[Returnanalytics-commits] r2695 - pkg/FactorAnalytics/R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Aug 2 02:09:21 CEST 2013
Author: chenyian
Date: 2013-08-02 02:09:21 +0200 (Fri, 02 Aug 2013)
New Revision: 2695
Removed:
pkg/FactorAnalytics/R/bootstrapFactorESdecomposition.r
pkg/FactorAnalytics/R/bootstrapFactorVaRdecomposition.r
pkg/FactorAnalytics/R/chart.RollingStyle.R
pkg/FactorAnalytics/R/chart.Style.R
Modified:
pkg/FactorAnalytics/R/
Log:
ignore function that haven't been reviewed yet.
Property changes on: pkg/FactorAnalytics/R
___________________________________________________________________
Modified: svn:ignore
- covEWMA.R
plot.MacroFactorModel.r
print.MacroFactorModel.r
summary.MacroFactorModel.r
+ bootstrapFactorESdecomposition.r
bootstrapFactorVaRdecomposition.r
chart.RollingStyle.R
chart.Style.R
covEWMA.R
plot.MacroFactorModel.r
print.MacroFactorModel.r
summary.MacroFactorModel.r
Deleted: pkg/FactorAnalytics/R/bootstrapFactorESdecomposition.r
===================================================================
--- pkg/FactorAnalytics/R/bootstrapFactorESdecomposition.r 2013-08-01 23:13:25 UTC (rev 2694)
+++ pkg/FactorAnalytics/R/bootstrapFactorESdecomposition.r 2013-08-02 00:09:21 UTC (rev 2695)
@@ -1,83 +0,0 @@
-bootstrapFactorESdecomposition <- function(bootData, beta.vec, sig2.e, tail.prob = 0.01,
- method=c("average"),
- VaR.method=c("HS", "CornishFisher")) {
-## Compute factor model ES decomposition based on Euler's theorem given bootstrap data
-## and factor model parameters.
-## The partial derivative of ES wrt factor beta is computed
-## as the expected factor return given fund return is less than or equal to portfolio VaR
-## VaR is compute either as the sample quantile or as an estimated quantile
-## using the Cornish-Fisher expansion
-## inputs:
-## bootData B x (k+2) matrix of bootstrap data. First column contains the fund returns,
-## second through k+1 columns contain factor returns, k+2 column contain residuals
-## scaled to have variance 1.
-## beta.vec k x 1 vector of factor betas
-## sig2.e scalar, residual variance from factor model
-## tail.prob scalar tail probability
-## method character, method for computing marginal ES. Valid choices are
-## "average" for approximating E[Fj | R<=VaR]
-## VaR.method character, method for computing VaR. Valid choices are "HS" for
-## historical simulation (empirical quantile); "CornishFisher" for
-## modified VaR based on Cornish-Fisher quantile estimate. Cornish-Fisher
-## computation is done with the VaR.CornishFisher in the PerformanceAnalytics
-## package
-## output:
-## Output:
-## A list with the following components:
-## ES.fm scalar, bootstrap ES value for fund reported as a positive number
-## mcES.fm k+1 x 1 vector of factor marginal contributions to ES
-## cES.fm k+1 x 1 vector of factor component contributions to ES
-## pcES.fm k+1 x 1 vector of factor percent contributions to ES
-## Remarks:
-## The factor model has the form
-## R(t) = beta'F(t) + e(t) = beta.star'F.star(t)
-## where beta.star = (beta, sig.e)' and F.star(t) = (F(t)', z(t))'
-## By Euler's theorem
-## ES.fm = sum(cES.fm) = sum(beta.star*mcES.fm)
-## References:
-## 1. Hallerback (2003), "Decomposing Portfolio Value-at-Risk: A General Analysis",
-## The Journal of Risk 5/2.
-## 2. Yamai and Yoshiba (2002). "Comparative Analyses of Expected Shortfall and
-## Value-at-Risk: Their Estimation Error, Decomposition, and Optimization
-## Bank of Japan.
-## 3. Meucci (2007). "Risk Contributions from Generic User-Defined Factors," Risk.
- require(PerformanceAnalytics)
- VaR.method = VaR.method[1]
- bootData = as.matrix(bootData)
- ncol.bootData = ncol(bootData)
- beta.names = c(names(beta.vec), "residual")
- #beta.vec = as.vector(beta.vec)
- beta.star.vec = c(beta.vec, sqrt(sig2.e))
- names(beta.star.vec) = beta.names
-
- if (VaR.method == "HS") {
- VaR.fm = quantile(bootData[, 1], prob=tail.prob)
- idx = which(bootData[, 1] <= VaR.fm)
- ES.fm = -mean(bootData[idx, 1])
- } else {
- VaR.fm = -VaR.CornishFisher(bootData[, 1], p=(1-tail.prob))
- idx = which(bootData[, 1] <= pVaR)
- ES.fm = -mean(bootData[idx, 1])
- }
- ##
- ## compute marginal contribution to ES
- ##
- if (method == "average") {
- ## compute marginal ES as expected value of factor return given fund
- ## return is less than or equal to VaR
- mcES.fm = -as.matrix(colMeans(bootData[idx, -1]))
- } else {
- stop("invalid method")
- }
-
-## compute correction factor so that sum of weighted marginal ES adds to portfolio ES
-#cf = as.numeric( ES.fm / sum(mcES.fm*beta.star.vec) )
-#mcES.fm = cf*mcES.fm
-cES.fm = mcES.fm*beta.star.vec
-pcES.fm = cES.fm/ES.fm
-ans = list(ES.fm = ES.fm,
- mcES.fm = mcES.fm,
- cES.fm = cES.fm,
- pcES.fm = pcES.fm)
-return(ans)
-}
Deleted: pkg/FactorAnalytics/R/bootstrapFactorVaRdecomposition.r
===================================================================
--- pkg/FactorAnalytics/R/bootstrapFactorVaRdecomposition.r 2013-08-01 23:13:25 UTC (rev 2694)
+++ pkg/FactorAnalytics/R/bootstrapFactorVaRdecomposition.r 2013-08-02 00:09:21 UTC (rev 2695)
@@ -1,90 +0,0 @@
-bootstrapFactorVaRdecomposition <- function(bootData, beta.vec, sig2.e, h=NULL, tail.prob = 0.01,
- method=c("average"),
- VaR.method=c("HS", "CornishFisher")) {
-## Compute factor model VaR decomposition based on Euler's theorem given bootstrap data
-## and factor model parameters.
-## The partial derivative of VaR wrt factor beta is computed
-## as the expected factor return given fund return is equal to portfolio VaR
-## VaR is compute either as the sample quantile or as an estimated quantile
-## using the Cornish-Fisher expansion
-## inputs:
-## bootData B x (k+2) matrix of bootstrap data. First column contains the fund returns,
-## second through k+1 columns contain factor returns, k+2 column contain residuals
-## scaled to have variance 1.
-## beta.vec k x 1 vector of factor betas
-## sig2.e scalar, residual variance from factor model
-## h integer, number of obvs on each side of VaR. Default is h=round(sqrt(B)/2)
-## tail.prob scalar tail probability
-## method character, method for computing marginal VaR. Valid choices are
-## "average" for approximating E[Fj | R=VaR]
-## VaR.method character, method for computing VaR. Valid choices are "HS" for
-## historical simulation (empirical quantile); "CornishFisher" for
-## modified VaR based on Cornish-Fisher quantile estimate. Cornish-Fisher
-## computation is done with the VaR.CornishFisher in the PerformanceAnalytics
-## package
-## output:
-## Output:
-## A list with the following components:
-## VaR.fm scalar, bootstrap VaR value for fund reported as a positive number
-## mcVaR.fm k+1 x 1 vector of factor marginal contributions to VaR
-## cVaR.fm k+1 x 1 vector of factor component contributions to VaR
-## pcVaR.fm k+1 x 1 vector of factor percent contributions to VaR
-## Remarks:
-## The factor model has the form
-## R(t) = beta'F(t) + e(t) = beta.star'F.star(t)
-## where beta.star = (beta, sig.e)' and F.star(t) = (F(t)', z(t))'
-## By Euler's theorem
-## VaR.fm = sum(cVaR.fm) = sum(beta.star*mcVaR.fm)
-## References:
-## 1. Hallerback (2003), "Decomposing Portfolio Value-at-Risk: A General Analysis",
-## The Journal of Risk 5/2.
-## 2. Yamai and Yoshiba (2002). "Comparative Analyses of Expected Shortfall and
-## Value-at-Risk: Their Estimation Error, Decomposition, and Optimization
-## Bank of Japan.
-## 3. Meucci (2007). "Risk Contributions from Generic User-Defined Factors," Risk.
- require(PerformanceAnalytics)
- VaR.method = VaR.method[1]
- bootData = as.matrix(bootData)
- ncol.bootData = ncol(bootData)
- beta.names = c(names(beta.vec), "residual")
- #beta.vec = as.vector(beta.vec)
- beta.star.vec = c(beta.vec, sqrt(sig2.e))
- names(beta.star.vec) = beta.names
-
- # determine number of obvs to average around VaR
- if (is.null(h)) {
- h = round(sqrt(nrow(bootData)))
- } else h = round(h)
-
- if (VaR.method == "HS") {
- VaR.fm = -quantile(bootData[,1], prob=tail.prob)
- } else {
- VaR.fm = VaR.CornishFisher(bootData[,1], p=(1-tail.prob))
- }
- ##
- ## compute marginal contribution to VaR
- ##
- if (method == "average") {
- ## compute marginal VaR as expected value of fund return given portfolio
- ## return is equal to portfolio VaR
- r.sort = sort(bootData[,1])
- idx.lower = which(r.sort <= -VaR.fm)
- idx.upper = which(r.sort > -VaR.fm)
- r.vals = c(r.sort[tail(idx.lower,n=h)], r.sort[head(idx.upper,n=h)])
- idx = which(bootData[,1] %in% r.vals)
- mcVaR.fm = -as.matrix(colMeans(bootData[idx,-1]))
- } else {
- stop("invalid method")
- }
-
-## compute correction factor so that sum of weighted marginal VaR adds to portfolio VaR
-cf = as.numeric( VaR.fm / sum(mcVaR.fm*beta.star.vec) )
-mcVaR.fm = cf*mcVaR.fm
-cVaR.fm = mcVaR.fm*beta.star.vec
-pcVaR.fm = cVaR.fm/VaR.fm
-ans = list(VaR.fm = VaR.fm,
- mcVaR.fm = mcVaR.fm,
- cVaR.fm = cVaR.fm,
- pcVaR.fm = pcVaR.fm)
-return(ans)
-}
Deleted: pkg/FactorAnalytics/R/chart.RollingStyle.R
===================================================================
--- pkg/FactorAnalytics/R/chart.RollingStyle.R 2013-08-01 23:13:25 UTC (rev 2694)
+++ pkg/FactorAnalytics/R/chart.RollingStyle.R 2013-08-02 00:09:21 UTC (rev 2695)
@@ -1,52 +0,0 @@
-chart.RollingStyle <-
-function (R.fund, R.style, method = c("constrained","unconstrained","normalized"), leverage = FALSE, width = 12, main = NULL, space = 0, ...)
-{ # @author Peter Carl
-
- result<-table.RollingStyle(R.fund=R.fund, R.style=R.style, method=method,leverage=leverage,width=width)
-
- if (is.null(main)){
- freq = periodicity(R.fund)
-
- switch(freq$scale,
- minute = {freq.lab = "minute"},
- hourly = {freq.lab = "hour"},
- daily = {freq.lab = "day"},
- weekly = {freq.lab = "week"},
- monthly = {freq.lab = "month"},
- quarterly = {freq.lab = "quarter"},
- yearly = {freq.lab = "year"}
- )
-
- main = paste(colnames(R.fund)[1]," Rolling ", width ,"-",freq.lab," Style Weights", sep="")
- }
-
- chart.StackedBar(result, main = main, space = space, ...)
-
-}
-
-###############################################################################
-# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
-#
-# Copyright (c) 2004-2007 Peter Carl and Brian G. Peterson
-#
-# This library is distributed under the terms of the GNU Public License (GPL)
-# for full details see the file COPYING
-#
-# $Id$
-#
-###############################################################################
-# $Log: not supported by cvs2svn $
-# Revision 1.4 2009-10-15 21:50:19 brian
-# - updates to add automatic periodicity to chart labels, and support different frequency data
-#
-# Revision 1.3 2008-07-11 03:22:01 peter
-# - removed unnecessary function attributes
-#
-# Revision 1.2 2008-04-18 03:59:52 peter
-# - added na.omit to avoid problems with missing data
-#
-# Revision 1.1 2008/02/23 05:55:21 peter
-# - chart demonstrating fund exposures through time
-#
-#
-###############################################################################
Deleted: pkg/FactorAnalytics/R/chart.Style.R
===================================================================
--- pkg/FactorAnalytics/R/chart.Style.R 2013-08-01 23:13:25 UTC (rev 2694)
+++ pkg/FactorAnalytics/R/chart.Style.R 2013-08-02 00:09:21 UTC (rev 2695)
@@ -1,195 +0,0 @@
-#' calculate and display effective style weights
-#'
-#' Functions that calculate effective style weights and display the results in
-#' a bar chart. \code{chart.Style} calculates and displays style weights
-#' calculated over a single period. \code{chart.RollingStyle} calculates and
-#' displays those weights in rolling windows through time. \code{style.fit}
-#' manages the calculation of the weights by method. \code{style.QPfit}
-#' calculates the specific constraint case that requires quadratic programming.
-#'
-#' These functions calculate style weights using an asset class style model as
-#' described in detail in Sharpe (1992). The use of quadratic programming to
-#' determine a fund's exposures to the changes in returns of major asset
-#' classes is usually refered to as "style analysis".
-#'
-#' The "unconstrained" method implements a simple factor model for style
-#' analysis, as in: \deqn{Ri = bi1*F1+bi2*F2+...+bin*Fn+ei}{R_i =
-#' b_{i1}F_1+b_{i2}F_2+\dots+b_{in}F_n +e_i} where \eqn{Ri}{R_i} represents the
-#' return on asset i, \eqn{Fj}{F_j} represents each factor, and \eqn{ei}{e_i}
-#' represents the "non-factor" component of the return on i. This is simply a
-#' multiple regression analysis with fund returns as the dependent variable and
-#' asset class returns as the independent variables. The resulting slope
-#' coefficients are then interpreted as the fund's historic exposures to asset
-#' class returns. In this case, coefficients do not sum to 1.
-#'
-#' The "normalized" method reports the results of a multiple regression
-#' analysis similar to the first, but with one constraint: the coefficients are
-#' required to add to 1. Coefficients may be negative, indicating short
-#' exposures. To enforce the constraint, coefficients are normalized.
-#'
-#' The "constrained" method includes the constraint that the coefficients sum
-#' to 1, but adds that the coefficients must lie between 0 and 1. These
-#' inequality constraints require a quadratic programming algorithm using
-#' \code{\link[quadprog]{solve.QP}} from the 'quadprog' package, and the
-#' implementation is discussed under \code{\link{style.QPfit}}. If set to
-#' TRUE, "leverage" allows the sum of the coefficients to exceed 1.
-#'
-#' According to Sharpe (1992), the calculation for the constrained case is
-#' represented as: \deqn{min var(Rf - sum[wi * R.si]) = min var(F - w*S)}{min
-#' \sigma(R_f - \sum{w_i * R_s_i}) = min \sigma(F - w*S)} \deqn{s.t. sum[wi] =
-#' 1; wi > 0}{ s.t. \sum{w_i} = 1; w_i > 0}
-#'
-#' Remembering that:
-#'
-#' \deqn{\sigma(aX + bY) = a^2 \sigma(X) + b^2 \sigma(Y) + 2ab cov(X,Y) =
-#' \sigma(R.f) + w'*V*w - 2*w'*cov(R.f,R.s)}
-#'
-#' we can drop \eqn{var(Rf)}{\sigma(R_f)} as it isn't a function of weights,
-#' multiply both sides by 1/2:
-#'
-#' \deqn{= min (1/2) w'*V*w - C'w}{= min (1/2) w'*V*w - C'w} \deqn{ s.t. w'*e =
-#' 1, w_i > 0}{ s.t. w'*e = 1, w_i > 0}
-#'
-#' Which allows us to use \code{\link[quadprog]{solve.QP}}, which is specified
-#' as: \deqn{min(-d' b + 1/2 b' D b)}{min(-d' b + 1/2 b' D b)} and the
-#' constraints \deqn{ A' b >= b.0 }{ A' b >= b_0 }
-#'
-#' so: b is the weight vector, D is the variance-covariance matrix of the
-#' styles d is the covariance vector between the fund and the styles
-#'
-#' The chart functions then provide a graphical summary of the results. The
-#' underlying function, \code{\link{style.fit}}, provides the outputs of the
-#' analysis and more information about fit, including an R-squared value.
-#'
-#' Styles identified in this analysis may be interpreted as an average of
-#' potentially changing exposures over the period covered. The function
-#' \code{\link{chart.RollingStyle}} may be useful for examining the behavior of
-#' a manager's average exposures to asset classes over time, using a
-#' rolling-window analysis.
-#'
-#' The chart functions plot a column chart or stacked column chart of the
-#' resulting style weights to the current device. Both \code{style.fit} and
-#' \code{style.QPfit} produce a list of data frames containing 'weights' and
-#' 'R.squared' results. If 'model' = TRUE in \code{style.QPfit}, the full
-#' result set is shown from the output of \code{solve.QP}.
-#'
-#' @aliases chart.Style chart.RollingStyle table.RollingStyle style.fit
-#' style.QPfit
-#' @param R.fund matrix, data frame, or zoo object with fund returns to be
-#' analyzed
-#' @param R.style matrix, data frame, or zoo object with style index returns.
-#' Data object must be of the same length and time-aligned with R.fund
-#' @param method specify the method of calculation of style weights as
-#' "constrained", "unconstrained", or "normalized". For more information, see
-#' \code{\link{style.fit}}
-#' @param leverage logical, defaults to 'FALSE'. If 'TRUE', the calculation of
-#' weights assumes that leverage may be used. For more information, see
-#' \code{\link{style.fit}}
-#' @param model logical. If 'model' = TRUE in \code{\link{style.QPfit}}, the
-#' full result set is shown from the output of \code{solve.QP}.
-#' @param selection either "none" (default) or "AIC". If "AIC", then the
-#' function uses a stepwise regression to identify find the model with minimum
-#' AIC value. See \code{\link{step}} for more detail.
-#' @param unstacked logical. If set to 'TRUE' \emph{and} only one row of data
-#' is submitted in 'w', then the chart creates a normal column chart. If more
-#' than one row is submitted, then this is ignored. See examples below.
-#' @param space the amount of space (as a fraction of the average bar width)
-#' left before each bar, as in \code{\link{barplot}}. Default for
-#' \code{chart.RollingStyle} is 0; for \code{chart.Style} the default is 0.2.
-#' @param main set the chart title, same as in \code{\link{plot}}
-#' @param width number of periods or window to apply rolling style analysis
-#' over
-#' @param ylim set the y-axis limit, same as in \code{\link{plot}}
-#' @param \dots for the charting functions, these are arguments to be passed to
-#' \code{\link{barplot}}. These can include further arguments (such as 'axes',
-#' 'asp' and 'main') and graphical parameters (see 'par') which are passed to
-#' 'plot.window()', 'title()' and 'axis'. For the calculation functions, these
-#' are ignored.
-#' @note None of the functions \code{chart.Style}, \code{style.fit}, and
-#' \code{style.QPfit} make any attempt to align the two input data series. The
-#' \code{chart.RollingStyle}, on the other hand, does merge the two series and
-#' manages the calculation over common periods.
-#' @author Peter Carl
-#' @seealso \code{\link{barplot}}, \code{\link{par}}
-#' @references Sharpe, W. Asset Allocation: Management Style and Performance
-#' Measurement Journal of Portfolio Management, 1992, 7-19. See \url{
-#' http://www.stanford.edu/~wfsharpe/art/sa/sa.htm}
-#' @keywords ts multivariate hplot
-#' @examples
-#'
-#' data(edhec)
-#' data(managers)
-#' style.fit(managers[97:132,2,drop=FALSE],edhec[85:120,], method="constrained", leverage=FALSE)
-#' chart.Style(managers[97:132,2,drop=FALSE],edhec[85:120,], method="constrained", leverage=FALSE, unstack=TRUE, las=3)
-#' chart.RollingStyle(managers[,2,drop=FALSE],edhec[,1:11], method="constrained", leverage=FALSE, width=36, cex.legend = .7, colorset=rainbow12equal, las=1)
-#'
-`chart.Style` <-
-function (R.fund, R.style, method = c("constrained", "unconstrained", "normalized"), leverage = FALSE, main = NULL, ylim = NULL, unstacked=TRUE, ...)
-{ # @author Peter Carl
-
- # DESCRIPTION:
- # A wrapper to create a chart of relative returns through time
-
- # R-Squared could deliver adjusted R-Squared if we wanted
-
- # FUNCTION:
-
- # Transform input data to a data frame
- R.fund = checkData(R.fund)
- R.style = checkData(R.style)
- method = method[1]
-
- # Calculate
- result = style.fit(R.fund, R.style, method = method, leverage = leverage)
- weights = t(as.matrix(result$weights))
-
- if(is.null(main))
- main = paste(colnames(R.fund)[1] ," Style Weights", sep="")
-
- if(is.null(ylim))
- if(method == "constrained" & leverage == FALSE) ylim = c(0,1)
- else ylim = NULL
-
- chart.StackedBar(weights, main = main, ylim = ylim, unstacked = unstacked, ...)
-# barplot(weights, main = main, ylim = ylim, ...)
-
-}
-
-###############################################################################
-# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
-#
-# Copyright (c) 2004-2007 Peter Carl and Brian G. Peterson
-#
-# This library is distributed under the terms of the GNU Public License (GPL)
-# for full details see the file COPYING
-#
-# $Id$
-#
-###############################################################################
-# $Log: not supported by cvs2svn $
-# Revision 1.7 2008-07-11 03:24:52 peter
-# - fixed error with alignment of results
-#
-# Revision 1.6 2008-04-18 03:58:04 peter
-# - reduced to a wrapper to chart.StackedBar
-#
-# Revision 1.5 2008/02/27 04:05:32 peter
-# - added 'leverage' tag to eliminate sum to one constraint
-# - added cex.names for controlling size of xaxis labels
-#
-# Revision 1.4 2008/02/26 04:49:06 peter
-# - handles single column fits better
-#
-# Revision 1.3 2008/02/26 04:39:40 peter
-# - moved legend and margin control into chart.StackedBar
-# - handles multiple columns
-#
-# Revision 1.2 2008/02/23 05:35:56 peter
-# - set ylim more sensibly depending on method
-#
-# Revision 1.1 2008/02/23 05:32:37 peter
-# - simple bar chart of a fund's exposures to a set of factors, as determined
-# by style.fit
-#
-#
-###############################################################################
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