[Returnanalytics-commits] r3593 - in pkg/FactorAnalytics: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Feb 3 03:56:12 CET 2015
Author: chenyian
Date: 2015-02-03 03:56:12 +0100 (Tue, 03 Feb 2015)
New Revision: 3593
Added:
pkg/FactorAnalytics/R/fitTsfmLagBeta.r
pkg/FactorAnalytics/man/fitTsfmLagBeta.Rd
Modified:
pkg/FactorAnalytics/NAMESPACE
Log:
Add a new function fitTsfmLagBeta.r to estimate lagged Betas time series factor model.
Modified: pkg/FactorAnalytics/NAMESPACE
===================================================================
--- pkg/FactorAnalytics/NAMESPACE 2015-02-03 02:23:52 UTC (rev 3592)
+++ pkg/FactorAnalytics/NAMESPACE 2015-02-03 02:56:12 UTC (rev 3593)
@@ -31,6 +31,7 @@
export(fitSfm)
export(fitTsfm)
export(fitTsfmTiming)
+export(fitTsfmLagBeta)
export(fmCov)
export(fmEsDecomp)
export(fmSdDecomp)
Added: pkg/FactorAnalytics/R/fitTsfmLagBeta.r
===================================================================
--- pkg/FactorAnalytics/R/fitTsfmLagBeta.r (rev 0)
+++ pkg/FactorAnalytics/R/fitTsfmLagBeta.r 2015-02-03 02:56:12 UTC (rev 3593)
@@ -0,0 +1,183 @@
+#' @title Fit a lagged Betas factor model using time series regression
+#'
+#' @description This is a wrapper function to fits a time series lagged Betas factor model for one
+#' or more asset returns or excess returns using time series regression.
+#' Users can choose between ordinary least squares-OLS, discounted least
+#' squares-DLS (or) robust regression. Several variable selection options
+#' including Stepwise, Subsets, Lars are available as well. An object of class
+#' \code{"tsfm"} is returned.
+#'
+#' @details
+#' Typically, factor models are fit using excess returns. \code{rf.name} gives
+#' the option to supply a risk free rate variable to subtract from each asset
+#' return and factor to compute excess returns.
+#'
+#' Estimation method "OLS" corresponds to ordinary least squares using
+#' \code{\link[stats]{lm}}, "DLS" is discounted least squares (weighted least
+#' squares with exponentially declining weights that sum to unity), and,
+#' "Robust" is robust regression (using \code{\link[robust]{lmRob}}).
+#'
+#' If \code{variable.selection="none"}, uses all the factors and performs no
+#' variable selection. Whereas, "stepwise" performs traditional stepwise
+#' LS or Robust regression (using \code{\link[stats]{step}} or
+#' \code{\link[robust]{step.lmRob}}), that starts from the initial set of
+#' factors and adds/subtracts factors only if the regression fit, as measured
+#' by the Bayesian Information Criterion (BIC) or Akaike Information Criterion
+#' (AIC), improves. And, "subsets" enables subsets selection using
+#' \code{\link[leaps]{regsubsets}}; chooses the best performing subset of any
+#' given size or within a range of subset sizes. Different methods such as
+#' exhaustive search (default), forward or backward stepwise, or sequential
+#' replacement can be employed.See \code{\link{fitTsfm.control}} for more
+#' details on the control arguments.
+#'
+#' \code{variable.selection="lars"} corresponds to least angle regression
+#' using \code{\link[lars]{lars}} with variants "lasso" (default), "lar",
+#' "stepwise" or "forward.stagewise". Note: If \code{variable.selection="lars"},
+#' \code{fit.method} will be ignored.
+#'
+#' Market timing accounts for
+#' the price movement of the general stock market relative to fixed income
+#' securities. It includes
+#' $down.market = max(0, R_f-R_m)$ as a factor, following Henriksson & Merton
+#' (1981). The coefficient of this down-market factor can be interpreted as the
+#' number of "free" put options on the market provided by the manager's
+#' market-timings kills.
+#'
+#' \subsection{Data Processing}{
+#'
+#' Note about NAs: Before model fitting, incomplete cases are removed for
+#' every asset (return data combined with respective factors' return data)
+#' using \code{\link[stats]{na.omit}}. Otherwise, all observations in
+#' \code{data} are included.
+#'
+#' Note about \code{asset.names} and \code{factor.names}: Spaces in column
+#' names of \code{data} will be converted to periods as \code{fitTsfm} works
+#' with \code{xts} objects internally and colnames won't be left as they are.
+#' }
+#'
+#' @param asset.names vector containing names of assets, whose returns or
+#' excess returns are the dependent variable.
+#' @param factor.names vector containing names of the macroeconomic factors.
+#' @param mkt.name name of the column for market excess returns (Rm-Rf); this
+#' is necessary to add market timing factors. Default is NULL.
+#' @param rf.name name of the column of risk free rate variable to calculate
+#' excess returns for all assets (in \code{asset.names}) and factors (in
+#' \code{factor.names}). Default is NULL, and no action is taken.
+#' @param LagBeta A integer number to specify numbers of lags of Betas to
+#' include in the model. The Default is 1.
+#' @param data vector, matrix, data.frame, xts, timeSeries or zoo object
+#' containing column(s) named in \code{asset.names}, \code{factor.names} and
+#' optionally, \code{mkt.name} and \code{rf.name}.
+#' @param fit.method the estimation method, one of "OLS", "DLS" or "Robust".
+#' See details. Default is "OLS".
+#' @param variable.selection the variable selection method, one of "none",
+#' "stepwise","subsets","lars". See details. Default is "none".
+#' \code{mkt.name} is required if any of these options are to be implemented.
+#' @param control list of control parameters. The default is constructed by
+#' the function \code{\link{fitTsfm.control}}. See the documentation for
+#' \code{\link{fitTsfm.control}} for details.
+#' @param ... arguments passed to \code{\link{fitTsfm.control}}
+#'
+#' @return fitTsfm returns an object of class \code{"tsfm"} for which
+#' \code{print}, \code{plot}, \code{predict} and \code{summary} methods exist.
+#'
+#' The generic accessor functions \code{coef}, \code{fitted} and
+#' \code{residuals} extract various useful features of the fit object.
+#' Additionally, \code{fmCov} computes the covariance matrix for asset returns
+#' based on the fitted factor model
+#'
+#' An object of class \code{"tsfm"} is a list containing the following
+#' components:
+#' \item{asset.fit}{list of fitted objects for each asset. Each object is of
+#' class \code{lm} if \code{fit.method="OLS" or "DLS"}, class \code{lmRob} if
+#' the \code{fit.method="Robust"}, or class \code{lars} if
+#' \code{variable.selection="lars"}.}
+#' \item{alpha}{length-N vector of estimated alphas.}
+#' \item{beta}{N x K matrix of estimated betas.}
+#' \item{r2}{length-N vector of R-squared values.}
+#' \item{resid.sd}{length-N vector of residual standard deviations.}
+#' \item{fitted}{xts data object of fitted values; iff
+#' \code{variable.selection="lars"}}
+#' \item{call}{the matched function call.}
+#' \item{data}{xts data object containing the assets and factors.}
+#' \item{asset.names}{asset.names as input.}
+#' \item{factor.names}{factor.names as input.}
+#' \item{fit.method}{fit.method as input.}
+#' \item{variable.selection}{variable.selection as input.}
+#' Where N is the number of assets, K is the number of factors and T is the
+#' number of time periods.
+#'
+#' @author Yi-An Chen.
+#'
+#' @references
+#' Christopherson, J. A., Carino, D. R., & Ferson, W. E. (2009). Portfolio
+#' performance measurement and benchmarking. McGraw Hill Professional.
+#'
+#' Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle
+#' regression. The Annals of statistics, 32(2), 407-499.
+#'
+#' Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., &
+#' Tibshirani, R. (2009). The elements of statistical learning (Vol. 2, No. 1).
+#' New York: Springer.
+#'
+#' Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment
+#' performance. II. Statistical procedures for evaluating forecasting skills.
+#' Journal of business, 513-533.
+#'
+#' Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market.
+#' Harvard business review, 44(4), 131-136.
+#'
+#' @seealso The \code{tsfm} methods for generic functions:
+#' \code{\link{plot.tsfm}}, \code{\link{predict.tsfm}},
+#' \code{\link{print.tsfm}} and \code{\link{summary.tsfm}}.
+#'
+#' And, the following extractor functions: \code{\link[stats]{coef}},
+#' \code{\link[stats]{fitted}}, \code{\link[stats]{residuals}},
+#' \code{\link{fmCov}}, \code{\link{fmSdDecomp}}, \code{\link{fmVaRDecomp}}
+#' and \code{\link{fmEsDecomp}}.
+#'
+#' \code{\link{paFm}} for Performance Attribution.
+#'
+#' @examples
+#' # load data from the database
+#' data(managers)
+#'
+#' # example: Market-timing factors with robust fit
+#' fit <- fitTsfmLagBeta(asset.names=colnames(managers[,(1:6)]), LagBeta=2,
+#' mkt.name="SP500.TR",rf.name="US.3m.TR",data=managers)
+#' summary(fit)
+#' fitted(fit)
+#'
+#' @importFrom PerformanceAnalytics checkData
+#' @importFrom robust lmRob step.lmRob
+#' @importFrom leaps regsubsets
+#' @importFrom lars lars cv.lars
+#'
+#' @export
+
+fitTsfmLagBeta <- function(asset.names, factor.names=NULL, mkt.name=NULL, rf.name=NULL,
+ data=data, fit.method=c("OLS","DLS","Robust"),LagBeta=1,
+ variable.selection=c("none","stepwise","subsets","lars"), control=fitTsfm.control(...),...) {
+
+ if (is.null(mkt.name)) {
+ stop("Missing argument: mkt.name has to be specified for lag Beta model.")
+ }
+
+
+ if (as.integer(LagBeta) != LagBeta | LagBeta < 1 ) {
+ stop("Invalid argument: LagBeta must be an integer and no less than 1. The default is 1.")
+ }
+
+ # Create market lag terms
+ mktlag <- lag(data[,mkt.name],k=seq(1:LagBeta))
+ for (i in 1:LagBeta) {
+ colnames(mktlag)[i] <- paste("MktLag",i,sep="")
+ factor.names <- c(factor.names,paste("MktLag",i,sep=""))
+ }
+ data <- merge(data,mktlag)
+
+ fit <- fitTsfm(asset.names=asset.names,factor.names=factor.names,mkt.name=mkt.name,rf.name=rf.name,
+ data=data,fit.method=fit.method,variable.selection=variable.selection,control=control)
+
+ return(fit)
+}
\ No newline at end of file
Added: pkg/FactorAnalytics/man/fitTsfmLagBeta.Rd
===================================================================
--- pkg/FactorAnalytics/man/fitTsfmLagBeta.Rd (rev 0)
+++ pkg/FactorAnalytics/man/fitTsfmLagBeta.Rd 2015-02-03 02:56:12 UTC (rev 3593)
@@ -0,0 +1,174 @@
+% Generated by roxygen2 (4.1.0): do not edit by hand
+% Please edit documentation in R/fitTsfmLagBeta.r
+\name{fitTsfmLagBeta}
+\alias{fitTsfmLagBeta}
+\title{Fit a lagged Betas factor model using time series regression}
+\usage{
+fitTsfmLagBeta(asset.names, factor.names = NULL, mkt.name = NULL,
+ rf.name = NULL, data = data, fit.method = c("OLS", "DLS", "Robust"),
+ LagBeta = 1, variable.selection = c("none", "stepwise", "subsets",
+ "lars"), control = fitTsfm.control(...), ...)
+}
+\arguments{
+\item{asset.names}{vector containing names of assets, whose returns or
+excess returns are the dependent variable.}
+
+\item{factor.names}{vector containing names of the macroeconomic factors.}
+
+\item{mkt.name}{name of the column for market excess returns (Rm-Rf); this
+is necessary to add market timing factors. Default is NULL.}
+
+\item{rf.name}{name of the column of risk free rate variable to calculate
+excess returns for all assets (in \code{asset.names}) and factors (in
+\code{factor.names}). Default is NULL, and no action is taken.}
+
+\item{data}{vector, matrix, data.frame, xts, timeSeries or zoo object
+containing column(s) named in \code{asset.names}, \code{factor.names} and
+optionally, \code{mkt.name} and \code{rf.name}.}
+
+\item{fit.method}{the estimation method, one of "OLS", "DLS" or "Robust".
+See details. Default is "OLS".}
+
+\item{LagBeta}{A integer number to specify numbers of lags of Betas to
+include in the model. The Default is 1.}
+
+\item{variable.selection}{the variable selection method, one of "none",
+"stepwise","subsets","lars". See details. Default is "none".
+\code{mkt.name} is required if any of these options are to be implemented.}
+
+\item{control}{list of control parameters. The default is constructed by
+the function \code{\link{fitTsfm.control}}. See the documentation for
+\code{\link{fitTsfm.control}} for details.}
+
+\item{...}{arguments passed to \code{\link{fitTsfm.control}}}
+}
+\value{
+fitTsfm returns an object of class \code{"tsfm"} for which
+\code{print}, \code{plot}, \code{predict} and \code{summary} methods exist.
+
+The generic accessor functions \code{coef}, \code{fitted} and
+\code{residuals} extract various useful features of the fit object.
+Additionally, \code{fmCov} computes the covariance matrix for asset returns
+based on the fitted factor model
+
+An object of class \code{"tsfm"} is a list containing the following
+components:
+\item{asset.fit}{list of fitted objects for each asset. Each object is of
+class \code{lm} if \code{fit.method="OLS" or "DLS"}, class \code{lmRob} if
+the \code{fit.method="Robust"}, or class \code{lars} if
+\code{variable.selection="lars"}.}
+\item{alpha}{length-N vector of estimated alphas.}
+\item{beta}{N x K matrix of estimated betas.}
+\item{r2}{length-N vector of R-squared values.}
+\item{resid.sd}{length-N vector of residual standard deviations.}
+\item{fitted}{xts data object of fitted values; iff
+\code{variable.selection="lars"}}
+\item{call}{the matched function call.}
+\item{data}{xts data object containing the assets and factors.}
+\item{asset.names}{asset.names as input.}
+\item{factor.names}{factor.names as input.}
+\item{fit.method}{fit.method as input.}
+\item{variable.selection}{variable.selection as input.}
+Where N is the number of assets, K is the number of factors and T is the
+number of time periods.
+}
+\description{
+This is a wrapper function to fits a time series lagged Betas factor model for one
+or more asset returns or excess returns using time series regression.
+Users can choose between ordinary least squares-OLS, discounted least
+squares-DLS (or) robust regression. Several variable selection options
+including Stepwise, Subsets, Lars are available as well. An object of class
+\code{"tsfm"} is returned.
+}
+\details{
+Typically, factor models are fit using excess returns. \code{rf.name} gives
+the option to supply a risk free rate variable to subtract from each asset
+return and factor to compute excess returns.
+
+Estimation method "OLS" corresponds to ordinary least squares using
+\code{\link[stats]{lm}}, "DLS" is discounted least squares (weighted least
+squares with exponentially declining weights that sum to unity), and,
+"Robust" is robust regression (using \code{\link[robust]{lmRob}}).
+
+If \code{variable.selection="none"}, uses all the factors and performs no
+variable selection. Whereas, "stepwise" performs traditional stepwise
+LS or Robust regression (using \code{\link[stats]{step}} or
+\code{\link[robust]{step.lmRob}}), that starts from the initial set of
+factors and adds/subtracts factors only if the regression fit, as measured
+by the Bayesian Information Criterion (BIC) or Akaike Information Criterion
+(AIC), improves. And, "subsets" enables subsets selection using
+\code{\link[leaps]{regsubsets}}; chooses the best performing subset of any
+given size or within a range of subset sizes. Different methods such as
+exhaustive search (default), forward or backward stepwise, or sequential
+replacement can be employed.See \code{\link{fitTsfm.control}} for more
+details on the control arguments.
+
+\code{variable.selection="lars"} corresponds to least angle regression
+using \code{\link[lars]{lars}} with variants "lasso" (default), "lar",
+"stepwise" or "forward.stagewise". Note: If \code{variable.selection="lars"},
+\code{fit.method} will be ignored.
+
+Market timing accounts for
+the price movement of the general stock market relative to fixed income
+securities. It includes
+$down.market = max(0, R_f-R_m)$ as a factor, following Henriksson & Merton
+(1981). The coefficient of this down-market factor can be interpreted as the
+number of "free" put options on the market provided by the manager's
+market-timings kills.
+
+\subsection{Data Processing}{
+
+Note about NAs: Before model fitting, incomplete cases are removed for
+every asset (return data combined with respective factors' return data)
+using \code{\link[stats]{na.omit}}. Otherwise, all observations in
+\code{data} are included.
+
+Note about \code{asset.names} and \code{factor.names}: Spaces in column
+names of \code{data} will be converted to periods as \code{fitTsfm} works
+with \code{xts} objects internally and colnames won't be left as they are.
+}
+}
+\examples{
+# load data from the database
+data(managers)
+
+# example: Market-timing factors with robust fit
+fit <- fitTsfmLagBeta(asset.names=colnames(managers[,(1:6)]), LagBeta=2,
+ mkt.name="SP500.TR",rf.name="US.3m.TR",data=managers)
+summary(fit)
+fitted(fit)
+}
+\author{
+Yi-An Chen.
+}
+\references{
+Christopherson, J. A., Carino, D. R., & Ferson, W. E. (2009). Portfolio
+performance measurement and benchmarking. McGraw Hill Professional.
+
+Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle
+regression. The Annals of statistics, 32(2), 407-499.
+
+Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Friedman, J., &
+Tibshirani, R. (2009). The elements of statistical learning (Vol. 2, No. 1).
+New York: Springer.
+
+Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment
+performance. II. Statistical procedures for evaluating forecasting skills.
+Journal of business, 513-533.
+
+Treynor, J., & Mazuy, K. (1966). Can mutual funds outguess the market.
+Harvard business review, 44(4), 131-136.
+}
+\seealso{
+The \code{tsfm} methods for generic functions:
+\code{\link{plot.tsfm}}, \code{\link{predict.tsfm}},
+\code{\link{print.tsfm}} and \code{\link{summary.tsfm}}.
+
+And, the following extractor functions: \code{\link[stats]{coef}},
+\code{\link[stats]{fitted}}, \code{\link[stats]{residuals}},
+\code{\link{fmCov}}, \code{\link{fmSdDecomp}}, \code{\link{fmVaRDecomp}}
+and \code{\link{fmEsDecomp}}.
+
+\code{\link{paFm}} for Performance Attribution.
+}
+
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