[Returnanalytics-commits] r1994 - in pkg/PerformanceAnalytics: R man

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

Author: matthieu_lestel
Date: 2012-06-07 17:31:33 +0200 (Thu, 07 Jun 2012)
New Revision: 1994

Modified:
   pkg/PerformanceAnalytics/R/DownsideDeviation.R
   pkg/PerformanceAnalytics/man/DownsideDeviation.Rd
Log:
update in DownsideDeviation documentation for the html version

Modified: pkg/PerformanceAnalytics/R/DownsideDeviation.R
===================================================================
--- pkg/PerformanceAnalytics/R/DownsideDeviation.R	2012-06-07 12:14:48 UTC (rev 1993)
+++ pkg/PerformanceAnalytics/R/DownsideDeviation.R	2012-06-07 15:31:33 UTC (rev 1994)
@@ -6,7 +6,7 @@
 #' Downside deviation, similar to semi deviation, eliminates positive returns
 #' when calculating risk.  Instead of using the mean return or zero, it uses
 #' the Minimum Acceptable Return as proposed by Sharpe (which may be the mean
-#' historical return or zero). It measures the the variability of underperformance
+#' historical return or zero). It measures the variability of underperformance
 #' below a minimum targer rate. The downside variance is the square of the downside
 #' potential.
 #' 
@@ -17,11 +17,14 @@
 #' 
 #'
 #' \deqn{ DownsideDeviation(R , MAR)= \delta_{MAR} = \sqrt{\sum^{n}_{t=1}\frac{
-#' min[(R_{t} - MAR), 0]^2}{n}}}
+#' min[(R_{t} - MAR), 0]^2}{n}}} {DownsideDeviation(R, MAR) = sqrt(1/n * sum(t=1..n)
+#' ((min(R(t)-MAR, 0))^2))}
 #'
-#' \deqn{ DownsideVariance(R, MAR) = \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]^2} {n}}
+#' \deqn{ DownsideVariance(R, MAR) = \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]^2} {n}} 
+#' {DownsideVariance(R, MAR) = 1/n * sum(t=1..n)((min(R(t)-MAR, 0))^2)}
 #'
-#' \deqn{DownsidePotential(R, MAR) = \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]} {n}}
+#' \deqn{DownsidePotential(R, MAR) = \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]} {n}} 
+#' {DownsidePotential(R, MAR) =  1/n * sum(t=1..n)(min(R(t)-MAR, 0))}
 #'
 #' where \eqn{n} is either the number of observations of the entire series or
 #' the number of observations in the subset of the series falling below the
@@ -83,7 +86,8 @@
 #'
 #' #with data used in Bacon 2008
 #'
-#' portfolio_return <- c(0.3,2.6,1.1,-1.0,1.5,2.5,1.6,6.7,-1.4,4.0,-0.5,8.1,4.0,-3.7,-6.1,1.7,-4.9,-2.2,7.0,5.8,-6.5,2.4,-0.5,-0.9)
+#' portfolio_return <- c(0.3,2.6,1.1,-1.0,1.5,2.5,1.6,6.7,-1.4,4.0,-0.5,8.1,4.0,-3.7,
+#' -6.1,1.7,-4.9,-2.2,7.0,5.8,-6.5,2.4,-0.5,-0.9)
 #' MAR = 0.5
 #' DownsideDeviation(portfolio_return, MAR) #expected 2.55
 #' DownsidePotential(portfolio_return, MAR) #expected 1.37

Modified: pkg/PerformanceAnalytics/man/DownsideDeviation.Rd
===================================================================
--- pkg/PerformanceAnalytics/man/DownsideDeviation.Rd	2012-06-07 12:14:48 UTC (rev 1993)
+++ pkg/PerformanceAnalytics/man/DownsideDeviation.Rd	2012-06-07 15:31:33 UTC (rev 1994)
@@ -34,9 +34,9 @@
   positive returns when calculating risk.  Instead of using
   the mean return or zero, it uses the Minimum Acceptable
   Return as proposed by Sharpe (which may be the mean
-  historical return or zero). It measures the the
-  variability of underperformance below a minimum targer
-  rate. The downside variance is the square of the downside
+  historical return or zero). It measures the variability
+  of underperformance below a minimum targer rate. The
+  downside variance is the square of the downside
   potential.
 
   To calculate it, we take the subset of returns that are
@@ -47,12 +47,18 @@
 
   \deqn{ DownsideDeviation(R , MAR)= \delta_{MAR} =
   \sqrt{\sum^{n}_{t=1}\frac{ min[(R_{t} - MAR), 0]^2}{n}}}
+  {DownsideDeviation(R, MAR) = sqrt(1/n * sum(t=1..n)
+  ((min(R(t)-MAR, 0))^2))}
 
   \deqn{ DownsideVariance(R, MAR) =
   \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]^2} {n}}
+  {DownsideVariance(R, MAR) = 1/n *
+  sum(t=1..n)((min(R(t)-MAR, 0))^2)}
 
   \deqn{DownsidePotential(R, MAR) =
   \sum^{n}_{t=1}\frac{min[(R_{t} - MAR), 0]} {n}}
+  {DownsidePotential(R, MAR) = 1/n *
+  sum(t=1..n)((min(R(t)-MAR, 0)))}
 
   where \eqn{n} is either the number of observations of the
   entire series or the number of observations in the subset
@@ -95,7 +101,8 @@
 \examples{
 #with data used in Bacon 2008
 
-portfolio_return <- c(0.3,2.6,1.1,-1.0,1.5,2.5,1.6,6.7,-1.4,4.0,-0.5,8.1,4.0,-3.7,-6.1,1.7,-4.9,-2.2,7.0,5.8,-6.5,2.4,-0.5,-0.9)
+portfolio_return <- c(0.3,2.6,1.1,-1.0,1.5,2.5,1.6,6.7,-1.4,4.0,-0.5,8.1,4.0,-3.7,
+-6.1,1.7,-4.9,-2.2,7.0,5.8,-6.5,2.4,-0.5,-0.9)
 MAR = 0.5
 DownsideDeviation(portfolio_return, MAR) #expected 2.55
 DownsidePotential(portfolio_return, MAR) #expected 1.37