[Returnanalytics-commits] r3969 - pkg/Dowd/R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Aug 20 00:32:34 CEST 2015
Author: dacharya
Date: 2015-08-20 00:32:33 +0200 (Thu, 20 Aug 2015)
New Revision: 3969
Modified:
pkg/Dowd/R/AdjustedNormalESHotspots.R
Log:
Error message to be displayed changed.
Modified: pkg/Dowd/R/AdjustedNormalESHotspots.R
===================================================================
--- pkg/Dowd/R/AdjustedNormalESHotspots.R 2015-08-16 12:54:50 UTC (rev 3968)
+++ pkg/Dowd/R/AdjustedNormalESHotspots.R 2015-08-19 22:32:33 UTC (rev 3969)
@@ -1,114 +1,114 @@
-#' @title Hotspots for ES adjusted by Cornish-Fisher correction
-#'
-#' @description Estimates the ES hotspots (or vector of incremental ESs) 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
-#' AdjustedNormalESHotspots(vc.matrix, mu, skew, kurtosis, positions, cl, hp)
-#'
-#' @export
-AdjustedNormalESHotspots <- 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")
- }
- 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
- # Begin with portfolio ES
- 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) # Initial VaR
- n <- 1000 # Number of slives into which tail is divided
- cl0 <- cl # Initial confidence level
- term <- VaR
- delta.cl <- (1 - cl) / n # Increment to confidence level
- for (k in 1:(n - 1)) {
- cl <- cl0 + k * delta.cl # Revised cl
- z <- qnorm(1 - cl, 0, 1)
- adjustment=(1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) *
- (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
- term <- term - mu %*% t(positions) * hp - (z + adjustment) * sigma *
- (sum(positions)^2) * sqrt(hp)
- }
- portfolio.ES <- term/n
-
- # Portfolio ES
- es <- double(length(positions))
- ies <- double(length(positions))
- for (j in 1:length(positions)) {
- x <- positions
- x[j] <- 0
- sigma <- x %*% vc.matrix %*% t(x) / (sum(x)^2)
- term[j] <- - mu %*% t(x) * hp - qnorm(1-cl, 0, 1) * x %*%
- vc.matrix %*% t(x) * sqrt(hp)
-
- for (k in 1:(n - 1)){
- cl <- cl0 + k * delta.cl # Revised cl
- z <- qnorm(1-cl, 0, 1)
- adjustment=(1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) *
- (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
- term[j] <- term[j] - mu %*% t(positions) * hp - (z + adjustment) *
- sigma * (sum(positions)^2) * sqrt(hp)
- }
- es[j] <- term[j]/n # ES on portfolio minus position j
- ies [j] <- portfolio.ES - es[j] # Incremental ES
-
- }
- y <- ies
- return(ies)
-
-}
+#' @title Hotspots for ES adjusted by Cornish-Fisher correction
+#'
+#' @description Estimates the ES hotspots (or vector of incremental ESs) 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
+#' AdjustedNormalESHotspots(vc.matrix, mu, skew, kurtosis, positions, cl, hp)
+#'
+#' @export
+AdjustedNormalESHotspots <- 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.")
+ }
+ 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
+ # Begin with portfolio ES
+ 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) # Initial VaR
+ n <- 1000 # Number of slives into which tail is divided
+ cl0 <- cl # Initial confidence level
+ term <- VaR
+ delta.cl <- (1 - cl) / n # Increment to confidence level
+ for (k in 1:(n - 1)) {
+ cl <- cl0 + k * delta.cl # Revised cl
+ z <- qnorm(1 - cl, 0, 1)
+ adjustment=(1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) *
+ (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
+ term <- term - mu %*% t(positions) * hp - (z + adjustment) * sigma *
+ (sum(positions)^2) * sqrt(hp)
+ }
+ portfolio.ES <- term/n
+
+ # Portfolio ES
+ es <- double(length(positions))
+ ies <- double(length(positions))
+ for (j in 1:length(positions)) {
+ x <- positions
+ x[j] <- 0
+ sigma <- x %*% vc.matrix %*% t(x) / (sum(x)^2)
+ term[j] <- - mu %*% t(x) * hp - qnorm(1-cl, 0, 1) * x %*%
+ vc.matrix %*% t(x) * sqrt(hp)
+
+ for (k in 1:(n - 1)){
+ cl <- cl0 + k * delta.cl # Revised cl
+ z <- qnorm(1-cl, 0, 1)
+ adjustment=(1 / 6) * (z ^ 2 - 1) * skew + (1 / 24) * (z ^ 3 - 3 * z) *
+ (kurtosis - 3) - (1 / 36) * (2 * z ^ 3 - 5 * z) * skew ^ 2
+ term[j] <- term[j] - mu %*% t(positions) * hp - (z + adjustment) *
+ sigma * (sum(positions)^2) * sqrt(hp)
+ }
+ es[j] <- term[j]/n # ES on portfolio minus position j
+ ies [j] <- portfolio.ES - es[j] # Incremental ES
+
+ }
+ y <- ies
+ return(ies)
+
+}
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