[Returnanalytics-commits] r3016 - in pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm: . R man vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Sep 7 13:19:40 CEST 2013
Author: shubhanm
Date: 2013-09-07 13:19:40 +0200 (Sat, 07 Sep 2013)
New Revision: 3016
Added:
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/ShaneAcarMaxLoss.synctex.gz
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/ShaneAcarMaxLoss.tex
Removed:
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/se.LoSharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/table.Sharpe.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/se.LoSharpe.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/table.Sharpe.Rd
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/CommodityReport.Rnw
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/LoSharpe.Rnw
Modified:
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/ShaneAcarMaxLoss.Rnw
pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/ShaneAcarMaxLoss.pdf
Log:
Temp Clean version of R CMD Build Checking...removing bugs in deleted functions
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/DESCRIPTION 2013-09-07 11:19:40 UTC (rev 3016)
@@ -16,7 +16,6 @@
License: GPL-3
ByteCompile: TRUE
Collate:
- 'AcarSim.R'
'ACStdDev.annualized.R'
'CalmarRatio.Norm.R'
'CDrawdown.R'
@@ -24,16 +23,13 @@
'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'
- 'table.Sharpe.R'
Modified: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/NAMESPACE 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,4 +1,3 @@
-export(AcarSim)
export(ACStdDev.annualized)
export(CalmarRatio.Norm)
export(CDrawdown)
@@ -6,13 +5,10 @@
export(chart.Autocorrelation)
export(EMaxDDGBM)
export(GLMSmoothIndex)
-export(LoSharpe)
export(QP.Norm)
export(Return.GLM)
export(Return.Okunev)
-export(se.LoSharpe)
export(SterlingRatio.Norm)
export(table.ComparitiveReturn.GLM)
export(table.EMaxDDGBM)
-export(table.Sharpe)
export(table.UnsmoothReturn)
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/AcarSim.R 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,103 +0,0 @@
-#' @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
-#' \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 can be interpreted as the 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
-#' @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}
-#' @keywords Maximum Loss Simulated Drawdown
-#' @examples
-#' library(PerformanceAnalytics)
-#' AcarSim(R)
-#' @rdname AcarSim
-#' @export
-AcarSim <-
- function(R,nsim=1)
- {
- library(PerformanceAnalytics)
-
- data(edhec)
-
- R = checkData(R, method="xts")
- # Get dimensions and labels
- # simulated parameters using edhec data
-mu=mean(Return.annualized(R))
-monthly=(1+mu)^(1/12)-1
- vol = as.numeric(StdDev.annualized(R));
- ret=as.numeric(Return.annualized(R))
- drawdown =as.numeric(maxDrawdown(R))
- sig=mean(StdDev.annualized(R));
-T= 36
-j=1
-dt=1/T
-thres=4;
-r=matrix(0,nsim,T+1)
-monthly = 0
-r[,1]=monthly;
-# Sigma 'monthly volatiltiy' will be the varying term
-ratio= seq(-2, 2, by=.1);
-len = length(ratio)
-ddown=array(0, dim=c(nsim,len,thres))
-fddown=array(0, dim=c(len,thres))
-Z <- array(0, c(len))
-for(i in 1:len)
-{
- monthly = sig*ratio[i];
-
- for(j in 1:nsim)
-{
- dz=rnorm(T)
-
-
- r[j,2:37]=monthly+(sig*dz*sqrt(3*dt))
-
- ddown[j,i,1]= ES((r[j,]),.99, method="modified")
- ddown[j,i,1][is.na(ddown[j,i,1])] <- 0
- fddown[i,1]=fddown[i,1]+ddown[j,i,1]
- ddown[j,i,2]= ES((r[j,]),.95, method="modified")
- ddown[j,i,2][is.na(ddown[j,i,2])] <- 0
- fddown[i,2]=fddown[i,2]+ddown[j,i,2]
- ddown[j,i,3]= ES((r[j,]),.90, method="modified")
- ddown[j,i,3][is.na(ddown[j,i,3])] <- 0
- fddown[i,3]=fddown[i,3]+ddown[j,i,3]
- ddown[j,i,4]= ES((r[j,]),.85, method="modified")
- ddown[j,i,4][is.na(ddown[j,i,4])] <- 0
- fddown[i,4]=fddown[i,4]+ddown[j,i,4]
- assign("last.warning", NULL, envir = baseenv())
-}
-}
-plot(((fddown[,1])/(sig*nsim)),xlab="Annualised Return/Volatility from [-2,2]",ylab="Maximum Drawdown/Volatility",type='o',col="blue")
-lines(((fddown[,2])/(sig*nsim)),type='o',col="pink")
-lines(((fddown[,3])/(sig*nsim)),type='o',col="green")
-lines(((fddown[,4])/(sig*nsim)),type='o',col="red")
- points((ret/vol), (-drawdown/vol), col = "black", pch=10)
- legend(32,-4, c("%99", "%95", "%90","%85","Fund"), col = c("blue","pink","green","red","black"), text.col= "black",
- lty = c(2, -1, 1,2), pch = c(-1, 3, 4,10), merge = TRUE, bg='gray90')
-
-title("Maximum Drawdown/Volatility as a function of Return/Volatility
-36 monthly returns simulated 6,000 times")
- edhec=NULL
-}
-
-###############################################################################
-# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
-#
-# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
-#
-# This R package is distributed under the terms of the GNU Public License (GPL)
-# for full details see the file COPYING
-#
-# $Id: AcarSim.R 2163 2012-07-16 00:30:19Z braverock $
-#
-###############################################################################
\ No newline at end of file
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/LoSharpe.R 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,98 +0,0 @@
-#'@title Andrew Lo Sharpe Ratio
-#'@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
-#' SR(q) : Estimated Lo Sharpe Ratio
-#' SR : Theoretical William Sharpe Ratio
-#' @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 Shubhankit Mohan
-#' @references Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002, AIMR.
-#'\code{\link[stats]{}} \cr
-#' \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-#'
-#' Andrew Lo,\emph{Sharpe Ratio may be Overstated}
-#' \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-#' @keywords ts multivariate distribution models non-iid
-#' @examples
-#'
-#' data(edhec)
-#' LoSharpe(edhec,0,3)
-#' @rdname LoSharpe
-#' @export
-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
- clean.ret=na.omit(R[,column.a])
- mu = sum(clean.ret)/(Time)
- sig=sqrt(((clean.ret-mu)^2/(Time)))
- pho.k = clean.lo(clean.ret,q)/(as.numeric(sig[1]))
- netaq=neta.lo(pho.k,q)
- #column.lo = (netaq*((mu-Rf)/as.numeric(sig[1])))
- column.lo = as.numeric(SharpeRatio.annualized(R[,column.a]))[1]*netaq
- if(column.a == 1) { lo = column.lo }
- else { lo = cbind (lo, column.lo) }
-
- }
- colnames(lo) = columnnames.a
- rownames(lo)= paste("Lo Sharpe Ratio")
- return(lo)
-
-
- # RESULTS:
-
- }
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/se.LoSharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/se.LoSharpe.R 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/se.LoSharpe.R 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,99 +0,0 @@
-#'@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
-#' Under the assumption of assumption of asymptotic variance of SR(q), the standard error for the Sharpe Ratio Esitmator can be computed as:
-#' \deqn{SE(SR(q)) = \sqrt((1+SR^2/2)/T)}
-#' SR(q) : Estimated Lo Sharpe Ratio
-#' SR : Theoretical William Sharpe Ratio
-#' @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 Shubhankit Mohan
-#' @references Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002, AIMR.
-#'\code{\link[stats]{}} \cr
-#' \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-#'
-#' Andrew Lo,\emph{Sharpe Ratio may be Overstated}
-#' \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-#' @keywords ts multivariate distribution models non-iid
-#' @examples
-#'
-#' data(edhec)
-#' 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:
-
- }
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/table.Sharpe.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/table.Sharpe.R 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/R/table.Sharpe.R 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,96 +0,0 @@
-#'@title Sharpe Ratio Statistics Summary
-#'@description
-#' The Sharpe ratio is simply the return per unit of risk (represented by
-#' variability). In the classic case, the unit of risk is the standard
-#' deviation of the returns.
-#'
-#' \deqn{\frac{\overline{(R_{a}-R_{f})}}{\sqrt{\sigma_{(R_{a}-R_{f})}}}}
-#'
-#' William Sharpe now recommends \code{\link{InformationRatio}} preferentially
-#' to the original Sharpe Ratio.
-#'
-#' The higher the Sharpe ratio, the better the combined performance of "risk"
-#' and return.
-#'
-#' As noted, the traditional Sharpe Ratio is a risk-adjusted measure of return
-#' that uses standard deviation to represent risk.
-
-#' 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
-#' SR(q) : Estimated Lo Sharpe Ratio
-#' SR : Theoretical William Sharpe Ratio
-#' @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 Shubhankit Mohan
-#' @references Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002, AIMR.
-#'\code{\link[stats]{}} \cr
-#' \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-#'
-#' Andrew Lo,\emph{Sharpe Ratio may be Overstated}
-#' \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-#' @keywords ts multivariate distribution models non-iid
-#' @examples
-#'
-#' data(edhec)
-#' table.Sharpe(edhec,0,3)
-#' @rdname table.Sharpe
-#' @export
-table.Sharpe <-
- function (Ra,Rf = 0,q = 3, ...)
- { y = checkData(Ra, method = "xts")
- columns = ncol(y)
- rows = nrow(y)
- columnnames = colnames(y)
- rownames = rownames(y)
-
- # for each column, do the following:
- for(column in 1:columns) {
- x = y[,column]
-
- z = c(as.numeric(SharpeRatio.annualized(x)),
- as.numeric(LoSharpe(x)),
- as.numeric(Return.annualized(x)),as.numeric(StdDev.annualized(x)),as.numeric(se.Losharpe(x)))
-
- znames = c(
- "William Sharpe Ratio",
- "Andrew Lo Sharpe Ratio",
- "Annualized Return",
- "Annualized Standard Deviation","Sharpe Ratio Standard Error(95%)"
- )
- if(column == 1) {
- resultingtable = data.frame(Value = z, row.names = znames)
- }
- else {
- nextcolumn = data.frame(Value = z, row.names = znames)
- resultingtable = cbind(resultingtable, nextcolumn)
- }
- }
- colnames(resultingtable) = columnnames
- ans = base::round(resultingtable, digits)
- ans
-
-
- }
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/AcarSim.Rd 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,52 +0,0 @@
-\name{AcarSim}
-\alias{AcarSim}
-\title{Acar-Shane Maximum Loss Plot}
-\usage{
- 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
- 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 can be interpreted as the 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(R)
-}
-\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}
-}
-\keyword{Drawdown}
-\keyword{Loss}
-\keyword{Maximum}
-\keyword{Simulated}
-
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/LoSharpe.Rd 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,71 +0,0 @@
-\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 SR(q) :
- Estimated Lo Sharpe Ratio SR : Theoretical William Sharpe
- Ratio
-}
-\examples{
-data(edhec)
-LoSharpe(edhec,0,3)
-}
-\author{
- Shubhankit Mohan
-}
-\references{
- Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002,
- AIMR. \code{\link[stats]{}} \cr
- \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-
- Andrew Lo,\emph{Sharpe Ratio may be Overstated}
- \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{non-iid}
-\keyword{ts}
-
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/se.LoSharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/se.LoSharpe.Rd 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/se.LoSharpe.Rd 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,74 +0,0 @@
-\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 Under the
- assumption of assumption of asymptotic variance of SR(q),
- the standard error for the Sharpe Ratio Esitmator can be
- computed as: \deqn{SE(SR(q)) = \sqrt((1+SR^2/2)/T)} SR(q)
- : Estimated Lo Sharpe Ratio SR : Theoretical William
- Sharpe Ratio
-}
-\examples{
-data(edhec)
-se.LoSharpe(edhec,0,3)
-}
-\author{
- Shubhankit Mohan
-}
-\references{
- Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002,
- AIMR. \code{\link[stats]{}} \cr
- \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-
- Andrew Lo,\emph{Sharpe Ratio may be Overstated}
- \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{non-iid}
-\keyword{ts}
-
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/table.Sharpe.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/table.Sharpe.Rd 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/man/table.Sharpe.Rd 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,86 +0,0 @@
-\name{table.Sharpe}
-\alias{table.Sharpe}
-\title{Sharpe Ratio Statistics Summary}
-\usage{
- table.Sharpe(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{
- The Sharpe ratio is simply the return per unit of risk
- (represented by variability). In the classic case, the
- unit of risk is the standard deviation of the returns.
-
- \deqn{\frac{\overline{(R_{a}-R_{f})}}{\sqrt{\sigma_{(R_{a}-R_{f})}}}}
-
- William Sharpe now recommends
- \code{\link{InformationRatio}} preferentially to the
- original Sharpe Ratio.
-
- The higher the Sharpe ratio, the better the combined
- performance of "risk" and return.
-
- As noted, the traditional Sharpe Ratio is a risk-adjusted
- measure of return that uses standard deviation to
- represent risk. 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 SR(q) :
- Estimated Lo Sharpe Ratio SR : Theoretical William Sharpe
- Ratio
-}
-\examples{
-data(edhec)
-table.Sharpe(edhec,0,3)
-}
-\author{
- Shubhankit Mohan
-}
-\references{
- Andrew Lo,\emph{ The Statistics of Sharpe Ratio.}2002,
- AIMR. \code{\link[stats]{}} \cr
- \url{http://papers.ssrn.com/sol3/papers.cfm?abstract_id=377260}
-
- Andrew Lo,\emph{Sharpe Ratio may be Overstated}
- \url{http://www.risk.net/risk-magazine/feature/1506463/lo-sharpe-ratios-overstated}
-}
-\keyword{distribution}
-\keyword{models}
-\keyword{multivariate}
-\keyword{non-iid}
-\keyword{ts}
-
Deleted: pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/CommodityReport.Rnw
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/CommodityReport.Rnw 2013-09-07 10:16:35 UTC (rev 3015)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/noniid.sm/vignettes/CommodityReport.Rnw 2013-09-07 11:19:40 UTC (rev 3016)
@@ -1,226 +0,0 @@
-%% no need for \DeclareGraphicsExtensions{.pdf,.eps}
-
-\documentclass[12pt,letterpaper,english]{article}
-\usepackage{times}
-\usepackage[T1]{fontenc}
-\IfFileExists{url.sty}{\usepackage{url}}
- {\newcommand{\url}{\texttt}}
-
-\usepackage{babel}
-%\usepackage{noweb}
-\usepackage{Rd}
-
-\usepackage{Sweave}
-\SweaveOpts{engine=R,eps=FALSE}
-%\VignetteIndexEntry{Performance Attribution from Bacon}
-%\VignetteDepends{PerformanceAnalytics}
-%\VignetteKeywords{returns, performance, risk, benchmark, portfolio}
-%\VignettePackage{PerformanceAnalytics}
-
-%\documentclass[a4paper]{article}
-%\usepackage[noae]{Sweave}
-%\usepackage{ucs}
-%\usepackage[utf8x]{inputenc}
-%\usepackage{amsmath, amsthm, latexsym}
-%\usepackage[top=3cm, bottom=3cm, left=2.5cm]{geometry}
-%\usepackage{graphicx}
-%\usepackage{graphicx, verbatim}
-%\usepackage{ucs}
-%\usepackage[utf8x]{inputenc}
-%\usepackage{amsmath, amsthm, latexsym}
-%\usepackage{graphicx}
-
-\title{Commodity Index Fund Performance Analysis}
-\author{Shubhankit Mohan}
-
-\begin{document}
-\SweaveOpts{concordance=TRUE}
-
-\maketitle
-
-
-\begin{abstract}
-The fact that many hedge fund returns exhibit extraordinary levels of serial correlation is now well-known and generally accepted as fact. The effect of this autocorrelation on investment returns diminishes the apparent risk of such asset classes as the true returns/risk is easily \textbf{camouflaged} within a haze of illiquidity, stale prices, averaged price quotes and smoothed return reporting. We highlight the effect \emph{autocorrelation} and \emph{drawdown} has on performance analysis by investigating the results of functions developed during the Google Summer of Code 2013 on \textbf{commodity based index} .
-\end{abstract}
-
-<<echo=FALSE >>=
-library(PerformanceAnalytics)
-library(noniid.sm)
-data(edhec)
-@
-
-
-\section{Background}
-The investigated fund index that tracks a basket of \emph{commodities} to measure their performance.The value of these indexes fluctuates based on their underlying commodities, and this value depends on the \emph{component}, \emph{methodology} and \emph{style} to cover commodity markets .
-
-A brief overview of the four index invested in our report are :
- \begin{itemize}
- \item
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 3016
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