[Returnanalytics-commits] r2949 - in pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 31 15:30:19 CEST 2013
Author: braverock
Date: 2013-08-31 15:30:19 +0200 (Sat, 31 Aug 2013)
New Revision: 2949
Removed:
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/inst/
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/man/
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/inst/
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/noniid.sm-package.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/quad.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/table.EmaxDDGBM.Rd
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/ACStdDev.annualized.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/QP.Norm.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.Okunev.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/SterlingRatio.Norm.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/ACStdDev.annualized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.GLM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.Okunev.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/SterlingRatio.Norm.Rd
Log:
- changes to get clean package build, clean doc build, and cleaner R CMD check
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,38 +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,
- tseries,
- stats
-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:
- 'AcarSim.R'
- 'ACStdDev.annualized.R'
- 'CalmarRatio.Norm.R'
- 'CDrawdown.R'
- 'chart.AcarSim.R'
- 'chart.Autocorrelation.R'
- 'GLMSmoothIndex.R'
- 'LoSharpe.R'
- 'na.skip.R'
- 'noniid.sm-internal.R'
- 'Return.GLM.R'
- 'Return.Okunev.R'
- 'se.LoSharpe.R'
- 'SterlingRatio.Norm.R'
- 'table.ComparitiveReturn.GLM.R'
- 'table.UnsmoothReturn.R'
- 'UnsmoothReturn.R'
- 'EMaxDDGBM.R'
- 'table.EMaxDDGBM.R'
- 'QP.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,
+ tseries,
+ stats
+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:
+ 'AcarSim.R'
+ 'ACStdDev.annualized.R'
+ 'CalmarRatio.Norm.R'
+ 'CDrawdown.R'
+ 'chart.AcarSim.R'
+ 'chart.Autocorrelation.R'
+ 'EmaxDDGBM.R'
+ 'GLMSmoothIndex.R'
+ 'LoSharpe.R'
+ 'na.skip.R'
+ 'noniid.sm-internal.R'
+ 'QP.Norm.R'
+ 'Return.GLM.R'
+ 'Return.Okunev.R'
+ 'se.LoSharpe.R'
+ 'SterlingRatio.Norm.R'
+ 'table.ComparitiveReturn.GLM.R'
+ 'table.EMaxDDGBM.R'
+ 'table.UnsmoothReturn.R'
+ 'UnsmoothReturn.R'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,16 +1,17 @@
-export(AcarSim)
-export(ACStdDev.annualized)
-export(CalmarRatio.Norm)
-export(CDrawdown)
-export(chart.AcarSim)
-export(chart.Autocorrelation)
-export(EMaxDDGBM)
-export(GLMSmoothIndex)
-export(LoSharpe)
-export(Return.GLM)
-export(Return.Okunev)
-export(se.LoSharpe)
-export(SterlingRatio.Norm)
-export(table.ComparitiveReturn.GLM)
-export(table.EMaxDDGBM)
-export(table.UnsmoothReturn)
+export(AcarSim)
+export(ACStdDev.annualized)
+export(CalmarRatio.Norm)
+export(CDrawdown)
+export(chart.AcarSim)
+export(chart.Autocorrelation)
+export(EMaxDDGBM)
+export(GLMSmoothIndex)
+export(LoSharpe)
+export(QP.Norm)
+export(Return.GLM)
+export(Return.Okunev)
+export(se.LoSharpe)
+export(SterlingRatio.Norm)
+export(table.ComparitiveReturn.GLM)
+export(table.EMaxDDGBM)
+export(table.UnsmoothReturn)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/ACStdDev.annualized.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/ACStdDev.annualized.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/ACStdDev.annualized.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -18,6 +18,7 @@
#' @keywords ts multivariate distribution models
#' @examples
#' library(PerformanceAnalytics)
+#' data(edhec)
#' ACStdDev.annualized(edhec,3)
#'
#' @export
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -34,7 +34,7 @@
#' @examples
#'
#' data(edhec)
-#' head(LoSharpe(edhec,0,3)
+#' head(LoSharpe(edhec,0,3))
#' @rdname LoSharpe
#' @export
LoSharpe <-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/QP.Norm.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/QP.Norm.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/QP.Norm.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,50 +1,11 @@
-#' calculate a Normalized Calmar or Sterling reward/risk ratio
-#'
-#' Normalized Calmar and Sterling Ratios are yet another method of creating a
-#' risk-adjusted measure for ranking investments similar to the
-#' \code{\link{SharpeRatio}}.
-#'
-#' Both the Normalized Calmar and the Sterling ratio are the ratio of annualized return
-#' over the absolute value of the maximum drawdown of an investment. The
-#' Sterling ratio adds an excess risk measure to the maximum drawdown,
-#' traditionally and defaulting to 10\%.
-#'
-#' It is also traditional to use a three year return series for these
-#' calculations, although the functions included here make no effort to
-#' determine the length of your series. If you want to use a subset of your
-#' series, you'll need to truncate or subset the input data to the desired
-#' length.
-#'
-#' Many other measures have been proposed to do similar reward to risk ranking.
-#' It is the opinion of this author that newer measures such as Sortino's
-#' \code{\link{UpsidePotentialRatio}} or Favre's modified
-#' \code{\link{SharpeRatio}} are both \dQuote{better} measures, and
-#' should be preferred to the Calmar or Sterling Ratio.
-#'
-#' @aliases Normalized.CalmarRatio Normalized.SterlingRatio
+#' QP function for calculation of Sharpe Ratio
+#'
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @param tau Time Scale Translations Factor
#' @param scale number of periods in a year (daily scale = 252, monthly scale =
-#' 12, quarterly scale = 4)
-#' @author Shubhankit
#' @seealso
-#' \code{\link{Return.annualized}}, \cr
-#' \code{\link{maxDrawdown}}, \cr
-#' \code{\link{SharpeRatio.modified}}, \cr
-#' \code{\link{UpsidePotentialRatio}}
-#' @references Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya, Maximum drawdown. Risk Magazine, 01 Oct 2004.
-#' @keywords ts multivariate distribution models
-#' @examples
-#'
-#' data(managers)
-#' Normalized.CalmarRatio(managers[,1,drop=FALSE])
-#' Normalized.CalmarRatio(managers[,1:6])
-#' Normalized.SterlingRatio(managers[,1,drop=FALSE])
-#' Normalized.SterlingRatio(managers[,1:6])
-#'
-#' @rdname QP.Norm
-#' QP function fo calculation of Sharpe Ratio
+#' \code{\link{CalmarRatio.Norm}}, \cr
#' @export
QP.Norm <- function (R, tau,scale = NA)
{
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.GLM.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.GLM.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,11 +1,12 @@
-#' @title GLM Return Model
-#' @description True returns represent the flow of information that would determine the equilibrium
+#' GLM Return Model
+#'
+#' 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 smoothing and \emph{develop estimators for the smoothing
#' profile as well as a smoothing-adjusted Sharpe ratio}.
#' @examples
-#' library(PerformanceAnalytics)
+#' data(edhec)
#' Return.GLM(edhec,4)
#' @param
#' Ra : an xts, vector, matrix, data frame, timeSeries or zoo object of
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.Okunev.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.Okunev.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/Return.Okunev.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,21 +1,38 @@
-#'@title OW Return Model
-#'@description The objective is to determine the true underlying return by removing the
+#' Okunev and White Return Model
+#'
+#' 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
+#' 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
+#' autocorrelations.It can be be said as the general form of Geltner Return Model
+#'
#'@details
-#'Given a sample of historical returns \eqn{R(1),R(2), . . .,R(T)},the method assumes the fund manager smooths returns in the following manner:
+#' Given a sample of historical returns \eqn{R(1),R(2), . . .,R(T)},
+#' the method assumes the fund manager smooths returns in the following manner:
#' \deqn{ r(0,t) = \sum \beta (i) r(0,t-i) + (1- \alpha)r(m,t) }
#' Where :\deqn{ \sum \beta (i) = (1- \alpha) }
#' \bold{r(0,t)} : is the observed (reported) return at time t (with 0 adjustments to reported returns),
-#'\bold{r(m,t)} : is the true underlying (unreported) return at time t (determined by making m adjustments to reported returns).
+#' \bold{r(m,t)} : is the true underlying (unreported) return at
+#' time t (determined by making m adjustments to reported returns).
#'
-#'To remove the \bold{first m orders} of autocorrelation from a given return series we would proceed in a manner very similar to that detailed in \bold{ \code{\link{Return.Geltner}} \cr}. We would initially remove the first order autocorrelation, then proceed to eliminate the second order autocorrelation through the iteration process. In general, to remove any order, m, autocorrelations from a given return series we would make the following transformation to returns:
-#' 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.
+#' To remove the \bold{first m orders} of autocorrelation from a given return
+#' series we would proceed in a manner very similar to that detailed in
+#' \bold{\code{\link{Return.Geltner}} \cr}. We would initially remove the first order
+#' autocorrelation, then proceed to eliminate the second order autocorrelation
+#' through the iteration process. In general, to remove any order, m,
+#' autocorrelations from a given return series we would make the following
+#' transformation to returns:
+#' 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.
+#'
#' @param
#' R : an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
@@ -32,7 +49,7 @@
#' @examples
#'
#' data(managers)
-#' head(Return.Okunev(managers[,1:3]),n=3)
+#' Return.Okunev(managers)
#'
#'
#' @export
@@ -47,35 +64,8 @@
}
return(c(column.okunev))
}
-#'@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
-#'Given a sample of historical returns \eqn{R(1),R(2), . . .,R(T)},the method assumes the fund manager smooths returns in the following manner:
-#' \deqn{ r(0,t) = \sum \beta (i) r(0,t-i) + (1- \alpha)r(m,t) }
-#' Where :\deqn{ \sum \beta (i) = (1- \alpha) }
-#' \bold{r(0,t)} : is the observed (reported) return at time t (with 0 adjustments to reported returns),
-#'\bold{r(m,t)} : is the true underlying (unreported) return at time t (determined by making m adjustments to reported returns).
-#'
-#'To remove the \bold{first m orders} of autocorrelation from a given return series we would proceed in a manner very similar to that detailed in \bold{ \code{\link{Return.Geltner}} \cr}. We would initially remove the first order autocorrelation, then proceed to eliminate the second order autocorrelation through the iteration process. In general, to remove any order, m, autocorrelations from a given return series we would make the following transformation to returns:
-#' 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.
-#' @param
-#' R : an xts column of
-#' asset returns
-#' @param
-#' d : order of autocorrelation coefficient lag factors
-#' @references Okunev, John and White, Derek R., \emph{ Hedge Fund Risk Factors and Value at Risk of Credit Trading Strategies} (October 2003).
-#' Available at SSRN: \url{http://ssrn.com/abstract=460641}
-#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
-#' @seealso \code{\link{Return.Geltner}} \cr
-#' @keywords ts multivariate distribution models
-#' @export
+
+#helper function for Return.Okunev, not exported
quad <- function(R,d)
{
coeff = as.numeric(acf(as.numeric(R, plot = FALSE)[1:2][[1]]))
@@ -85,6 +75,7 @@
#a <- a[!is.na(a)]
return(c(ans))
}
+
###############################################################################
# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
#
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/SterlingRatio.Norm.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/SterlingRatio.Norm.R 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/SterlingRatio.Norm.R 2013-08-31 13:30:19 UTC (rev 2949)
@@ -9,7 +9,7 @@
#' Sterling ratio adds an \bold{excess risk} measure to the maximum drawdown,
#' traditionally and defaulting to 10\%.
#'
-#' \deqn{Sterling Ratio = [Return over (0,T)]/[max Drawdown(0,T) - 10%]}
+#' \deqn{Sterling Ratio = [Return over (0,T)]/[max Drawdown(0,T) - 10\%]}
#' It is also \emph{traditional} to use a three year return series for these
#' calculations, although the functions included here make no effort to
#' determine the length of your series. If you want to use a subset of your
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/ACStdDev.annualized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/ACStdDev.annualized.Rd 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/ACStdDev.annualized.Rd 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,52 +1,53 @@
-\name{ACStdDev.annualized}
-\alias{ACStdDev.annualized}
-\alias{sd.annualized}
-\alias{sd.multiperiod}
-\alias{StdDev.annualized}
-\title{Autocorrleation adjusted Standard Deviation}
-\usage{
- ACStdDev.annualized(R, lag = 6, scale = NA, ...)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-
- \item{lag}{: number of autocorrelated lag factors
- inputted by user}
-
- \item{scale}{number of periods in a year (daily scale =
- 252, monthly scale = 12, quarterly scale = 4)}
-
- \item{\dots}{any other passthru parameters}
-}
-\description{
- Incorporating the component of lagged autocorrelation
- factor into adjusted time scale standard deviation
- translation
-}
-\details{
- Given a sample of historical returns R(1),R(2), . .
- .,R(T),the method assumes the fund manager smooths
- returns in the following manner, when 't' is the unit
- time interval: The square root time translation can be
- defined as : \deqn{ \sigma(T) = T \sqrt\sigma(t)}
-}
-\examples{
-library(PerformanceAnalytics)
-ACStdDev.annualized(edhec,3)
-}
-\author{
- Peter Carl,Brian Peterson, Shubhankit Mohan
- \url{http://en.wikipedia.org/wiki/Volatility_(finance)}
-}
-\references{
- Burghardt, G., and L. Liu, \emph{ It's the
- Autocorrelation, Stupid (November 2012) Newedge working
- paper.}
- \url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{ACStdDev.annualized}
+\alias{ACStdDev.annualized}
+\alias{sd.annualized}
+\alias{sd.multiperiod}
+\alias{StdDev.annualized}
+\title{Autocorrleation adjusted Standard Deviation}
+\usage{
+ ACStdDev.annualized(R, lag = 6, scale = NA, ...)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+
+ \item{lag}{: number of autocorrelated lag factors
+ inputted by user}
+
+ \item{scale}{number of periods in a year (daily scale =
+ 252, monthly scale = 12, quarterly scale = 4)}
+
+ \item{\dots}{any other passthru parameters}
+}
+\description{
+ Incorporating the component of lagged autocorrelation
+ factor into adjusted time scale standard deviation
+ translation
+}
+\details{
+ Given a sample of historical returns R(1),R(2), . .
+ .,R(T),the method assumes the fund manager smooths
+ returns in the following manner, when 't' is the unit
+ time interval: The square root time translation can be
+ defined as : \deqn{ \sigma(T) = T \sqrt\sigma(t)}
+}
+\examples{
+library(PerformanceAnalytics)
+data(edhec)
+ACStdDev.annualized(edhec,3)
+}
+\author{
+ Peter Carl,Brian Peterson, Shubhankit Mohan
+ \url{http://en.wikipedia.org/wiki/Volatility_(finance)}
+}
+\references{
+ Burghardt, G., and L. Liu, \emph{ It's the
+ Autocorrelation, Stupid (November 2012) Newedge working
+ paper.}
+ \url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,70 +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.
- \url{http://ssrn.com/abstract=384700}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{non-iid}
-\keyword{ts}
-
+\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.
+ \url{http://ssrn.com/abstract=384700}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{non-iid}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.GLM.Rd 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.GLM.Rd 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,66 +1,66 @@
-\name{Return.GLM}
-\alias{Return.GLM}
-\title{GLM Return Model}
-\usage{
- Return.GLM(Ra, q = 3)
-}
-\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
- smoothing and \emph{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. \deqn{\theta (j)
- \epsilon [0,1] where : j = 0,1,....,k } and, \deqn{\theta
- _1 + \theta _2 + \theta _3 \cdots + \theta _k = 1} Using
- the methods outlined above , the paper estimates the
- smoothing model using maximumlikelihood
- procedure-programmed in Matlab using the Optimization
- Toolbox andreplicated in Stata usingits MA(k) estimation
- routine.Using Time seseries analysis and computational
- finance("\bold{tseries}") library , we fit an it an
- \bold{ARMA} model to a univariate time series by
- conditional least squares. For exact maximum likelihood
- estimation,arima0 from package \bold{stats} can be used.
-}
-\examples{
-library(PerformanceAnalytics)
-Return.GLM(edhec,4)
-}
-\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).\url{
- http://ssrn.com/abstract=384700}
-}
-\seealso{
- Return.Geltner
-}
-\keyword{distribution}
-\keyword{model}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{Return.GLM}
+\alias{Return.GLM}
+\title{GLM Return Model}
+\usage{
+ Return.GLM(Ra, q = 3)
+}
+\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
+ smoothing and \emph{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. \deqn{\theta (j)
+ \epsilon [0,1] where : j = 0,1,....,k } and, \deqn{\theta
+ _1 + \theta _2 + \theta _3 \cdots + \theta _k = 1} Using
+ the methods outlined above , the paper estimates the
+ smoothing model using maximumlikelihood
+ procedure-programmed in Matlab using the Optimization
+ Toolbox andreplicated in Stata usingits MA(k) estimation
+ routine.Using Time seseries analysis and computational
+ finance("\bold{tseries}") library , we fit an it an
+ \bold{ARMA} model to a univariate time series by
+ conditional least squares. For exact maximum likelihood
+ estimation,arima0 from package \bold{stats} can be used.
+}
+\examples{
+data(edhec)
+Return.GLM(edhec,4)
+}
+\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).\url{
+ http://ssrn.com/abstract=384700}
+}
+\seealso{
+ Return.Geltner
+}
+\keyword{distribution}
+\keyword{model}
+\keyword{multivariate}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.Okunev.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.Okunev.Rd 2013-08-31 10:39:53 UTC (rev 2948)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/Return.Okunev.Rd 2013-08-31 13:30:19 UTC (rev 2949)
@@ -1,78 +1,79 @@
-\name{Return.Okunev}
-\alias{Return.Okunev}
-\title{OW Return Model}
-\usage{
- Return.Okunev(R, q = 3)
-}
-\arguments{
- \item{R}{: an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-
- \item{q}{: order of autocorrelation coefficient lag
- factors}
-}
-\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{
- Given a sample of historical returns \eqn{R(1),R(2), . .
- .,R(T)},the method assumes the fund manager smooths
- returns in the following manner: \deqn{ r(0,t) = \sum
- \beta (i) r(0,t-i) + (1- \alpha)r(m,t) } Where :\deqn{
- \sum \beta (i) = (1- \alpha) } \bold{r(0,t)} : is the
- observed (reported) return at time t (with 0 adjustments
- to reported returns), \bold{r(m,t)} : is the true
- underlying (unreported) return at time t (determined by
- making m adjustments to reported returns).
-
- To remove the \bold{first m orders} of autocorrelation
- from a given return series we would proceed in a manner
- very similar to that detailed in \bold{
- \code{\link{Return.Geltner}} \cr}. We would initially
- remove the first order autocorrelation, then proceed to
- eliminate the second order autocorrelation through the
- iteration process. In general, to remove any order, m,
- autocorrelations from a given return series we would make
- the following transformation to returns: 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.
-}
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 2949
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