[Returnanalytics-commits] r3719 - in pkg/Dowd: R man

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

Author: dacharya
Date: 2015-06-23 11:23:48 +0200 (Tue, 23 Jun 2015)
New Revision: 3719

Modified:
   pkg/Dowd/R/ProductCopulaVaR.R
   pkg/Dowd/man/ProductCopulaVaR.Rd
Log:
Example changed so that the result lies in assumed region. Missing return was also added.

Modified: pkg/Dowd/R/ProductCopulaVaR.R
===================================================================
--- pkg/Dowd/R/ProductCopulaVaR.R	2015-06-23 09:22:57 UTC (rev 3718)
+++ pkg/Dowd/R/ProductCopulaVaR.R	2015-06-23 09:23:48 UTC (rev 3719)
@@ -1,63 +1,64 @@
-#' Bivariate Product Copule VaR
-#' 
-#' Derives VaR using bivariate Product or logistic copula with specified inputs 
-#' for normal marginals.
-#' 
-#' @param mu1 Mean of Profit/Loss on first position 
-#' @param mu2 Mean of Profit/Loss on second position
-#' @param sigma1 Standard Deviation of Profit/Loss on first position
-#' @param sigma2 Standard Deviation of Profit/Loss on second position
-#' @param cl VaR onfidece level
-#' @return Copula based VaR
-#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
-#' 
-#' Dowd, K. and Fackler, P. Estimating VaR with copulas. Financial Engineering
-#' News, 2004.
-#'
-#' 
-#' @author Dinesh Acharya
-#' @examples
-#' 
-#'    # VaR using bivariate Product for X and Y with given parameters:
-#'    ProductCopulaVaR(2.3, 4.1, 1.2, 1.5, .95)
-#'
-#' @export
-ProductCopulaVaR <- function(mu1, mu2, sigma1, sigma2, cl){
-  
-  p <- 1 - cl # p is tail probability or cdf
-  
-  # Compute portfolio mean and sigma (NB: These are used here to help compute
-  # initial bounds automatically)
-  portfolio.mu <- mu1 + mu2
-  portfolio.variance <- sigma1^2+sigma2^2
-  portfolio.sigma <- sqrt(portfolio.variance)
-  
-  # Specify bounds arbitrarily (NB: Would need to change manually if these were
-  # inappropriate)
-  L <- -portfolio.mu - 5 * portfolio.sigma
-  fL <- CdfOfSumUsingProductCopula(L, mu1, mu2, sigma1, sigma2) - p
-  sign.fL <- sign(fL)
-  U <- -portfolio.mu + 5 *  portfolio.sigma
-  fU <- CdfOfSumUsingProductCopula(U, mu1, mu2, sigma1, sigma2) - p
-  sign.fU <- sign(fU)
-  if (sign.fL == sign.fU){
-    stop("Assumed bounds do not include answer")
-  }
-  
-  # Bisection Algorithm
-  tol <- 0.0001 # Tolerance level (NM: change manually if desired)
-  while (U - L > tol){
-    x <- (L + U) / 2 # Bisection carried out in terms of P/L quantiles or minus VaR
-    cum.prob <- CdfOfSumUsingProductCopula(x, mu1, mu2, sigma1, sigma2)
-    fx <- cum.prob - p
-    if (sign(fx) == sign(fL)){
-      L <- x
-      fL <- fx
-    } else {
-      U <- x
-      fU <- fx
-    }
-  }
-  y <- -x # VaR is negative of terminal x-value or P/L quantile
-  
-}
\ No newline at end of file
+#' Bivariate Product Copule VaR
+#' 
+#' Derives VaR using bivariate Product or logistic copula with specified inputs 
+#' for normal marginals.
+#' 
+#' @param mu1 Mean of Profit/Loss on first position 
+#' @param mu2 Mean of Profit/Loss on second position
+#' @param sigma1 Standard Deviation of Profit/Loss on first position
+#' @param sigma2 Standard Deviation of Profit/Loss on second position
+#' @param cl VaR onfidece level
+#' @return Copula based VaR
+#' @references Dowd, K. Measuring Market Risk, Wiley, 2007.
+#' 
+#' Dowd, K. and Fackler, P. Estimating VaR with copulas. Financial Engineering
+#' News, 2004.
+#'
+#' 
+#' @author Dinesh Acharya
+#' @examples
+#' 
+#'    # VaR using bivariate Product for X and Y with given parameters:
+#'    ProductCopulaVaR(.9, 2.1, 1.2, 1.5, .95)
+#'
+#' @export
+ProductCopulaVaR <- function(mu1, mu2, sigma1, sigma2, cl){
+  
+  p <- 1 - cl # p is tail probability or cdf
+  
+  # Compute portfolio mean and sigma (NB: These are used here to help compute
+  # initial bounds automatically)
+  portfolio.mu <- mu1 + mu2
+  portfolio.variance <- sigma1^2+sigma2^2
+  portfolio.sigma <- sqrt(portfolio.variance)
+  
+  # Specify bounds arbitrarily (NB: Would need to change manually if these were
+  # inappropriate)
+  L <- -portfolio.mu - 5 * portfolio.sigma
+  fL <- CdfOfSumUsingProductCopula(L, mu1, mu2, sigma1, sigma2) - p
+  sign.fL <- sign(fL)
+  U <- -portfolio.mu + 5 *  portfolio.sigma
+  fU <- CdfOfSumUsingProductCopula(U, mu1, mu2, sigma1, sigma2) - p
+  sign.fU <- sign(fU)
+  if (sign.fL == sign.fU){
+    stop("Assumed bounds do not include answer")
+  }
+  
+  # Bisection Algorithm
+  tol <- 0.0001 # Tolerance level (NM: change manually if desired)
+  while (U - L > tol){
+    x <- (L + U) / 2 # Bisection carried out in terms of P/L quantiles or minus VaR
+    cum.prob <- CdfOfSumUsingProductCopula(x, mu1, mu2, sigma1, sigma2)
+    fx <- cum.prob - p
+    if (sign(fx) == sign(fL)){
+      L <- x
+      fL <- fx
+    } else {
+      U <- x
+      fU <- fx
+    }
+  }
+  y <- -x # VaR is negative of terminal x-value or P/L quantile
+  
+  return(y)
+}

Modified: pkg/Dowd/man/ProductCopulaVaR.Rd
===================================================================
--- pkg/Dowd/man/ProductCopulaVaR.Rd	2015-06-23 09:22:57 UTC (rev 3718)
+++ pkg/Dowd/man/ProductCopulaVaR.Rd	2015-06-23 09:23:48 UTC (rev 3719)
@@ -26,7 +26,7 @@
 }
 \examples{
 # VaR using bivariate Product for X and Y with given parameters:
-   ProductCopulaVaR(2.3, 4.1, 1.2, 1.5, .95)
+   ProductCopulaVaR(.9, 2.1, 1.2, 1.5, .95)
 }
 \author{
 Dinesh Acharya