[Returnanalytics-commits] r2986 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: man noniid.sm noniid.sm/R noniid.sm/man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Sep 4 14:57:34 CEST 2013
Author: shubhanm
Date: 2013-09-04 14:57:34 +0200 (Wed, 04 Sep 2013)
New Revision: 2986
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd
Log:
Documentation update and parameter change in AcarSim
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-09-04 10:49:08 UTC (rev 2985)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-09-04 12:57:34 UTC (rev 2986)
@@ -1,48 +1,50 @@
-\name{GLMSmoothIndex}
-\alias{GLMSmoothIndex}
-\alias{Return.Geltner}
-\title{GLM Index}
-\usage{
- GLMSmoothIndex(R = NULL, ...)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-}
-\description{
- 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)
-head(GLMSmoothIndex(edhec))
-}
-\author{
- Peter Carl, Brian Peterson, Shubhankit Mohan
-}
-\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}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{non-iid}
-\keyword{ts}
-
+\name{GLMSmoothIndex}
+\alias{GLMSmoothIndex}
+\alias{Return.Geltner}
+\title{GLM Index}
+\usage{
+ GLMSmoothIndex(R = NULL, ...)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+}
+\description{
+ 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{
+require(PerformanceAnalytics)
+ library(PerformanceAnalytics)
+ data(edhec)
+GLMSmoothIndex(edhec)
+}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
+\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}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{non-iid}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd 2013-09-04 10:49:08 UTC (rev 2985)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Return.Okunev.Rd 2013-09-04 12:57:34 UTC (rev 2986)
@@ -1,71 +1,78 @@
-\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{
- 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{
- 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}
-\keyword{ts}
-
+\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.
+}
+\examples{
+data(managers)
+head(Return.Okunev(managers[,1:3]),n=3)
+}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
+\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}
+}
+\seealso{
+ \code{\link{Return.Geltner}} \cr
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-09-04 10:49:08 UTC (rev 2985)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-09-04 12:57:34 UTC (rev 2986)
@@ -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'
- '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'
+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/R/AcarSim.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R 2013-09-04 10:49:08 UTC (rev 2985)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R 2013-09-04 12:57:34 UTC (rev 2986)
@@ -12,6 +12,7 @@
#' Where j varies from 1 to n ,which is the number of drawdown's in simulation
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
+#' @param nsim number of simulations input
#' @author Shubhankit Mohan
#' @references Maximum Loss and Maximum Drawdown in Financial Markets,\emph{International Conference Sponsored by BNP and Imperial College on:
#' Forecasting Financial Markets, London, United Kingdom, May 1997} \url{http://www.intelligenthedgefundinvesting.com/pubs/easj.pdf}
@@ -22,7 +23,7 @@
#' @rdname AcarSim
#' @export
AcarSim <-
- function(R)
+ function(R,nsim=1)
{
library(PerformanceAnalytics)
@@ -40,7 +41,6 @@
T= 36
j=1
dt=1/T
-nsim=30;
thres=4;
r=matrix(0,nsim,T+1)
monthly = 0
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd 2013-09-04 10:49:08 UTC (rev 2985)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd 2013-09-04 12:57:34 UTC (rev 2986)
@@ -2,11 +2,13 @@
\alias{AcarSim}
\title{Acar-Shane Maximum Loss Plot}
\usage{
- AcarSim(R)
+ AcarSim(R, nsim = 1)
}
\arguments{
\item{R}{an xts, vector, matrix, data frame, timeSeries
or zoo object of asset returns}
+
+ \item{nsim}{number of simulations input}
}
\description{
To get some insight on the relationships between maximum
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