspots (or vector of incremental VaRs) for a
+#' portfolio with portfolio return adjusted for non-normality by Cornish-Fisher 
+#' corerction, for specified confidence level and holding period.
+#' 
+#' @param vc.matrix Variance covariance matrix for returns
+#' @param mu Vector of expected position returns
+#' @param skew Return skew
+#' @param kurtosis Return kurtosis
+#' @param positions Vector of positions
+#' @param cl Confidence level and is scalar
+#' @param hp Holding period and is scalar
+#' 
+#' @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)
+#'    skew <- .5
+#'    kurtosis <- 1.2
+#'    positions <- c(5,2,6,10)
+#'    cl <- .95
+#'    hp <- 280
+#'    AdjustedNormalVaRHotspots(vc.matrix, mu, skew, kurtosis, positions, cl, hp)
+#'    
+#' @export
+AdjustedNormalVaRHotspots <- function(vc.matrix, mu, skew, kurtosis, 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 variance-covariance matrix must have compatible dimensions.")
+  }
+  
+  # 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
+  z <- qnorm(1 - cl, 0 ,1)
+  sigma <- positions %*% vc.matrix %*% t(positions)/(sum(positions)^2) # Initial standard deviation of portfolio returns
+  adjustment <- (1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) * (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
+  VaR <- - mu %*% t(positions) * hp - (z + adjustment) * sigma * (sum(positions)^2) * sqrt(hp)
+  
+  # VaR
+  x <- double(length(positions))
+  sigma <- double(length(positions))
+  iVaR <- double(length(positions))
+  for (i in 1:length(positions)){
+    x <- positions
+    x[i] <- 0
+    sigma[i] <- x %*% vc.matrix %*% t(x)/sum(x)^2 # standard deviation of portfolio returns
+    iVaR[i] <- VaR + mu %*% t(x) %*% hp + (z + adjustment) * sigma[i] * (sum(x))^2 * sqrt(hp) # Incremental VaR
+  }
+  y <- iVaR
+  return(y)
+  
 }
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