[Returnanalytics-commits] r2708 - in pkg/FactorAnalytics: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sat Aug 3 00:32:44 CEST 2013
Author: chenyian
Date: 2013-08-03 00:32:44 +0200 (Sat, 03 Aug 2013)
New Revision: 2708
Modified:
pkg/FactorAnalytics/R/factorModelSdDecomposition.R
pkg/FactorAnalytics/man/factorModelEsDecomposition.Rd
pkg/FactorAnalytics/man/factorModelSdDecomposition.Rd
pkg/FactorAnalytics/man/factorModelVaRDecomposition.Rd
pkg/FactorAnalytics/man/plot.FundamentalFactorModel.Rd
pkg/FactorAnalytics/man/plot.StatFactorModel.Rd
pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd
Log:
modify .Rd file for the change of VaR.method
Modified: pkg/FactorAnalytics/R/factorModelSdDecomposition.R
===================================================================
--- pkg/FactorAnalytics/R/factorModelSdDecomposition.R 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/R/factorModelSdDecomposition.R 2013-08-02 22:32:44 UTC (rev 2708)
@@ -19,34 +19,22 @@
#' @examples
#'
#' # load data from the database
-#' data(managers.df)
-#' ret.assets = managers.df[,(1:6)]
-#' factors = managers.df[,(7:9)]
-#' # fit the factor model with OLS
-#' fit <- fitMacroeconomicFactorModel(ret.assets,factors,fit.method="OLS",
-#' variable.selection="all subsets",
-#' factor.set = 3)
-#' # factor SD decomposition for HAM1
-#' cov.factors = var(factors)
-#' manager.names = colnames(managers.df[,(1:6)])
-#' factor.names = colnames(managers.df[,(7:9)])
-#' factor.sd.decomp.HAM1 = factorModelSdDecomposition(fit$beta.mat["HAM1",],
-#' cov.factors, fit$residVars.vec["HAM1"])
+#' data("stat.fm.data")
+#' fit.stat <- fitStatisticalFactorModel(sfm.dat,k=2)
+#' cov.factors = var(fit.stat$factors)
+#' names = colnames(fit.stat$asset.ret)
+#' factor.sd.decomp.list = list()
+#' for (i in names) {
+#' factor.sd.decomp.list[[i]] =
+#' factorModelSdDecomposition(fit.stat$loadings[,i],
+#' cov.factors, fit.stat$resid.variance[i])
+#' }
#'
#' @export
#'
factorModelSdDecomposition <-
function(beta.vec, factor.cov, sig2.e) {
-## Inputs:
-## beta k x 1 vector of factor betas with factor names in the rownames
-## factor.cov k x k factor excess return covariance matrix
-## sig2.e scalar, residual variance from factor model (residVars.vec in fitFundamentalFactorModel)
-## Output:
-## A list with the following components:
-## sd.fm scalar, std dev based on factor model
-## mcr.fm k+1 x 1 vector of factor marginal contributions to risk (sd)
-## cr.fm k+1 x 1 vector of factor component contributions to risk (sd)
-## pcr.fm k+1 x 1 vector of factor percent contributions to risk (sd)
+
## Remarks:
## The factor model has the form
## R(t) = beta'F(t) + e(t) = beta.star'F.star(t)
Modified: pkg/FactorAnalytics/man/factorModelEsDecomposition.Rd
===================================================================
--- pkg/FactorAnalytics/man/factorModelEsDecomposition.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/factorModelEsDecomposition.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -4,7 +4,7 @@
\usage{
factorModelEsDecomposition(Data, beta.vec, sig2.e,
tail.prob = 0.05,
- VaR.method = c("HS", "CornishFisher"))
+ VaR.method = c("modified", "gaussian", "historical", "kernel"))
}
\arguments{
\item{Data}{\code{B x (k+2)} matrix of historic or
@@ -23,11 +23,10 @@
quantile. Typically 0.01 or 0.05.}
\item{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.}
+ Valid choices are one of
+ "modified","gaussian","historical", "kernel". computation
+ is done with the \code{VaR} in the PerformanceAnalytics
+ package. package.}
}
\value{
A list with the following components:
Modified: pkg/FactorAnalytics/man/factorModelSdDecomposition.Rd
===================================================================
--- pkg/FactorAnalytics/man/factorModelSdDecomposition.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/factorModelSdDecomposition.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -1,43 +1,40 @@
-\name{factorModelSdDecomposition}
-\alias{factorModelSdDecomposition}
-\title{Compute factor model factor risk (sd) decomposition for individual fund.}
-\usage{
- factorModelSdDecomposition(beta.vec, factor.cov, sig2.e)
-}
-\arguments{
- \item{beta.vec}{k x 1 vector of factor betas with factor
- names in the rownames.}
-
- \item{factor.cov}{k x k factor excess return covariance
- matrix.}
-
- \item{sig2.e}{scalar, residual variance from factor
- model.}
-}
-\value{
- an S3 object containing
-}
-\description{
- Compute factor model factor risk (sd) decomposition for
- individual fund.
-}
-\examples{
-# load data from the database
-data(managers.df)
-ret.assets = managers.df[,(1:6)]
-factors = managers.df[,(7:9)]
-# fit the factor model with OLS
-fit <- fitMacroeconomicFactorModel(ret.assets,factors,fit.method="OLS",
- variable.selection="all subsets",
- factor.set = 3)
-# factor SD decomposition for HAM1
-cov.factors = var(factors)
-manager.names = colnames(managers.df[,(1:6)])
-factor.names = colnames(managers.df[,(7:9)])
-factor.sd.decomp.HAM1 = factorModelSdDecomposition(fit$beta.mat["HAM1",],
- cov.factors, fit$residVars.vec["HAM1"])
-}
-\author{
- Eric Zivot and Yi-An Chen
-}
-
+\name{factorModelSdDecomposition}
+\alias{factorModelSdDecomposition}
+\title{Compute factor model factor risk (sd) decomposition for individual fund.}
+\usage{
+ factorModelSdDecomposition(beta.vec, factor.cov, sig2.e)
+}
+\arguments{
+ \item{beta.vec}{k x 1 vector of factor betas with factor
+ names in the rownames.}
+
+ \item{factor.cov}{k x k factor excess return covariance
+ matrix.}
+
+ \item{sig2.e}{scalar, residual variance from factor
+ model.}
+}
+\value{
+ an S3 object containing
+}
+\description{
+ Compute factor model factor risk (sd) decomposition for
+ individual fund.
+}
+\examples{
+# load data from the database
+data("stat.fm.data")
+fit.stat <- fitStatisticalFactorModel(sfm.dat,k=2)
+cov.factors = var(fit.stat$factors)
+names = colnames(fit.stat$asset.ret)
+factor.sd.decomp.list = list()
+for (i in names) {
+ factor.sd.decomp.list[[i]] =
+ factorModelSdDecomposition(fit.stat$loadings[,i],
+ cov.factors, fit.stat$resid.variance[i])
+}
+}
+\author{
+ Eric Zivot and Yi-An Chen
+}
+
Modified: pkg/FactorAnalytics/man/factorModelVaRDecomposition.Rd
===================================================================
--- pkg/FactorAnalytics/man/factorModelVaRDecomposition.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/factorModelVaRDecomposition.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -2,12 +2,12 @@
\alias{factorModelVaRDecomposition}
\title{Compute factor model factor VaR decomposition}
\usage{
- factorModelVaRDecomposition(bootData, beta.vec, sig2.e,
+ factorModelVaRDecomposition(Data, beta.vec, sig2.e,
tail.prob = 0.01,
- VaR.method = c("HS", "CornishFisher"))
+ VaR.method = c("modified", "gaussian", "historical", "kernel"))
}
\arguments{
- \item{bootData}{B x (k+2) matrix of bootstrap data. First
+ \item{Data}{B x (k+2) matrix of bootstrap data. First
column contains the fund returns, second through k+1
columns contain factor returns, (k+2)nd column contain
residuals scaled to have unit variance .}
@@ -20,11 +20,10 @@
\item{tail.prob}{scalar, tail probability}
\item{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.}
+ Valid choices are one of
+ "modified","gaussian","historical", "kernel". computation
+ is done with the \code{VaR} in the PerformanceAnalytics
+ package.}
}
\value{
an S3 object containing
@@ -55,8 +54,9 @@
tmpData = cbind(managers.df[,1],managers.df[,c("EDHEC.LS.EQ","SP500.TR")] ,
residuals(fit.macro$asset.fit$HAM1)/sqrt(fit.macro$resid.variance[1]))
colnames(tmpData)[c(1,4)] = c("HAM1", "residual")
-factor.VaR.decomp.HAM1 = factorModelEsDecomposition(tmpData, fit.macro$beta[1,],
- fit.macro$resid.variance[1], tail.prob=0.05)
+factor.VaR.decomp.HAM1 = factorModelVaRDecomposition(tmpData, fit.macro$beta[1,],
+ fit.macro$resid.variance[1], tail.prob=0.05,
+ VaR.method="historical)
}
\author{
Eric Zivot and Yi-An Chen
Modified: pkg/FactorAnalytics/man/plot.FundamentalFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/plot.FundamentalFactorModel.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/plot.FundamentalFactorModel.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -6,7 +6,7 @@
which.plot = c("none", "1L", "2L", "3L", "4L", "5L", "6L"),
max.show = 4, plot.single = FALSE, asset.name,
which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L", "8L", "9L"),
- legend.txt = TRUE, ...)
+ legend.txt = TRUE, VaR.method = "historical", ...)
}
\arguments{
\item{x}{fit object created by
@@ -40,6 +40,12 @@
\item{legend.txt}{Logical. TRUE will plot legend on
barplot. Defualt is \code{TRUE}.}
+ \item{VaR.method}{haracter, method for computing VaR.
+ Valid choices are one of
+ "modified","gaussian","historical", "kernel". computation
+ is done with the \code{VaR} in the PerformanceAnalytics
+ package. Default is "historical".}
+
\item{...}{other variables for barplot method.}
}
\description{
Modified: pkg/FactorAnalytics/man/plot.StatFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/plot.StatFactorModel.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/plot.StatFactorModel.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -8,7 +8,7 @@
hgrid = FALSE, vgrid = FALSE, plot.single = FALSE,
asset.name,
which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L", "8L", "9L", "10L", "11L", "12L", "13L"),
- max.show = 6, ...)
+ max.show = 6, VaR.method = "historical", ...)
}
\arguments{
\item{x}{fit object created by
@@ -60,6 +60,12 @@
\item{max.show}{Maximum assets to plot. Default is 6.}
+ \item{VaR.method}{haracter, method for computing VaR.
+ Valid choices are one of
+ "modified","gaussian","historical", "kernel". computation
+ is done with the \code{VaR} in the PerformanceAnalytics
+ package. Default is "historical".}
+
\item{...}{other variables for barplot method.}
}
\description{
Modified: pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd 2013-08-02 22:27:25 UTC (rev 2707)
+++ pkg/FactorAnalytics/man/plot.TimeSeriesFactorModel.Rd 2013-08-02 22:32:44 UTC (rev 2708)
@@ -6,7 +6,8 @@
colorset = c(1:12), legend.loc = NULL,
which.plot = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L"),
max.show = 6, plot.single = FALSE, asset.name,
- which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L", "8L", "9L", "10L", "11L", "12L", "13L"))
+ which.plot.single = c("none", "1L", "2L", "3L", "4L", "5L", "6L", "7L", "8L", "9L", "10L", "11L", "12L", "13L"),
+ VaR.method = "historical")
}
\arguments{
\item{x}{fit object created by fitTimeSeriesFactorModel.}
@@ -43,6 +44,12 @@
OLS residuals 12= CUSUM plot of recursive estimates
relative to full sample estimates 13= rolling estimates
over 24 month window}
+
+ \item{VaR.method}{haracter, method for computing VaR.
+ Valid choices are one of
+ "modified","gaussian","historical", "kernel". computation
+ is done with the \code{VaR} in the PerformanceAnalytics
+ package. Default is "historical".}
}
\description{
Generic function of plot method for
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