[Returnanalytics-commits] r2874 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 24 19:52:50 CEST 2013
Author: shubhanm
Date: 2013-08-24 19:52:50 +0200 (Sat, 24 Aug 2013)
New Revision: 2874
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/AcarSim.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDD.Opt.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDrawdown.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/EmaxDDGBM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.ComparitiveReturn.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.UnsmoothReturn.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.normDD.R
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/Cdrawdown.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.ComparitiveReturn.GLM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.EmaxDDGBM.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.NormDD.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.UnsmoothReturn.Rd
Log:
/.Rd Completed Documentation
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/AcarSim.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/AcarSim.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/AcarSim.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,16 +1,21 @@
-#' @title Acar and Shane Maximum Loss
+#' @title Acar-Shane Maximum Loss Plot
#'
#'@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
-#' two to two by step of 0.1. The process has been repeated six thousand times.
+#' \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
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @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}
-#' @keywords Maximum Loss Simulared Drawdown
+#' Forecasting Financial Markets, London, United Kingdom, May 1997} \url{http://www.intelligenthedgefundinvesting.com/pubs/easj.pdf}
+#' @keywords Maximum Loss Simulated Drawdown
#' @examples
#' library(PerformanceAnalytics)
#' AcarSim(edhec)
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDD.Opt.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDD.Opt.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDD.Opt.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -2,7 +2,7 @@
#'
#' @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
+#' 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:
@@ -10,17 +10,24 @@
#' \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}
#' @param Ra return vector of the portfolio
#' @param p confidence interval
#' @author 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
+#' @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}
#' @keywords Conditional Drawdown models
#' @examples
#'
#'library(PerformanceAnalytics)
#' data(edhec)
#' CDDopt(edhec)
+#' @seealso CDrawdown.R
#' @rdname CDD.Opt
#' @export
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDrawdown.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDrawdown.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CDrawdown.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -4,17 +4,23 @@
#'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 The CDD measure generalizes the notion of the drawdown functional to a multi-scenario case and can be considered as a
-#'generalization of deviation measure to a dynamic case. The CDD measure includes the
-#'Maximal Drawdown and Average Drawdown as its limiting cases. The model is focused on concept of drawdown measure which is in possession of all properties of a deviation measure,generalization of deviation measures to a dynamic case.Concept of risk profiling - Mixed Conditional Drawdown (generalization of CDD).Optimization techniques for CDD computation - reduction to linear programming (LP) problem. Portfolio optimization with constraint on Mixed CDD
-#' The model develops concept of drawdown measure by generalizing the notion
-#' of the CDD to the case of several sample paths for portfolio uncompounded rate
-#' of return.
+#'@details
+#'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.
#' @param Ra return vector of the portfolio
#' @param p confidence interval
#' @author 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
+#' @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}
#' @keywords Conditional Drawdown models
#' @examples
#'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/CalmarRatio.Norm.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -26,7 +26,8 @@
#' @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/R/EmaxDDGBM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/EmaxDDGBM.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/EmaxDDGBM.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -2,11 +2,18 @@
#'
#' @description Works on the model specified by Maddon-Ismail which investigates the behavior of this statistic for a Brownian motion
#' with drift.
+#' @details If X(t) is a random process on [0, T ], the maximum drawdown at time T , D(T), is defined by
+#' where \deqn{D(T) = sup [X(s) - X(t)]} where s belongs to [0,t] and s belongs to [0,T]
+#'Informally, this is the largest drop from a peak to a bottom. In this paper, we investigate the
+#'behavior of this statistic for a Brownian motion with drift. In particular, we give an infinite
+#'series representation of its distribution, and consider its expected value. When the drift is zero,
+#'we give an analytic expression for the expected value, and for non-zero drift, we give an infinite
+#'series representation. For all cases, we compute the limiting \bold{(\eqn{T tends to \infty})} behavior, which can be
+#'logarithmic (\eqn{\mu} > 0), square root (\eqn{\mu} = 0), or linear (\eqn{\mu} < 0).
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
#' @keywords Expected Drawdown Using Brownian Motion Assumptions
-#' @references An Analysis of the maximum drawdown measure,\emph{Journal of Applied Probability}
-#' (2004)
+#' @references Magdon-Ismail, M., Atiya, A., Pratap, A., and Yaser S. Abu-Mostafa: On the Maximum Drawdown of a Browninan Motion, Journal of Applied Probability 41, pp. 147-161, 2004 \url{http://alumnus.caltech.edu/~amir/drawdown-jrnl.pdf}
#' @keywords Drawdown models Brownian Motion Assumptions
#' @examples
#'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.ComparitiveReturn.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.ComparitiveReturn.GLM.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.ComparitiveReturn.GLM.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -11,6 +11,8 @@
#' @param digits number of digits to round results to
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
#' @keywords ts unsmooth GLM return models
+#' @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}
#' @rdname table.ComparitiveReturn.GLM
#' @export
table.ComparitiveReturn.GLM <-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.UnsmoothReturn.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.UnsmoothReturn.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.UnsmoothReturn.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,17 +1,34 @@
-#' @title Compenent Decomposition of Table of Unsmooth Returns
+#' @title Table of Unsmooth Returns
#'
#' @description Creates a table of estimates of moving averages for comparison across
#' multiple instruments or funds as well as their standard error and
-#' smoothing index
+#' smoothing index , which is Compenent Decomposition of Table of Unsmooth Returns
#'
+#' @details The estimation method is based on a maximum likelihood estimation of a moving average
+#' process (we use the innovations algorithm proposed by \bold{Brockwell and Davis} [1991]). The first
+#' step of this approach consists in computing a series of de-meaned observed returns:
+#' \deqn{X(t) = R(0,t)- \mu}
+#' where \eqn{\mu} is the expected value of the series of observed returns.
+#' As a consequence, the above equation can be written as :
+#' \deqn{X(t)= \theta(0)\eta(t) + \theta(1)\eta(t-1) + ..... + \theta(k)\eta(t-k)}
+#' with the additional assumption that : \bold{\eqn{\eta(k)= N(0,\sigma(\eta)^2)}}
+#' The structure of the model and the two constraints suppose that the complete integration of
+#'information in the price of the considered asset may take up to k periods because of its illiquidity.
+#'In addition, according to Getmansky et al., this model is in line with previous models of nonsynchronous trading such as the one developed by \bold{Cohen, Maier, Schwartz and Whitcomb}
+#' [1986].
+#' Smoothing has an impact on the third and fourth moments of the returns distribution too.
#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
#' asset returns
#' @param ci confidence interval, defaults to 95\%
#' @param n number of series lags
#' @param p confidence level for calculation, default p=.99
#' @param digits number of digits to round results to
+#' @references Cavenaile, Laurent, Coen, Alain and Hubner, Georges,\emph{ The Impact of Illiquidity and Higher Moments of Hedge Fund Returns on Their Risk-Adjusted Performance and Diversification Potential} (October 30, 2009). Journal of Alternative Investments, Forthcoming. Available at SSRN: \url{http://ssrn.com/abstract=1502698} Working paper is at \url{http://www.hec.ulg.ac.be/sites/default/files/workingpapers/WP_HECULg_20091001_Cavenaile_Coen_Hubner.pdf}
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
#' @keywords ts smooth return models
+#' @seealso Reutrn.Geltner Reutrn.GLM Return.Okunev
+#'
+#'
#' @rdname table.UnsmoothReturn
#' @export
table.UnsmoothReturn <-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.normDD.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.normDD.R 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/R/table.normDD.R 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,12 +1,15 @@
-#'@title Generalised Lambda Distribution Simulated Drardown
-#'
-#'@description To simulate net asset value (NAV) series where skewness and kurtosis are zero,
+#'@title Generalised Lambda Distribution Simulated Drawdown
+#'@description When selecting a hedge fund manager, one risk measure investors often
+#' consider is drawdown. How should drawdown distributions look? Carr Futures'
+#' Galen Burghardt, Ryan Duncan and Lianyan Liu share some insights from their
+#'research to show investors how to begin to answer this tricky question
+#'@details To simulate net asset value (NAV) series where skewness and kurtosis are zero,
#' we draw sample returns from a lognormal return distribution. To capture skewness
-#' and kurtosis, we sample returns from a generalised lambda distribution.The values of
-#' skewness and excess kurtosis used were roughly consistent with the range of values we
+#' and kurtosis, we sample returns from a \bold{generalised \eqn{\lambda} distribution}.The values of
+#' skewness and excess kurtosis used were roughly consistent with the range of values the paper
#' observed for commodity trading advisers in our database. The NAV series is constructed
#' from the return series. The simulated drawdowns are then derived and used to produce
-#' the theoretical drawdown distributions. A typical run usually requires 10,000
+#' the theoretical drawdown distributions. A typical run usually requires \bold{10,000}
#' iterations to produce a smooth distribution.
#'
#'
@@ -16,8 +19,10 @@
#' working paper.}
#' \code{\link[stats]{}} \cr
#' \url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
+#' Burghardt, G., Duncan, R. and L. Liu, \eph{Deciphering drawdown}. Risk magazine, Risk management for investors, September, S16-S20, 2003. \url{http://www.risk.net/data/risk/pdf/investor/0903_risk.pdf}
#' @author Peter Carl, Brian Peterson, Shubhankit Mohan
#' @keywords Simulated Drawdown Using Brownian Motion Assumptions
+#' @seealso Drawdowns.R
#' @rdname table.normDD
#' @export
table.NormDD <-
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/AcarSim.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,6 +1,6 @@
\name{AcarSim}
\alias{AcarSim}
-\title{Acar and Shane Maximum Loss}
+\title{Acar-Shane Maximum Loss Plot}
\usage{
AcarSim(R)
}
@@ -15,9 +15,20 @@
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 two to two by step of 0.1. The process
- has been repeated six thousand times.
+ 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)
@@ -30,9 +41,10 @@
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{Simulared}
+\keyword{Simulated}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CDD.Opt.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -17,7 +17,7 @@
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 -
+ functional is defined as the mean of the worst (1 -
\eqn{\alpha})% drawdowns.
}
\details{
@@ -27,7 +27,17 @@
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. }
+ 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)
@@ -38,11 +48,15 @@
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}
}
+\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 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/CalmarRatio.Norm.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -45,8 +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}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/Cdrawdown.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -44,22 +44,22 @@
the assets' rates of return. 2) Uncompounded cumulative
portfolio rate of return rather than compounded one.
- The CDD measure generalizes the notion of the drawdown
- functional to a multi-scenario case and can be considered
- as a generalization of deviation measure to a dynamic
- case. The CDD measure includes the Maximal Drawdown and
- Average Drawdown as its limiting cases. The model is
- focused on concept of drawdown measure which is in
- possession of all properties of a deviation
- measure,generalization of deviation measures to a dynamic
- case.Concept of risk profiling - Mixed Conditional
- Drawdown (generalization of CDD).Optimization techniques
- for CDD computation - reduction to linear programming
- (LP) problem. Portfolio optimization with constraint on
- Mixed CDD The model develops concept of drawdown measure
- by generalizing the notion of the CDD to the case of
- several sample paths for portfolio uncompounded rate of
- return.
+ 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)
@@ -80,10 +80,11 @@
and Applied Finance} ,Fall 1994, 49-58.Vol. 8, No. 1
(2005) 13-58
- 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}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.ComparitiveReturn.GLM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.ComparitiveReturn.GLM.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.ComparitiveReturn.GLM.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -23,6 +23,12 @@
\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}
+}
\keyword{GLM}
\keyword{models}
\keyword{return}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.EmaxDDGBM.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.EmaxDDGBM.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.EmaxDDGBM.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -13,6 +13,23 @@
investigates the behavior of this statistic for a
Brownian motion with drift.
}
+\details{
+ If X(t) is a random process on [0, T ], the maximum
+ drawdown at time T , D(T), is defined by where \deqn{D(T)
+ = sup [X(s) - X(t)]} where s belongs to [0,t] and s
+ belongs to [0,T] Informally, this is the largest drop
+ from a peak to a bottom. In this paper, we investigate
+ the behavior of this statistic for a Brownian motion with
+ drift. In particular, we give an infinite series
+ representation of its distribution, and consider its
+ expected value. When the drift is zero, we give an
+ analytic expression for the expected value, and for
+ non-zero drift, we give an infinite series
+ representation. For all cases, we compute the limiting
+ \bold{(\eqn{T tends to \infty})} behavior, which can be
+ logarithmic (\eqn{\mu} > 0), square root (\eqn{\mu} = 0),
+ or linear (\eqn{\mu} < 0).
+}
\examples{
library(PerformanceAnalytics)
data(edhec)
@@ -22,8 +39,11 @@
Peter Carl, Brian Peterson, Shubhankit Mohan
}
\references{
- An Analysis of the maximum drawdown measure,\emph{Journal
- of Applied Probability} (2004)
+ Magdon-Ismail, M., Atiya, A., Pratap, A., and Yaser S.
+ Abu-Mostafa: On the Maximum Drawdown of a Browninan
+ Motion, Journal of Applied Probability 41, pp. 147-161,
+ 2004
+ \url{http://alumnus.caltech.edu/~amir/drawdown-jrnl.pdf}
}
\keyword{Assumptions}
\keyword{Brownian}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.NormDD.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.NormDD.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.NormDD.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,6 +1,6 @@
\name{table.NormDD}
\alias{table.NormDD}
-\title{Generalised Lambda Distribution Simulated Drardown}
+\title{Generalised Lambda Distribution Simulated Drawdown}
\usage{
table.NormDD(R, digits = 4)
}
@@ -9,17 +9,26 @@
or zoo object of asset returns}
}
\description{
+ When selecting a hedge fund manager, one risk measure
+ investors often consider is drawdown. How should drawdown
+ distributions look? Carr Futures' Galen Burghardt, Ryan
+ Duncan and Lianyan Liu share some insights from their
+ research to show investors how to begin to answer this
+ tricky question
+}
+\details{
To simulate net asset value (NAV) series where skewness
and kurtosis are zero, we draw sample returns from a
lognormal return distribution. To capture skewness and
- kurtosis, we sample returns from a generalised lambda
- distribution.The values of skewness and excess kurtosis
- used were roughly consistent with the range of values we
- observed for commodity trading advisers in our database.
- The NAV series is constructed from the return series. The
- simulated drawdowns are then derived and used to produce
- the theoretical drawdown distributions. A typical run
- usually requires 10,000 iterations to produce a smooth
+ kurtosis, we sample returns from a \bold{generalised
+ \eqn{\lambda} distribution}.The values of skewness and
+ excess kurtosis used were roughly consistent with the
+ range of values the paper observed for commodity trading
+ advisers in our database. The NAV series is constructed
+ from the return series. The simulated drawdowns are then
+ derived and used to produce the theoretical drawdown
+ distributions. A typical run usually requires
+ \bold{10,000} iterations to produce a smooth
distribution.
}
\author{
@@ -30,7 +39,14 @@
Autocorrelation, Stupid (November 2012) Newedge working
paper.} \code{\link[stats]{}} \cr
\url{http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf}
+ Burghardt, G., Duncan, R. and L. Liu, \eph{Deciphering
+ drawdown}. Risk magazine, Risk management for investors,
+ September, S16-S20, 2003.
+ \url{http://www.risk.net/data/risk/pdf/investor/0903_risk.pdf}
}
+\seealso{
+ Drawdowns.R
+}
\keyword{Assumptions}
\keyword{Brownian}
\keyword{Drawdown}
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.UnsmoothReturn.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.UnsmoothReturn.Rd 2013-08-24 17:48:09 UTC (rev 2873)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/man/table.UnsmoothReturn.Rd 2013-08-24 17:52:50 UTC (rev 2874)
@@ -1,6 +1,6 @@
\name{table.UnsmoothReturn}
\alias{table.UnsmoothReturn}
-\title{Compenent Decomposition of Table of Unsmooth Returns}
+\title{Table of Unsmooth Returns}
\usage{
table.UnsmoothReturn(R, n = 3, p = 0.95, digits = 4)
}
@@ -19,11 +19,47 @@
\description{
Creates a table of estimates of moving averages for
comparison across multiple instruments or funds as well
- as their standard error and smoothing index
+ as their standard error and smoothing index , which is
+ Compenent Decomposition of Table of Unsmooth Returns
}
+\details{
+ The estimation method is based on a maximum likelihood
+ estimation of a moving average process (we use the
+ innovations algorithm proposed by \bold{Brockwell and
+ Davis} [1991]). The first step of this approach consists
+ in computing a series of de-meaned observed returns:
+ \deqn{X(t) = R(0,t)- \mu} where \eqn{\mu} is the expected
+ value of the series of observed returns. As a
+ consequence, the above equation can be written as :
+ \deqn{X(t)= \theta(0)\eta(t) + \theta(1)\eta(t-1) + .....
+ + \theta(k)\eta(t-k)} with the additional assumption that
+ : \bold{\eqn{\eta(k)= N(0,\sigma(\eta)^2)}} The structure
+ of the model and the two constraints suppose that the
+ complete integration of information in the price of the
+ considered asset may take up to k periods because of its
+ illiquidity. In addition, according to Getmansky et al.,
+ this model is in line with previous models of
+ nonsynchronous trading such as the one developed by
+ \bold{Cohen, Maier, Schwartz and Whitcomb} [1986].
+ Smoothing has an impact on the third and fourth moments
+ of the returns distribution too.
+}
\author{
Peter Carl, Brian Peterson, Shubhankit Mohan
}
+\references{
+ Cavenaile, Laurent, Coen, Alain and Hubner,
+ Georges,\emph{ The Impact of Illiquidity and Higher
+ Moments of Hedge Fund Returns on Their Risk-Adjusted
+ Performance and Diversification Potential} (October 30,
+ 2009). Journal of Alternative Investments, Forthcoming.
+ Available at SSRN: \url{http://ssrn.com/abstract=1502698}
+ Working paper is at
+ \url{http://www.hec.ulg.ac.be/sites/default/files/workingpapers/WP_HECULg_20091001_Cavenaile_Coen_Hubner.pdf}
+}
+\seealso{
+ Reutrn.Geltner Reutrn.GLM Return.Okunev
+}
\keyword{models}
\keyword{return}
\keyword{smooth}
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