[Returnanalytics-commits] r2880 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Aug 25 12:42:42 CEST 2013
Author: braverock
Date: 2013-08-25 12:42:42 +0200 (Sun, 25 Aug 2013)
New Revision: 2880
Removed:
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Normalized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.NormDD.Rd
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/LoSharpe.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
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/chart.Autocorrelation.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/quad.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.ComparitiveReturn.GLM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.EmaxDDGBM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.UnsmoothReturn.Rd
Log:
- update roxygen docs
- remove old different-case files for several functions
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/DESCRIPTION 2013-08-25 10:42:42 UTC (rev 2880)
@@ -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
-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'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/ACStdDev.annualized.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,52 +1,52 @@
-\name{ACStdDev.annualized}
-\alias{ACStdDev.annualized}
-\alias{sd.annualized}
-\alias{sd.multiperiod}
-\alias{StdDev.annualized}
-\title{Autocorrleation adjusted Standard Deviation}
-\usage{
- ACsd.annualized(edhec,3)
-}
-\arguments{
- \item{x}{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)}
-}
-\author{
- Peter Carl,Brian Peterson, Shubhankit Mohan
-}
-\references{
- Burghardt, G., and L. Liu, \emph{ It's the
- Autocorrelation, Stupid (November 2012) Newedge working
- paper.} \code{\link[stats]{}} \cr
- \url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
-}
-\seealso{
- \code{\link[stats]{sd}} \cr
- \code{\link[stats]{stdDev.annualized}} \cr
- \url{http://en.wikipedia.org/wiki/Volatility_(finance)}
-}
-\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{
+ ACsd.annualized(edhec,3)
+}
+\arguments{
+ \item{x}{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)}
+}
+\author{
+ Peter Carl,Brian Peterson, Shubhankit Mohan
+}
+\references{
+ Burghardt, G., and L. Liu, \emph{ It's the
+ Autocorrelation, Stupid (November 2012) Newedge working
+ paper.} \code{\link[stats]{}} \cr
+ \url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
+}
+\seealso{
+ \code{\link[stats]{sd}} \cr
+ \code{\link[stats]{stdDev.annualized}} \cr
+ \url{http://en.wikipedia.org/wiki/Volatility_(finance)}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,50 +1,50 @@
-\name{AcarSim}
-\alias{AcarSim}
-\title{Acar-Shane Maximum Loss Plot}
-\usage{
- AcarSim(R)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-}
-\description{
- To get some insight on the relationships between maximum
- drawdown per unit of volatility and mean return divided
- by volatility, we have proceeded to Monte-Carlo
- simulations. We have simulated cash flows over a period
- of 36 monthly returns and measured maximum drawdown for
- varied levels of annualised return divided by volatility
- varying from minus \emph{two to two} by step of
- \emph{0.1} . The process has been repeated \bold{six
- thousand times}.
-}
-\details{
- Unfortunately, there is no \bold{analytical formulae} to
- establish the maximum drawdown properties under the
- random walk assumption. We should note first that due to
- its definition, the maximum drawdown divided by
- volatility is an only function of the ratio mean divided
- by volatility. \deqn{MD/[\sigma]= Min (\sum[X(j)])/\sigma
- = F(\mu/\sigma)} Where j varies from 1 to n ,which is the
- number of drawdown's in simulation
-}
-\examples{
-library(PerformanceAnalytics)
-AcarSim(edhec)
-}
-\author{
- Peter Carl, Brian Peterson, 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}
-}
-\keyword{Drawdown}
-\keyword{Loss}
-\keyword{Maximum}
-\keyword{Simulated}
-
+\name{AcarSim}
+\alias{AcarSim}
+\title{Acar-Shane Maximum Loss Plot}
+\usage{
+ AcarSim(R)
+}
+\arguments{
+ \item{R}{an xts, vector, matrix, data frame, timeSeries
+ or zoo object of asset returns}
+}
+\description{
+ To get some insight on the relationships between maximum
+ drawdown per unit of volatility and mean return divided
+ by volatility, we have proceeded to Monte-Carlo
+ simulations. We have simulated cash flows over a period
+ of 36 monthly returns and measured maximum drawdown for
+ varied levels of annualised return divided by volatility
+ varying from minus \emph{two to two} by step of
+ \emph{0.1} . The process has been repeated \bold{six
+ thousand times}.
+}
+\details{
+ Unfortunately, there is no \bold{analytical formulae} to
+ establish the maximum drawdown properties under the
+ random walk assumption. We should note first that due to
+ its definition, the maximum drawdown divided by
+ volatility is an only function of the ratio mean divided
+ by volatility. \deqn{MD/[\sigma]= Min (\sum[X(j)])/\sigma
+ = F(\mu/\sigma)} Where j varies from 1 to n ,which is the
+ number of drawdown's in simulation
+}
+\examples{
+library(PerformanceAnalytics)
+AcarSim(edhec)
+}
+\author{
+ Peter Carl, Brian Peterson, 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}
+}
+\keyword{Drawdown}
+\keyword{Loss}
+\keyword{Maximum}
+\keyword{Simulated}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,63 +1,63 @@
-\name{CDD.Opt}
-\alias{CDD.Opt}
-\title{Chekhlov Conditional Drawdown at Risk Optimization}
-\usage{
- CDD.Opt(rmat, alpha = 0.05, rmin = 0, wmin = 0, wmax = 1,
- weight.sum = 1)
-}
-\arguments{
- \item{Ra}{return vector of the portfolio}
-
- \item{p}{confidence interval}
-}
-\description{
- A new one-parameter family of risk measures called
- Conditional Drawdown (CDD) has been proposed. These
- measures of risk are functionals of the portfolio
- drawdown (underwater) curve considered in active
- portfolio management. For some value of the tolerance
- parameter, in the case of a single sample path, drawdown
- functional is defined as the mean of the worst (1 -
- \eqn{\alpha})% drawdowns.
-}
-\details{
- This section formulates a portfolio optimization problem
- with drawdown risk measure and suggests efficient
- optimization techniques for its solving. Optimal asset
- allocation considers: \enumerate{ \item Generation of
- sample paths for the assets' rates of return. \item
- Uncompounded cumulative portfolio rate of return rather
- than compounded one. } Given a sample path of
- instrument's rates of return (r(1),r(2)...,r(N)), the CDD
- functional, \eqn{\delta[\alpha(w)]}, is computed by the
- following optimization procedure \deqn{\delta[\alpha(w)]
- = min y + [1]/[(1-\alpha)N] \sum [z(k)]} s.t. \deqn{z(k)
- greater than u(k)-y } \deqn{u(k) greater than u(k-1)-
- r(k)} which leads to a single optimal value of y equal to
- \eqn{\epsilon(\alpha)} if \eqn{\pi(\epsilon(\alpha)) >
- \alpha}, and to a closed interval of optimal y with the
- left endpoint of \eqn{\epsilon(\alpha)} if
- \eqn{\pi(\epsilon(\alpha)) = \alpha}
-}
-\examples{
-library(PerformanceAnalytics)
-data(edhec)
-CDDopt(edhec)
-}
-\author{
- Peter Carl, Brian Peterson, Shubhankit Mohan
-}
-\references{
- Chekhlov, Alexei, Uryasev, Stanislav P. and Zabarankin,
- Michael, \emph{Drawdown Measure in Portfolio
- Optimization} (June 25, 2003). Available at SSRN:
- \url{http://ssrn.com/abstract=544742} or
- \url{http://dx.doi.org/10.2139/ssrn.544742}
-}
-\seealso{
- CDrawdown.R
-}
-\keyword{Conditional}
-\keyword{Drawdown}
-\keyword{models}
-
+\name{CDD.Opt}
+\alias{CDD.Opt}
+\title{Chekhlov Conditional Drawdown at Risk Optimization}
+\usage{
+ CDD.Opt(rmat, alpha = 0.05, rmin = 0, wmin = 0, wmax = 1,
+ weight.sum = 1)
+}
+\arguments{
+ \item{Ra}{return vector of the portfolio}
+
+ \item{p}{confidence interval}
+}
+\description{
+ A new one-parameter family of risk measures called
+ Conditional Drawdown (CDD) has been proposed. These
+ measures of risk are functionals of the portfolio
+ drawdown (underwater) curve considered in active
+ portfolio management. For some value of the tolerance
+ parameter, in the case of a single sample path, drawdown
+ functional is defined as the mean of the worst (1 -
+ \eqn{\alpha})% drawdowns.
+}
+\details{
+ This section formulates a portfolio optimization problem
+ with drawdown risk measure and suggests efficient
+ optimization techniques for its solving. Optimal asset
+ allocation considers: \enumerate{ \item Generation of
+ sample paths for the assets' rates of return. \item
+ Uncompounded cumulative portfolio rate of return rather
+ than compounded one. } Given a sample path of
+ instrument's rates of return (r(1),r(2)...,r(N)), the CDD
+ functional, \eqn{\delta[\alpha(w)]}, is computed by the
+ following optimization procedure \deqn{\delta[\alpha(w)]
+ = min y + [1]/[(1-\alpha)N] \sum [z(k)]} s.t. \deqn{z(k)
+ greater than u(k)-y } \deqn{u(k) greater than u(k-1)-
+ r(k)} which leads to a single optimal value of y equal to
+ \eqn{\epsilon(\alpha)} if \eqn{\pi(\epsilon(\alpha)) >
+ \alpha}, and to a closed interval of optimal y with the
+ left endpoint of \eqn{\epsilon(\alpha)} if
+ \eqn{\pi(\epsilon(\alpha)) = \alpha}
+}
+\examples{
+library(PerformanceAnalytics)
+data(edhec)
+CDDopt(edhec)
+}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
+\references{
+ Chekhlov, Alexei, Uryasev, Stanislav P. and Zabarankin,
+ Michael, \emph{Drawdown Measure in Portfolio
+ Optimization} (June 25, 2003). Available at SSRN:
+ \url{http://ssrn.com/abstract=544742} or
+ \url{http://dx.doi.org/10.2139/ssrn.544742}
+}
+\seealso{
+ CDrawdown.R
+}
+\keyword{Conditional}
+\keyword{Drawdown}
+\keyword{models}
+
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,56 +1,56 @@
-\name{CalmarRatio.Norm}
-\alias{CalmarRatio.Norm}
-\title{Normalized Calmar ratio}
-\usage{
- CalmarRatio.Norm(R, tau = 1, scale = NA)
-}
-\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{
- 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. \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.
-}
-\examples{
-data(managers)
- CalmarRatio.Norm(managers[,1,drop=FALSE])
- CalmarRatio.Norm(managers[,1:6])
-}
-\author{
- Brian G. Peterson , Peter Carl , Shubhankit Mohan
-}
-\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}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{ts}
-
+\name{CalmarRatio.Norm}
+\alias{CalmarRatio.Norm}
+\title{Normalized Calmar ratio}
+\usage{
+ CalmarRatio.Norm(R, tau = 1, scale = NA)
+}
+\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{
+ 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. \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.
+}
+\examples{
+data(managers)
+ CalmarRatio.Norm(managers[,1,drop=FALSE])
+ CalmarRatio.Norm(managers[,1:6])
+}
+\author{
+ Brian G. Peterson , Peter Carl , Shubhankit Mohan
+}
+\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}
+}
+\keyword{distribution}
+\keyword{models}
+\keyword{multivariate}
+\keyword{ts}
+
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Normalized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Normalized.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Normalized.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,7 +0,0 @@
-\name{SterlingRatio.Normalized}
-\alias{SterlingRatio.Normalized}
-\usage{
- SterlingRatio.Normalized(R, tau = 1, scale = NA,
- excess = 0.1)
-}
-
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,7 +0,0 @@
-\name{SterlingRatio.Normalized}
-\alias{SterlingRatio.Normalized}
-\usage{
- SterlingRatio.Normalized(R, tau = 1, scale = NA,
- excess = 0.1)
-}
-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.normalized.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -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/Cdrawdown.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,92 +1,92 @@
-\name{CDDOpt}
-\alias{CDDOpt}
-\alias{CDrawdown}
-\title{Chekhlov Conditional Drawdown at Risk}
-\usage{
- CDDOpt(rmat, alpha = 0.05, rmin = 0, wmin = 0, wmax = 1,
- weight.sum = 1)
-
- CDrawdown(R, p = 0.9, ...)
-}
-\arguments{
- \item{Ra}{return vector of the portfolio}
-
- \item{p}{confidence interval}
-
- \item{Ra}{return vector of the portfolio}
-
- \item{p}{confidence interval}
-}
-\description{
- A new one-parameter family of risk measures called
- Conditional Drawdown (CDD) has been proposed. These
- measures of risk are functionals of the portfolio
- drawdown (underwater) curve considered in active
- portfolio management. For some value of the tolerance
- parameter, in the case of a single sample path, drawdown
- functional is defineed as the mean of the worst (1 -
- \eqn{\alpha})% drawdowns.
-
- A new one-parameter family of risk measures called
- Conditional Drawdown (CDD) has been proposed. These
- measures of risk are functionals of the portfolio
- drawdown (underwater) curve considered in active
- portfolio management. For some value of the tolerance
- parameter, in the case of a single sample path, drawdown
- functional is defineed as the mean of the worst (1 -
- \eqn{\alpha})% drawdowns.
-}
-\details{
- This section formulates a portfolio optimization problem
- with drawdown risk measure and suggests e???cient
- optimization techniques for its solving. Optimal asset
- allocation considers: 1) Generation of sample paths for
- the assets' rates of return. 2) Uncompounded cumulative
- portfolio rate of return rather than compounded one.
-
- The \bold{CDD} is related to Value-at-Risk (VaR) and
- Conditional Value-at-Risk (CVaR) measures studied by
- Rockafellar and Uryasev . By definition, with respect to
- a specified probability level \eqn{\alpha}, the
- \bold{\eqn{\alpha}-VaR} of a portfolio is the lowest
- amount \eqn{\epsilon} , \eqn{\alpha} such that, with
- probability \eqn{\alpha}, the loss will not exceed
- \eqn{\epsilon} , \eqn{\alpha} in a specified time T,
- whereas the \bold{\eqn{\alpha}-CVaR} is the conditional
- expectation of losses above that amount \eqn{\epsilon} .
- Various issues about VaR methodology were discussed by
- Jorion . The CDD is similar to CVaR and can be viewed as
- a modification of the CVaR to the case when the
- loss-function is defined as a drawdown. CDD and CVaR are
- conceptually related percentile-based risk performance
- functionals.
-}
-\examples{
-library(PerformanceAnalytics)
-data(edhec)
-CDDopt(edhec)
-library(PerformanceAnalytics)
-data(edhec)
-CDrawdown(edhec)
-}
-\author{
- Peter Carl, Brian Peterson, Shubhankit Mohan
-
- Peter Carl, Brian Peterson, Shubhankit Mohan
-}
-\references{
- DRAWDOWN MEASURE IN PORTFOLIO
- OPTIMIZATION,\emph{International Journal of Theoretical
- and Applied Finance} ,Fall 1994, 49-58.Vol. 8, No. 1
- (2005) 13-58
-
- Chekhlov, Alexei, Uryasev, Stanislav P. and Zabarankin,
- Michael, \emph{Drawdown Measure in Portfolio
- Optimization} (June 25, 2003). Available at SSRN:
- \url{http://ssrn.com/abstract=544742} or
- \url{http://dx.doi.org/10.2139/ssrn.544742}
-}
-\keyword{Conditional}
-\keyword{Drawdown}
-\keyword{models}
-
+\name{CDDOpt}
+\alias{CDDOpt}
+\alias{CDrawdown}
+\title{Chekhlov Conditional Drawdown at Risk}
+\usage{
+ CDDOpt(rmat, alpha = 0.05, rmin = 0, wmin = 0, wmax = 1,
+ weight.sum = 1)
+
+ CDrawdown(R, p = 0.9, ...)
+}
+\arguments{
+ \item{Ra}{return vector of the portfolio}
+
+ \item{p}{confidence interval}
+
+ \item{Ra}{return vector of the portfolio}
+
+ \item{p}{confidence interval}
+}
+\description{
+ A new one-parameter family of risk measures called
+ Conditional Drawdown (CDD) has been proposed. These
+ measures of risk are functionals of the portfolio
+ drawdown (underwater) curve considered in active
+ portfolio management. For some value of the tolerance
+ parameter, in the case of a single sample path, drawdown
+ functional is defineed as the mean of the worst (1 -
+ \eqn{\alpha})% drawdowns.
+
+ A new one-parameter family of risk measures called
+ Conditional Drawdown (CDD) has been proposed. These
+ measures of risk are functionals of the portfolio
+ drawdown (underwater) curve considered in active
+ portfolio management. For some value of the tolerance
+ parameter, in the case of a single sample path, drawdown
+ functional is defineed as the mean of the worst (1 -
+ \eqn{\alpha})% drawdowns.
+}
+\details{
+ This section formulates a portfolio optimization problem
+ with drawdown risk measure and suggests e???cient
+ optimization techniques for its solving. Optimal asset
+ allocation considers: 1) Generation of sample paths for
+ the assets' rates of return. 2) Uncompounded cumulative
+ portfolio rate of return rather than compounded one.
+
+ The \bold{CDD} is related to Value-at-Risk (VaR) and
+ Conditional Value-at-Risk (CVaR) measures studied by
+ Rockafellar and Uryasev . By definition, with respect to
+ a specified probability level \eqn{\alpha}, the
+ \bold{\eqn{\alpha}-VaR} of a portfolio is the lowest
+ amount \eqn{\epsilon} , \eqn{\alpha} such that, with
+ probability \eqn{\alpha}, the loss will not exceed
+ \eqn{\epsilon} , \eqn{\alpha} in a specified time T,
+ whereas the \bold{\eqn{\alpha}-CVaR} is the conditional
+ expectation of losses above that amount \eqn{\epsilon} .
+ Various issues about VaR methodology were discussed by
+ Jorion . The CDD is similar to CVaR and can be viewed as
+ a modification of the CVaR to the case when the
+ loss-function is defined as a drawdown. CDD and CVaR are
+ conceptually related percentile-based risk performance
+ functionals.
+}
+\examples{
+library(PerformanceAnalytics)
+data(edhec)
+CDDopt(edhec)
+library(PerformanceAnalytics)
+data(edhec)
+CDrawdown(edhec)
+}
+\author{
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+
+ Peter Carl, Brian Peterson, Shubhankit Mohan
+}
+\references{
+ DRAWDOWN MEASURE IN PORTFOLIO
+ OPTIMIZATION,\emph{International Journal of Theoretical
+ and Applied Finance} ,Fall 1994, 49-58.Vol. 8, No. 1
+ (2005) 13-58
+
+ Chekhlov, Alexei, Uryasev, Stanislav P. and Zabarankin,
+ Michael, \emph{Drawdown Measure in Portfolio
+ Optimization} (June 25, 2003). Available at SSRN:
+ \url{http://ssrn.com/abstract=544742} or
+ \url{http://dx.doi.org/10.2139/ssrn.544742}
+}
+\keyword{Conditional}
+\keyword{Drawdown}
+\keyword{models}
+
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/EmaxDDGBM.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,23 +0,0 @@
-\name{EMaxDDGBM}
-\alias{EMaxDDGBM}
-\title{Expected Drawdown using Brownian Motion Assumptions}
-\usage{
- EMaxDDGBM(R, digits = 4)
-}
-\arguments{
- \item{R}{an xts, vector, matrix, data frame, timeSeries
- or zoo object of asset returns}
-}
-\description{
- Works on the model specified by Maddon-Ismail
-}
-\author{
- R
-}
-\keyword{Assumptions}
-\keyword{Brownian}
-\keyword{Drawdown}
-\keyword{Expected}
-\keyword{Motion}
-\keyword{Using}
-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-24 23:07:08 UTC (rev 2879)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/GLMSmoothIndex.Rd 2013-08-25 10:42:42 UTC (rev 2880)
@@ -1,48 +1,48 @@
-\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
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 2880
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