[Returnanalytics-commits] r2872 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 24 14:19:30 CEST 2013
Author: shubhanm
Date: 2013-08-24 14:19:30 +0200 (Sat, 24 Aug 2013)
New Revision: 2872
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/ACStdDev.annualized.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/SterlingRatio.Norm.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/SterlingRatio.Norm.Rd
Log:
/.Rd file detailed info
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-24 12:19:30 UTC (rev 2872)
@@ -10,6 +10,7 @@
export(GLMSmoothIndex)
export(LoSharpe)
export(QP.Norm)
+export(Return.GLM)
export(Return.Okunev)
export(SterlingRatio.Norm)
export(SterlingRatio.Normalized)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/ACStdDev.annualized.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/ACStdDev.annualized.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/ACStdDev.annualized.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,5 +1,8 @@
#' @title Autocorrleation adjusted Standard Deviation
#' @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)}
#' @aliases sd.multiperiod sd.annualized StdDev.annualized
#' @param x an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
@@ -8,8 +11,8 @@
#' 12, quarterly scale = 4)
#' @param \dots any other passthru parameters
#' @author Peter Carl,Brian Peterson, Shubhankit Mohan
-#' @seealso \code{\link[stats]{sd}} \cr
-#' \url{http://wikipedia.org/wiki/inverse-square_law}
+#' @seealso \code{\link[stats]{sd}} \cr \code{\link[stats]{stdDev.annualized}} \cr
+#' \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.}
#' \code{\link[stats]{}} \cr
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,14 +1,17 @@
-#' @title Normalized Calmar reward/risk ratio
+#' @title Normalized Calmar ratio
#'
#' @description Normalized Calmar and Sterling Ratios are yet another method of creating a
#' risk-adjusted measure for ranking investments similar to the Sharpe Ratio.
#'
#' @details
#' 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%.
-#'
+#' over the absolute value of the maximum drawdown of an investment.
+#' \deqn{Sterling Ratio = [Return over (0,T)]/[max Drawdown(0,T)]}
+#' 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
+#' series, you'll need to truncate or subset the input data to the desired
+#' length.
#' 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
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/GLMSmoothIndex.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -13,8 +13,7 @@
#' asset returns
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
#' @aliases Return.Geltner
-#' @references "An econometric model of serial correlation and illiquidity in
-#' hedge fund returns" Mila Getmansky, Andrew W. Lo, Igor Makarov
+#' @references \emph{Getmansky, Mila, Lo, Andrew W. and Makarov, Igor} 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. Available at SSRN: \url{http://ssrn.com/abstract=384700}
#'
#' @keywords ts multivariate distribution models non-iid
#' @examples
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.GLM.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -2,8 +2,8 @@
#' @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.
+#' 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}.
#' @usage
#' Return.GLM(edhec,4)
#' @usage
@@ -19,11 +19,19 @@
#' 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.
+#' 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.
+#'
#' @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).
+#' and illiquidity in hedge fund Returns},Journal of Financial Economics 74 (2004).\url{ http://ssrn.com/abstract=384700}
#' @keywords ts multivariate distribution model
+#' @seealso Return.Geltner
+#' @export
Return.GLM <-
function (Ra,q=3)
{ # @author Brian G. Peterson, Peter Carl
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/Return.Okunev.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -7,10 +7,21 @@
#' 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
-#'
+#'@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.
+#'
+#'
+#' @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
#' @examples
#'
@@ -19,7 +30,6 @@
#'
#'
#' @export
-
Return.Okunev<-function(R,q=3)
{
column.okunev=R
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/SterlingRatio.Norm.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/SterlingRatio.Norm.R 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/SterlingRatio.Norm.R 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,21 +1,21 @@
-#' @title Normalized Sterling reward/risk ratio
+#' @title Normalized Sterling Ratio
#'
-#' @description Normalized Sterling and Sterling Ratios are yet another method of creating a
+#' @description Normalized Sterling Ratio is another method of creating a
#' risk-adjusted measure for ranking investments similar to the Sharpe Ratio.
#'
#' @details
#' Both the Normalized Sterling and the Calmar 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%.
+#' Sterling ratio adds an \bold{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
+#' \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
#' series, you'll need to truncate or subset the input data to the desired
#' length.
-#'
-#'
+#' Malik Magdon-Ismail impmemented a sclaing law for different \eqn{\mu ,\sigma and T}
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @param scale number of periods in a year (daily scale = 252, monthly scale =
@@ -23,7 +23,8 @@
#' @param excess for Sterling Ratio, excess amount to add to the max drawdown,
#' traditionally and default .1 (10\%)
#' @author Brian G. Peterson , Peter Carl , Shubhankit Mohan
-#' @references Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya, Maximum drawdown. Risk Magazine, 01 Oct 2004.
+#' @references Bacon, Carl, Magdon-Ismail, M. and Amir Atiya,\emph{ Maximum drawdown. Risk Magazine,} 01 Oct 2004.
+#' \url{http://www.cs.rpi.edu/~magdon/talks/mdd_NYU04.pdf}
#' @keywords ts multivariate distribution models
#' @examples
#'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -24,6 +24,13 @@
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)}
+}
\author{
Peter Carl,Brian Peterson, Shubhankit Mohan
}
@@ -35,7 +42,8 @@
}
\seealso{
\code{\link[stats]{sd}} \cr
- \url{http://wikipedia.org/wiki/inverse-square_law}
+ \code{\link[stats]{stdDev.annualized}} \cr
+ \url{http://en.wikipedia.org/wiki/Volatility_(finance)}
}
\keyword{distribution}
\keyword{models}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,6 +1,6 @@
\name{CalmarRatio.Norm}
\alias{CalmarRatio.Norm}
-\title{Normalized Calmar reward/risk ratio}
+\title{Normalized Calmar ratio}
\usage{
CalmarRatio.Norm(R, tau = 1, scale = NA)
}
@@ -22,14 +22,17 @@
\details{
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,
+ maximum drawdown of an investment. \deqn{Sterling Ratio =
+ [Return over (0,T)]/[max Drawdown(0,T)]} 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 series, you'll need
+ to truncate or subset the input data to the desired
+ length. 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.
}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,77 +1,77 @@
-\name{QP.Norm}
-\alias{Normalized.CalmarRatio}
-\alias{Normalized.SterlingRatio}
-\alias{QP.Norm}
-\alias{SterlingRatio.Normalized}
-\title{QP function fo calculation of Sharpe Ratio}
-\usage{
- QP.Norm(R, tau, scale = NA)
-
- SterlingRatio.Normalized(R, tau = 1, scale = NA,
- excess = 0.1)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-
- \item{scale}{number of periods in a year (daily scale =
- 252, monthly scale = 12, quarterly scale = 4)}
-
- \item{excess}{for Sterling Ratio, excess amount to add to
- the max drawdown, traditionally and default .1 (10\%)}
-}
-\description{
- calculate a Normalized Calmar or Sterling reward/risk
- ratio
-}
-\details{
- 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.
-}
-\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])
-}
-\author{
- Brian G. Peterson
-}
-\references{
- Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya,
- Maximum drawdown. Risk Magazine, 01 Oct 2004.
-}
-\seealso{
- \code{\link{Return.annualized}}, \cr
- \code{\link{maxDrawdown}}, \cr
- \code{\link{SharpeRatio.modified}}, \cr
- \code{\link{UpsidePotentialRatio}}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{QP.Norm}
+\alias{Normalized.CalmarRatio}
+\alias{Normalized.SterlingRatio}
+\alias{QP.Norm}
+\alias{SterlingRatio.Normalized}
+\title{QP function fo calculation of Sharpe Ratio}
+\usage{
+ QP.Norm(R, tau, scale = NA)
+
+ SterlingRatio.Normalized(R, tau = 1, scale = NA,
+ excess = 0.1)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+
+ \item{scale}{number of periods in a year (daily scale =
+ 252, monthly scale = 12, quarterly scale = 4)}
+
+ \item{excess}{for Sterling Ratio, excess amount to add to
+ the max drawdown, traditionally and default .1 (10\%)}
+}
+\description{
+ calculate a Normalized Calmar or Sterling reward/risk
+ ratio
+}
+\details{
+ 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.
+}
+\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])
+}
+\author{
+ Brian G. Peterson
+}
+\references{
+ Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya,
+ Maximum drawdown. Risk Magazine, 01 Oct 2004.
+}
+\seealso{
+ \code{\link{Return.annualized}}, \cr
+ \code{\link{maxDrawdown}}, \cr
+ \code{\link{SharpeRatio.modified}}, \cr
+ \code{\link{UpsidePotentialRatio}}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -32,9 +32,13 @@
Peter Carl, Brian Peterson, Shubhankit Mohan
}
\references{
- "An econometric model of serial correlation and
- illiquidity in hedge fund returns" Mila Getmansky, Andrew
- W. Lo, Igor Makarov
+ \emph{Getmansky, Mila, Lo, Andrew W. and Makarov, Igor}
+ 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. Available at SSRN:
+ \url{http://ssrn.com/abstract=384700}
}
\keyword{distribution}
\keyword{models}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.GLM.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -18,8 +18,8 @@
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.
+ 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
@@ -27,9 +27,20 @@
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
+ \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.
+ 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.
}
\author{
Brian Peterson,Peter Carl, Shubhankit Mohan
@@ -38,8 +49,12 @@
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).
+ Economics 74 (2004).\url{
+ http://ssrn.com/abstract=384700}
}
+\seealso{
+ Return.Geltner
+}
\keyword{distribution}
\keyword{model}
\keyword{multivariate}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -19,16 +19,51 @@
Geltner Return Model
}
\details{
- dffd
+ 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.
}
\examples{
data(managers)
head(Return.Okunev(managers[,1:3]),n=3)
}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
\references{
- "Hedge Fund Risk Factors and Value at Risk of Credit
- Trading Strategies , John Okunev & Derek White
+ 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}
}
+\seealso{
+ \code{\link{Return.Geltner}} \cr
+}
\keyword{distribution}
\keyword{models}
\keyword{multivariate}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/SterlingRatio.Norm.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/SterlingRatio.Norm.Rd 2013-08-24 09:24:23 UTC (rev 2871)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/SterlingRatio.Norm.Rd 2013-08-24 12:19:30 UTC (rev 2872)
@@ -1,6 +1,6 @@
\name{SterlingRatio.Norm}
\alias{SterlingRatio.Norm}
-\title{Normalized Sterling reward/risk ratio}
+\title{Normalized Sterling Ratio}
\usage{
SterlingRatio.Norm(R, tau = 1, scale = NA, excess = 0.1)
}
@@ -15,23 +15,26 @@
the max drawdown, traditionally and default .1 (10\%)}
}
\description{
- Normalized Sterling and Sterling Ratios are yet another
- method of creating a risk-adjusted measure for ranking
- investments similar to the Sharpe Ratio.
+ Normalized Sterling Ratio is another method of creating a
+ risk-adjusted measure for ranking investments similar to
+ the Sharpe Ratio.
}
\details{
Both the Normalized Sterling and the Calmar 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%.
+ adds an \bold{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.
+ \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 series, you'll need to truncate or
+ subset the input data to the desired length. Malik
+ Magdon-Ismail impmemented a sclaing law for different
+ \eqn{\mu ,\sigma and T}
}
\examples{
data(managers)
@@ -42,8 +45,9 @@
Brian G. Peterson , Peter Carl , Shubhankit Mohan
}
\references{
- Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya,
- Maximum drawdown. Risk Magazine, 01 Oct 2004.
+ Bacon, Carl, Magdon-Ismail, M. and Amir Atiya,\emph{
+ Maximum drawdown. Risk Magazine,} 01 Oct 2004.
+ \url{http://www.cs.rpi.edu/~magdon/talks/mdd_NYU04.pdf}
}
\keyword{distribution}
\keyword{models}
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