[Returnanalytics-commits] r2916 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed Aug 28 10:55:04 CEST 2013
Author: shubhanm
Date: 2013-08-28 10:55:04 +0200 (Wed, 28 Aug 2013)
New Revision: 2916
Added:
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/se.LoSharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/se.LoSharpe.Rd
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd
Log:
/ standard error LoSharpe
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-28 05:08:11 UTC (rev 2915)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-28 08:55:04 UTC (rev 2916)
@@ -1,38 +1,39 @@
-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'
+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'
+ 'se.LoSharpe.R'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-28 05:08:11 UTC (rev 2915)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/NAMESPACE 2013-08-28 08:55:04 UTC (rev 2916)
@@ -12,6 +12,7 @@
export(QP.Norm)
export(Return.GLM)
export(Return.Okunev)
+export(se.LoSharpe)
export(SterlingRatio.Norm)
export(SterlingRatio.Normalized)
export(table.ComparitiveReturn.GLM)
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/se.LoSharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/se.LoSharpe.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/se.LoSharpe.R 2013-08-28 08:55:04 UTC (rev 2916)
@@ -0,0 +1,92 @@
+#'@title Andrew Lo Sharpe Ratio Statistics
+#'@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 which 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(se.LoSharpe(edhec,0,3)
+#' @rdname se.LoSharpe
+#' @export
+se.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])))
+ column.lo= 1.96*sqrt((1+(column.lo*column.lo/2))/(Time))
+ if(column.a == 1) { lo = column.lo }
+ else { lo = cbind (lo, column.lo) }
+
+ }
+ colnames(lo) = columnnames.a
+ rownames(lo)= paste("Standard Error of Sharpe Ratio Estimates(95% Confidence)")
+ return(lo)
+
+
+ # RESULTS:
+
+ }
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd 2013-08-28 05:08:11 UTC (rev 2915)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.Rd 2013-08-28 08:55:04 UTC (rev 2916)
@@ -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. \code{\link[stats]{}} \cr
- \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. \code{\link[stats]{}} \cr
+ \url{http://ssrn.com/abstract=384700}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{non-iid}
+\keyword{ts}
+
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/se.LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/se.LoSharpe.Rd (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/se.LoSharpe.Rd 2013-08-28 08:55:04 UTC (rev 2916)
@@ -0,0 +1,70 @@
+\name{se.LoSharpe}
+\alias{se.LoSharpe}
+\title{Andrew Lo Sharpe Ratio Statistics}
+\usage{
+ se.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 which 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(se.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}
+
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