[Returnanalytics-commits] r2871 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 24 11:24:24 CEST 2013
Author: shubhanm
Date: 2013-08-24 11:24:23 +0200 (Sat, 24 Aug 2013)
New Revision: 2871
Added:
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/LoSharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/quad.Rd
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
Log:
.Rd details added
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,37 +1,38 @@
-Package: noniid.sm
-Type: Package
-Title: Non-i.i.d. GSoC 2013 Shubhankit
-Version: 0.1
-Date: $Date: 2013-05-13 14:30:22 -0500 (Mon, 13 May 2013) $
-Author: Shubhankit Mohan <shubhankit1 at gmail.com>
-Contributors: Peter Carl, Brian G. Peterson
-Depends:
- xts,
- PerformanceAnalytics
-Suggests:
- PortfolioAnalytics
-Maintainer: Brian G. Peterson <brian at braverock.com>
-Description: GSoC 2013 project to replicate literature on drawdowns and
- non-i.i.d assumptions in finance.
-License: GPL-3
-ByteCompile: TRUE
-Collate:
- 'ACStdDev.annualized.R'
- 'CalmarRatio.Normalized.R'
- 'CDDopt.R'
- 'CDrawdown.R'
- 'chart.Autocorrelation.R'
- 'EmaxDDGBM.R'
- 'GLMSmoothIndex.R'
- 'maxDDGBM.R'
- 'na.skip.R'
- 'Return.GLM.R'
- 'table.ComparitiveReturn.GLM.R'
- 'table.normDD.R'
- 'table.UnsmoothReturn.R'
- 'UnsmoothReturn.R'
- 'AcarSim.R'
- 'CDD.Opt.R'
- 'CalmarRatio.Norm.R'
- 'SterlingRatio.Norm.R'
-
+Package: noniid.sm
+Type: Package
+Title: Non-i.i.d. GSoC 2013 Shubhankit
+Version: 0.1
+Date: $Date: 2013-05-13 14:30:22 -0500 (Mon, 13 May 2013) $
+Author: Shubhankit Mohan <shubhankit1 at gmail.com>
+Contributors: Peter Carl, Brian G. Peterson
+Depends:
+ xts,
+ PerformanceAnalytics
+Suggests:
+ PortfolioAnalytics
+Maintainer: Brian G. Peterson <brian at braverock.com>
+Description: GSoC 2013 project to replicate literature on drawdowns and
+ non-i.i.d assumptions in finance.
+License: GPL-3
+ByteCompile: TRUE
+Collate:
+ 'ACStdDev.annualized.R'
+ 'CalmarRatio.Normalized.R'
+ 'CDDopt.R'
+ 'CDrawdown.R'
+ 'chart.Autocorrelation.R'
+ 'EmaxDDGBM.R'
+ 'GLMSmoothIndex.R'
+ 'maxDDGBM.R'
+ 'na.skip.R'
+ 'Return.GLM.R'
+ 'table.ComparitiveReturn.GLM.R'
+ 'table.normDD.R'
+ 'table.UnsmoothReturn.R'
+ 'UnsmoothReturn.R'
+ 'AcarSim.R'
+ 'CDD.Opt.R'
+ 'CalmarRatio.Norm.R'
+ 'SterlingRatio.Norm.R'
+ 'LoSharpe.R'
+ 'Return.Okunev.R'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,12 +1,19 @@
-export(ACStdDev.annualized)
-export(CalmarRatio.Normalized)
-export(CDrawdown)
-export(chart.Autocorrelation)
-export(EMaxDDGBM)
-export(GLMSmoothIndex)
-export(QP.Norm)
-export(SterlingRatio.Normalized)
-export(table.ComparitiveReturn.GLM)
-export(table.EMaxDDGBM)
-export(table.NormDD)
-export(table.UnsmoothReturn)
+export(AcarSim)
+export(ACStdDev.annualized)
+export(CalmarRatio.Norm)
+export(CalmarRatio.Normalized)
+export(CDD.Opt)
+export(CDDOpt)
+export(CDrawdown)
+export(chart.Autocorrelation)
+export(EMaxDDGBM)
+export(GLMSmoothIndex)
+export(LoSharpe)
+export(QP.Norm)
+export(Return.Okunev)
+export(SterlingRatio.Norm)
+export(SterlingRatio.Normalized)
+export(table.ComparitiveReturn.GLM)
+export(table.EMaxDDGBM)
+export(table.NormDD)
+export(table.UnsmoothReturn)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,14 +1,14 @@
-#'@title Getmansky Lo Markov Smoothing Index Parameter
+#'@title GLM Index
#'@description
-#'A useful summary statistic for measuring the concentration of weights is
+#'Getmansky Lo Markov Smoothing Index is 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.
+#' \bold{ Herfindahl index}, a measure of the concentration of firms in a given industry.
#' The index is maximized when one coefficient is 1 and the rest are 0. In the context of
-#'smoothed returns, a lower value of x implies more smoothing, and the upper bound
-#'of 1 implies no smoothing, hence x is reffered as a ''smoothingindex' '.
-#'
-#' \deqn{ R_t = \mu + \beta \delta_t+ \xi_t}
+#'smoothed returns, a lower value implies more smoothing, and the upper bound
+#'of 1 implies no smoothing, hence \eqn{\xi} is reffered as a '\bold{smoothingindex}'.
+#'\deqn{ \xi = \sum\theta(j)^2}
+#'Where j belongs to 0 to k,which is the number of lag factors input.
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/LoSharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/LoSharpe.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/LoSharpe.R 2013-08-24 09:24:23 UTC (rev 2871)
@@ -0,0 +1,92 @@
+#'@title Andrew Lo Sharpe Ratio
+#'@description
+#' Although the Sharpe ratio has become part of the canon of modern financial
+#' analysis, its applications typically do not account for the fact that it is an
+#' estimated quantity, subject to estimation errors that can be substantial in
+#' some cases.
+#'
+#' Many studies have documented various violations of the assumption of
+#' IID returns for financial securities.
+#'
+#' Under the assumption of stationarity,a version of the Central Limit Theorem can
+#' still be applied to the estimator .
+#' @details
+#' The relationship between SR and SR(q) is somewhat more involved for non-
+#'IID returns because the variance of Rt(q) is not just the sum of the variances of component returns but also includes all the covariances. Specifically, under
+#' the assumption that returns \eqn{R_t} are stationary,
+#' \deqn{ Var[(R_t)] = \sum \sum Cov(R(t-i),R(t-j)) = q{\sigma^2} + 2{\sigma^2} \sum (q-k)\rho(k) }
+#' Where \eqn{ \rho(k) = Cov(R(t),R(t-k))/Var[(R_t)]} is the \eqn{k^{th}} order autocorrelation coefficient of the series of returns.This yields the following relationship between SR and SR(q):
+#' and i,j belongs to 0 to q-1
+#'\deqn{SR(q) = \eta(q) }
+#'Where :
+#' \deqn{ }{\eta(q) = [q]/[\sqrt(q\sigma^2) + 2\sigma^2 \sum(q-k)\rho(k)] }
+#' Where k belongs to 0 to q-1
+#' @param Ra an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' daily asset returns
+#' @param Rf an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' annualized Risk Free Rate
+#' @param q Number of autocorrelated lag periods. Taken as 3 (Default)
+#' @param \dots any other pass thru parameters
+#' @author Brian G. Peterson, Peter Carl, Shubhankit Mohan
+#' @references Getmansky, Mila, Lo, Andrew W. and Makarov, Igor,\emph{ An Econometric Model of Serial Correlation and Illiquidity in Hedge Fund Returns} (March 1, 2003). MIT Sloan Working Paper No. 4288-03; MIT Laboratory for Financial Engineering Working Paper No. LFE-1041A-03; EFMA 2003 Helsinki Meetings.
+#'\code{\link[stats]{}} \cr
+#' \url{http://ssrn.com/abstract=384700}
+#' @keywords ts multivariate distribution models non-iid
+#' @examples
+#'
+#' data(edhec)
+#' head(LoSharpe(edhec,0,3)
+#' @rdname LoSharpe
+#' @export
+LoSharpe <-
+ function (Ra,Rf = 0,q = 3, ...)
+ { # @author Brian G. Peterson, Peter Carl
+
+
+ # Function:
+ R = checkData(Ra, method="xts")
+ # Get dimensions and labels
+ columns.a = ncol(R)
+ columnnames.a = colnames(R)
+ # Time used for daily Return manipulations
+ Time= 252*nyears(edhec)
+ clean.lo <- function(column.R,q) {
+ # compute the lagged return series
+ gamma.k =matrix(0,q)
+ mu = sum(column.R)/(Time)
+ Rf= Rf/(Time)
+ for(i in 1:q){
+ lagR = lag(column.R, k=i)
+ # compute the Momentum Lagged Values
+ gamma.k[i]= (sum(((column.R-mu)*(lagR-mu)),na.rm=TRUE))
+ }
+ return(gamma.k)
+ }
+ neta.lo <- function(pho.k,q) {
+ # compute the lagged return series
+ sumq = 0
+ for(j in 1:q){
+ sumq = sumq+ (q-j)*pho.k[j]
+ }
+ return(q/(sqrt(q+2*sumq)))
+ }
+ for(column.a in 1:columns.a) { # for each asset passed in as R
+ # clean the data and get rid of NAs
+ mu = sum(R[,column.a])/(Time)
+ sig=sqrt(((R[,column.a]-mu)^2/(Time)))
+ pho.k = clean.lo(R[,column.a],q)/(as.numeric(sig[1]))
+ netaq=neta.lo(pho.k,q)
+ column.lo = (netaq*((mu-Rf)/as.numeric(sig[1])))
+
+ if(column.a == 1) { lo = column.lo }
+ else { lo = cbind (lo, column.lo) }
+
+ }
+ colnames(lo) = columnnames.a
+ rownames(lo)= paste("Lo Sharpe Ratio")
+ return(lo)
+
+
+ # RESULTS:
+
+ }
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R 2013-08-24 09:24:23 UTC (rev 2871)
@@ -0,0 +1,55 @@
+#'@title OW Return Model
+#'@description The objective is to determine the true underlying return by removing the
+#' autocorrelation structure in the original return series without making any assumptions
+#' regarding the actual time series properties of the underlying process. We are
+#' implicitly assuming by this approach that the autocorrelations that arise in reported
+#'returns are entirely due to the smoothing behavior funds engage in when reporting
+#' results. In fact, the method may be adopted to produce any desired
+#' level of autocorrelation at any lag and is not limited to simply eliminating all
+#'autocorrelations.It can be be said as the general form of Geltner Return Model
+#'@details dffd
+#' @references "Hedge Fund Risk Factors and Value at Risk of Credit
+#' Trading Strategies , John Okunev & Derek White
+#'
+#' @keywords ts multivariate distribution models
+#' @examples
+#'
+#' data(managers)
+#' head(Return.Okunev(managers[,1:3]),n=3)
+#'
+#'
+#' @export
+
+Return.Okunev<-function(R,q=3)
+{
+ column.okunev=R
+ column.okunev <- column.okunev[!is.na(column.okunev)]
+ for(i in 1:q)
+ {
+ lagR = lag(column.okunev, k=i)
+ column.okunev= (column.okunev-(lagR*quad(lagR,0)))/(1-quad(lagR,0))
+ }
+ return(c(column.okunev))
+}
+#' Recusrsive Okunev Call Function
+quad <- function(R,d)
+{
+ coeff = as.numeric(acf(as.numeric(edhec[,1]), plot = FALSE)[1:2][[1]])
+b=-(1+coeff[2]-2*d*coeff[1])
+c=(coeff[1]-d)
+ ans= (-b-sqrt(b*b-4*c*c))/(2*c)
+ #a <- a[!is.na(a)]
+ return(c(ans))
+}
+###############################################################################
+# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
+#
+# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: Return.Okunev.R 2163 2012-07-16 00:30:19Z braverock $
+#
+###############################################################################
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,25 +1,23 @@
-\name{table.EMaxDDGBM}
-\alias{table.EMaxDDGBM}
-\title{Expected Drawdown using Brownian Motion Assumptions}
-\usage{
- table.EMaxDDGBM(R, digits = 4)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-}
-\description{
- Works on the model specified by Maddon-Ismail which
- investigates the behavior of this statistic for a
- Brownian motion with drift.
-}
-\author{
- Peter Carl, Brian Peterson, Shubhankit Mohan
-}
-\keyword{Assumptions}
-\keyword{Brownian}
-\keyword{Drawdown}
-\keyword{Expected}
-\keyword{Motion}
-\keyword{Using}
-
+\name{EMaxDDGBM}
+\alias{EMaxDDGBM}
+\title{Expected Drawdown using Brownian Motion Assumptions}
+\usage{
+ EMaxDDGBM(R, digits = 4)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+}
+\description{
+ Works on the model specified by Maddon-Ismail
+}
+\author{
+ R
+}
+\keyword{Assumptions}
+\keyword{Brownian}
+\keyword{Drawdown}
+\keyword{Expected}
+\keyword{Motion}
+\keyword{Using}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,7 +1,7 @@
\name{GLMSmoothIndex}
\alias{GLMSmoothIndex}
\alias{Return.Geltner}
-\title{Getmansky Lo Markov Smoothing Index Parameter}
+\title{GLM Index}
\usage{
GLMSmoothIndex(R = NULL, ...)
}
@@ -10,18 +10,19 @@
or zoo object of asset returns}
}
\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. The index is maximized when one coefficient is
- 1 and the rest are 0. In the context of smoothed returns,
- a lower value of x implies more smoothing, and the upper
- bound of 1 implies no smoothing, hence x is reffered as a
- ''smoothingindex' '.
-
- \deqn{ R_t = \mu + \beta \delta_t+ \xi_t}
+ Getmansky Lo Markov Smoothing Index is 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 \bold{ Herfindahl index}, a measure of
+ the concentration of firms in a given industry. The index
+ is maximized when one coefficient is 1 and the rest are
+ 0. In the context of smoothed returns, a lower value
+ implies more smoothing, and the upper bound of 1 implies
+ no smoothing, hence \eqn{\xi} is reffered as a
+ '\bold{smoothingindex}'. \deqn{ \xi = \sum\theta(j)^2}
+ Where j belongs to 0 to k,which is the number of lag
+ factors input.
}
\examples{
data(edhec)
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -0,0 +1,70 @@
+\name{LoSharpe}
+\alias{LoSharpe}
+\title{Andrew Lo Sharpe Ratio}
+\usage{
+ LoSharpe(Ra, Rf = 0, q = 3, ...)
+}
+\arguments{
+ \item{Ra}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of daily asset returns}
+
+ \item{Rf}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of annualized Risk Free Rate}
+
+ \item{q}{Number of autocorrelated lag periods. Taken as 3
+ (Default)}
+
+ \item{\dots}{any other pass thru parameters}
+}
+\description{
+ Although the Sharpe ratio has become part of the canon of
+ modern financial analysis, its applications typically do
+ not account for the fact that it is an estimated
+ quantity, subject to estimation errors that can be
+ substantial in some cases.
+
+ Many studies have documented various violations of the
+ assumption of IID returns for financial securities.
+
+ Under the assumption of stationarity,a version of the
+ Central Limit Theorem can still be applied to the
+ estimator .
+}
+\details{
+ The relationship between SR and SR(q) is somewhat more
+ involved for non- IID returns because the variance of
+ Rt(q) is not just the sum of the variances of component
+ returns but also includes all the covariances.
+ Specifically, under the assumption that returns \eqn{R_t}
+ are stationary, \deqn{ Var[(R_t)] = \sum \sum
+ Cov(R(t-i),R(t-j)) = q{\sigma^2} + 2{\sigma^2} \sum
+ (q-k)\rho(k) } Where \eqn{ \rho(k) =
+ Cov(R(t),R(t-k))/Var[(R_t)]} is the \eqn{k^{th}} order
+ autocorrelation coefficient of the series of returns.This
+ yields the following relationship between SR and SR(q):
+ and i,j belongs to 0 to q-1 \deqn{SR(q) = \eta(q) } Where
+ : \deqn{ }{\eta(q) = [q]/[\sqrt(q\sigma^2) + 2\sigma^2
+ \sum(q-k)\rho(k)] } Where k belongs to 0 to q-1
+}
+\examples{
+data(edhec)
+head(LoSharpe(edhec,0,3)
+}
+\author{
+ Brian G. Peterson, Peter Carl, Shubhankit Mohan
+}
+\references{
+ Getmansky, Mila, Lo, Andrew W. and Makarov, Igor,\emph{
+ An Econometric Model of Serial Correlation and
+ Illiquidity in Hedge Fund Returns} (March 1, 2003). MIT
+ Sloan Working Paper No. 4288-03; MIT Laboratory for
+ Financial Engineering Working Paper No. LFE-1041A-03;
+ EFMA 2003 Helsinki Meetings. \code{\link[stats]{}} \cr
+ \url{http://ssrn.com/abstract=384700}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{non-iid}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-24 00:07:51 UTC (rev 2870)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -1,47 +1,47 @@
-\name{Return.GLM}
-\alias{Return.GLM}
-\title{GLM Return Model}
-\usage{
- Return.GLM(edhec,4)
-}
-\arguments{
- \item{Ra}{: an xts, vector, matrix, data frame,
- timeSeries or zoo object of asset returns}
-
- \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. 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 profile
- as well as a smoothing-adjusted Sharpe ratio.
-}
-\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).
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{Return.GLM}
+\alias{Return.GLM}
+\title{GLM Return Model}
+\usage{
+ Return.GLM(edhec,4)
+}
+\arguments{
+ \item{Ra}{: an xts, vector, matrix, data frame,
+ timeSeries or zoo object of asset returns}
+
+ \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. 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
+ profile as well as a smoothing-adjusted Sharpe ratio.
+}
+\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).
+}
+\keyword{distribution}
+\keyword{model}
+\keyword{multivariate}
+\keyword{ts}
+
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -0,0 +1,36 @@
+\name{Return.Okunev}
+\alias{Return.Okunev}
+\title{OW Return Model}
+\usage{
+ Return.Okunev(R, q = 3)
+}
+\description{
+ The objective is to determine the true underlying return
+ by removing the autocorrelation structure in the original
+ return series without making any assumptions regarding
+ the actual time series properties of the underlying
+ process. We are implicitly assuming by this approach that
+ the autocorrelations that arise in reported returns are
+ entirely due to the smoothing behavior funds engage in
+ when reporting results. In fact, the method may be
+ adopted to produce any desired level of autocorrelation
+ at any lag and is not limited to simply eliminating all
+ autocorrelations.It can be be said as the general form of
+ Geltner Return Model
+}
+\details{
+ dffd
+}
+\examples{
+data(managers)
+head(Return.Okunev(managers[,1:3]),n=3)
+}
+\references{
+ "Hedge Fund Risk Factors and Value at Risk of Credit
+ Trading Strategies , John Okunev & Derek White
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/quad.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/quad.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/quad.Rd 2013-08-24 09:24:23 UTC (rev 2871)
@@ -0,0 +1,10 @@
+\name{quad}
+\alias{quad}
+\title{Recusrsive Okunev Call Function}
+\usage{
+ quad(R, d)
+}
+\description{
+ Recusrsive Okunev Call Function
+}
+
More information about the Returnanalytics-commits
mailing list