[Returnanalytics-commits] r2804 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . R Week1/Code man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 17 13:39:33 CEST 2013
Author: shubhanm
Date: 2013-08-17 13:39:33 +0200 (Sat, 17 Aug 2013)
New Revision: 2804
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week1/Code/GLMSmoothIndex.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
Log:
/.Rd files
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-17 04:49:45 UTC (rev 2803)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-17 11:39:33 UTC (rev 2804)
@@ -4,7 +4,6 @@
export(EMaxDDGBM)
export(GLMSmoothIndex)
export(QP.Norm)
-export(Return.GLM)
export(SterlingRatio.Normalized)
export(table.ComparitiveReturn.GLM)
export(table.EMaxDDGBM)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R 2013-08-17 04:49:45 UTC (rev 2803)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R 2013-08-17 11:39:33 UTC (rev 2804)
@@ -1,40 +1,29 @@
-#' Getmansky Lo Markov Unsmooth Return Model
-#'
-#'
-#' True returns represent the flow of information that would determine the equilibrium
+#' @title GLM Return Model
+#' @description True returns represent the flow of information that would determine the equilibrium
#' value of the fund's securities in a frictionless market. However, true economic
-#' returns are not observed. Instead, Rot
-#' denotes the reported or observed return in
-#' period t, which is a weighted average of the fund's true returns over the most recent k þ 1
-#' periods, includingthe current period.
-#' This averaging process captures the essence of smoothed returns in several
-#' respects. From the perspective of illiquidity-driven smoothing, is consistent
-#' with several models in the nonsynchronous tradingliterat ure. For example, Cohen
-#' et al. (1 986, Chapter 6.1) propose a similar weighted-average model for observed
-#' returns.
-#'
-#' The Geltner autocorrelation adjusted return series may be calculated via:
-#'
-#' @param Ra an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' returns are not observed. The returns to hedge funds and other alternative investments are often
+#' highly serially correlated.We propose an econometric model of return smoothingand develop estimators for the smoothing
+#' proï¬le as well as a smoothing-adjusted Sharpe ratio.
+#' @usage
+#' Return.GLM(edhec,4)
+#' @usage
+#' Return.GLM(edhec,4)
+#' @param
+#' Ra : an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
-
-#' @param q order of autocorrelation coefficient
-#' @author R
-#' @references "An econometric model of serial correlation and
-#' illiquidity in hedge fund returns
-#' Mila Getmansky1, Andrew W. Lo*, Igor Makarov
-#' MIT Sloan School of Management, 50 Memorial Drive, E52-432, Cambridge, MA 02142-1347, USA
-#' Received 16 October 2002; received in revised form 7 March 2003; accepted 15 May 2003
-#' Available online 10 July 2004
-#'
-#'
+#' @param
+#' q : order of autocorrelation coefficient lag factors
+#'
+#' @details
+#' To quantify the impact of all of these possible sources of serial correlation, denote by R(t)
+#' the true economic return of a hedge fund in period 't'; and let R(t) satisfy the following linear
+#' single-factor model: where:
+#' \deqn{R(0,t) = \theta_{0}R(t) + \theta_{1}R(t-1) + \theta_{2}R(t-2) .... + \theta_{k}R(t-k)}
+#' where \eqn{\theta}'i is defined as the weighted lag of autocorrelated lag and whose sum is 1.
+#' @author Brian Peterson,Peter Carl, Shubhankit Mohan
+#' @references Mila Getmansky, Andrew W. Lo, Igor Makarov,\emph{An econometric model of serial correlation and
+#' and illiquidity in hedge fund Returns},Journal of Financial Economics 74 (2004).
#' @keywords ts multivariate distribution models
-#' @examples
-#'
-#' data(edhec)
-#' Return.GLM(edhec,4)
-#'
-#' @export
Return.GLM <-
function (Ra,q=3)
{ # @author Brian G. Peterson, Peter Carl
@@ -82,6 +71,6 @@
# This R package is distributed under the terms of the GNU Public License (GPL)
# for full details see the file COPYING
#
-# $Id: Return.GLM.R 2163 2012-07-16 00:30:19Z braverock $
+# $Id: Return.GLM.R 2334 2013-04-01 16:57:25Z braverock $
#
###############################################################################
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week1/Code/GLMSmoothIndex.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week1/Code/GLMSmoothIndex.R 2013-08-17 04:49:45 UTC (rev 2803)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week1/Code/GLMSmoothIndex.R 2013-08-17 11:39:33 UTC (rev 2804)
@@ -1,6 +1,6 @@
-#'@title Getmansky Lo Markov Smoothing Index Parameter
-#'@description
-#'A useful summary statistic for measuring the concentration of weights is
+#' @title Getmansky Lo Markov Smoothing Index Parameter
+#' @description
+#' A useful summary statistic for measuring the concentration of weights is
#' a sum of square of Moving Average lag coefficient.
#' This measure is well known in the industrial organization literature as the
#' Herfindahl index, a measure of the concentration of firms in a given industry.
@@ -11,7 +11,7 @@
#' \deqn{ R_t = {\mu} + {\beta}{{\delta}}_t+ \xi_t}
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
-#' @author R
+#' @author Peter Carl
#' @aliases Return.Geltner
#' @references "An econometric model of serial correlation and illiquidity in
#' hedge fund returns" Mila Getmansky1, Andrew W. Lo*, Igor Makarov
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-17 04:49:45 UTC (rev 2803)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-17 11:39:33 UTC (rev 2804)
@@ -1,49 +1,44 @@
\name{Return.GLM}
\alias{Return.GLM}
-\title{Getmansky Lo Markov Unsmooth Return Model}
+\title{GLM Return Model}
\usage{
- Return.GLM(Ra, q = 3)
+ Return.GLM(edhec,4)
}
\arguments{
- \item{Ra}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
+ \item{Ra}{: an xts, vector, matrix, data frame,
+ timeSeries or zoo object of asset returns}
- \item{q}{order of autocorrelation coefficient}
+ \item{q}{: order of autocorrelation coefficient lag
+ factors}
}
\description{
True returns represent the flow of information that would
determine the equilibrium value of the fund's securities
in a frictionless market. However, true economic returns
- are not observed. Instead, Rot denotes the reported or
- observed return in period t, which is a weighted average
- of the fund's true returns over the most recent k þ 1
- periods, includingthe current period. This averaging
- process captures the essence of smoothed returns in
- several respects. From the perspective of
- illiquidity-driven smoothing, is consistent with several
- models in the nonsynchronous tradingliterat ure. For
- example, Cohen et al. (1 986, Chapter 6.1) propose a
- similar weighted-average model for observed returns.
+ are not observed. The returns to hedge funds and other
+ alternative investments are often highly serially
+ correlated.We propose an econometric model of return
+ smoothingand develop estimators for the smoothing
+ proï¬le as well as a smoothing-adjusted Sharpe ratio.
}
\details{
- The Geltner autocorrelation adjusted return series may be
- calculated via:
+ To quantify the impact of all of these possible sources
+ of serial correlation, denote by R(t) the true economic
+ return of a hedge fund in period 't'; and let R(t)
+ satisfy the following linear single-factor model: where:
+ \deqn{R(0,t) = \theta_{0}R(t) + \theta_{1}R(t-1) +
+ \theta_{2}R(t-2) .... + \theta_{k}R(t-k)} where
+ \eqn{\theta} is defined as the weighted lag of
+ autocorrelated lag and whose sum is 1.
}
-\examples{
-data(edhec)
-Return.GLM(edhec,4)
-}
\author{
- R
+ Brian Peterson,Peter Carl, Shubhankit Mohan
}
\references{
- "An econometric model of serial correlation and
- illiquidity in hedge fund returns Mila Getmansky1, Andrew
- W. Lo*, Igor Makarov MIT Sloan School of Management, 50
- Memorial Drive, E52-432, Cambridge, MA 02142-1347, USA
- Received 16 October 2002; received in revised form 7
- March 2003; accepted 15 May 2003 Available online 10 July
- 2004
+ Mila Getmansky, Andrew W. Lo, Igor Makarov,\emph{An
+ econometric model of serial correlation and and
+ illiquidity in hedge fund Returns},Journal of Financial
+ Economics 74 (2004).
}
\keyword{distribution}
\keyword{models}
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