[Returnanalytics-commits] r2456 - in pkg/FactorAnalytics: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jun 28 00:39:32 CEST 2013
Author: chenyian
Date: 2013-06-28 00:39:32 +0200 (Fri, 28 Jun 2013)
New Revision: 2456
Added:
pkg/FactorAnalytics/R/summary.FundamentalFactorModel.r
pkg/FactorAnalytics/man/summary.FundamentalFactorModel.Rd
Modified:
pkg/FactorAnalytics/R/factorModelCovariance.r
pkg/FactorAnalytics/R/fitStatisticalFactorModel.R
Log:
1. modify factorModelCovariance.r
2. create skeleton of summary.FundamentalFactorModel.Rd and summary.FundamentalFactorModel.r
Modified: pkg/FactorAnalytics/R/factorModelCovariance.r
===================================================================
--- pkg/FactorAnalytics/R/factorModelCovariance.r 2013-06-27 22:14:44 UTC (rev 2455)
+++ pkg/FactorAnalytics/R/factorModelCovariance.r 2013-06-27 22:39:32 UTC (rev 2456)
@@ -1,61 +1,75 @@
-#' Compute Factor Model Covariance Matrix.
-#'
-#' Compute asset return covariance matrix from factor model parameters.
-#'
-#' The return on asset \code{i} (\code{i = 1,...,N}) is assumed to follow the
-#' factor model \cr \code{R(i,t) = alpha + t(beta)*F(t) + e(i,t), e(i,t) ~ iid
-#' (0, sig(i)^2)} \cr where \code{beta} is a \code{K x 1} vector of factor
-#' exposures. The return variance is then \cr \code{var(R(i,t) =
-#' t(beta)*var(F(t))*beta + sig(i)^2}, \cr and the \code{N x N} covariance
-#' matrix of the return vector \code{R} is \cr \code{var(R) = B*var(F(t))*t(B)
-#' + D} \cr where B is the \code{N x K} matrix of asset betas and \code{D} is a
-#' diagonal matrix with \code{sig(i)^2} values along the diagonal.
-#'
-#' @param beta.mat \code{N x K} matrix of factor betas, where \code{N} is the
-#' number of assets and \code{K} is the number of factors.
-#' @param factor.cov \code{K x K} factor return covariance matrix.
-#' @param residVars.vec \code{N x 1} vector of asset specific residual
-#' variances from the factor model.
-#' @return \code{N x N} return covariance matrix based on factor model
-#' parameters.
-#' @author Eric Zivot and Yi-An Chen.
-#' @references Zivot, E. and J. Wang (2006), \emph{Modeling Financial Time
-#' Series with S-PLUS, Second Edition}, Springer-Verlag.
-#' @examples
-#'
-#' # factorModelCovariance
-#' data(managers.df)
-#' factors = managers.df[,(7:9)]
-#' ret.assets = managers.df[,(1:6)]
-#' fit <-fitMacroeconomicFactorModel(ret.assets,factors,fit.method="OLS",
-#' variable.selection="all subsets", factor.set = 3)
-#' factorModelCovariance(fit$beta.mat,var(factors),fit$residVars.vec)
-#'
-factorModelCovariance <-
-function(beta.mat, factor.cov, residVars.vec) {
-## Inputs:
-## beta.mat n x k matrix of factor betas
-## factor.cov k x k factor return covariance matrix
-## residVars.vec n x 1 vector of residual variances from factor model
-## Output:
-## cov.fm n x n return covariance matrix based on
-## estimated factor model.
- beta.mat = as.matrix(beta.mat)
- factor.cov = as.matrix(factor.cov)
- sig.e = as.vector(residVars.vec)
- if (length(sig.e) > 1) {
- D.e = diag(as.vector(sig.e))
- } else {
- D.e = as.matrix(sig.e)
- }
- if (ncol(beta.mat) != ncol(factor.cov))
- stop("beta.mat and factor.cov must have same number of columns")
-
- if (nrow(D.e) != nrow(beta.mat))
- stop("beta.mat and D.e must have same number of rows")
- cov.fm = beta.mat %*% factor.cov %*% t(beta.mat) + D.e
- if (any(diag(chol(cov.fm)) == 0))
- warning("Covariance matrix is not positive definite")
- return(cov.fm)
-}
-
+#' Compute Factor Model Covariance Matrix.
+#'
+#' Compute asset return covariance matrix from factor model parameters.
+#'
+#' The return on asset \code{i} (\code{i = 1,...,N}) is assumed to follow the
+#' factor model \cr \code{R(i,t) = alpha + t(beta)*F(t) + e(i,t), e(i,t) ~ iid
+#' (0, sig(i)^2)} \cr where \code{beta} is a \code{K x 1} vector of factor
+#' exposures. The return variance is then \cr \code{var(R(i,t) =
+#' t(beta)*var(F(t))*beta + sig(i)^2}, \cr and the \code{N x N} covariance
+#' matrix of the return vector \code{R} is \cr \code{var(R) = B*var(F(t))*t(B)
+#' + D} \cr where B is the \code{N x K} matrix of asset betas and \code{D} is a
+#' diagonal matrix with \code{sig(i)^2} values along the diagonal.
+#'
+#' @param beta \code{N x K} matrix of factor betas, where \code{N} is the
+#' number of assets and \code{K} is the number of factors.
+#' @param factor.cov \code{K x K} factor return covariance matrix.
+#' @param resid.variance \code{N x 1} vector of asset specific residual
+#' variances from the factor model.
+#' @return \code{N x N} return covariance matrix based on factor model
+#' parameters.
+#' @author Eric Zivot and Yi-An Chen.
+#' @references Zivot, E. and J. Wang (2006), \emph{Modeling Financial Time
+#' Series with S-PLUS, Second Edition}, Springer-Verlag.
+#' @examples
+#'
+#' # Time Series model
+#'
+#' data(managers.df)
+#' factors = managers.df[,(7:9)]
+#' fit <- fitTimeseriesFactorModel(assets.names=colnames(managers.df[,(1:6)]),
+#' factors.names=c("EDHEC.LS.EQ","SP500.TR"),
+#' data=managers.df,fit.method="OLS")
+#' factorModelCovariance(fit$beta,var(factors),fit$resid.variance)
+#'
+#' # Statistical Model
+#' data(stat.fm.data)
+#' fit <- fitStatisticalFactorModel(sfm.dat,k=2,
+#' ckeckData.method="data.frame")
+#'
+#' factorModelCovariance(t(sfm.pca.fit$loadings),var(sfm.pca.fit$factors),sfm.pca.fit$resid.variance)
+#'
+#' sfm.apca.fit <- fitStatisticalFactorModel(sfm.apca.dat,k=2
+#' ,ckeckData.method="data.frame")
+#'
+#' factorModelCovariance(t(sfm.apca.fit$loadings),
+#' var(sfm.apca.fit$factors),sfm.apca.fit$resid.variance)
+#'
+factorModelCovariance <-
+function(beta.mat, factor.cov, residVars.vec) {
+## Inputs:
+## beta.mat n x k matrix of factor betas
+## factor.cov k x k factor return covariance matrix
+## residVars.vec n x 1 vector of residual variances from factor model
+## Output:
+## cov.fm n x n return covariance matrix based on
+## estimated factor model.
+ beta.mat = as.matrix(beta.mat)
+ factor.cov = as.matrix(factor.cov)
+ sig.e = as.vector(residVars.vec)
+ if (length(sig.e) > 1) {
+ D.e = diag(as.vector(sig.e))
+ } else {
+ D.e = as.matrix(sig.e)
+ }
+ if (ncol(beta.mat) != ncol(factor.cov))
+ stop("beta.mat and factor.cov must have same number of columns")
+
+ if (nrow(D.e) != nrow(beta.mat))
+ stop("beta.mat and D.e must have same number of rows")
+ cov.fm = beta.mat %*% factor.cov %*% t(beta.mat) + D.e
+ if (any(diag(chol(cov.fm)) == 0))
+ warning("Covariance matrix is not positive definite")
+ return(cov.fm)
+}
+
Modified: pkg/FactorAnalytics/R/fitStatisticalFactorModel.R
===================================================================
--- pkg/FactorAnalytics/R/fitStatisticalFactorModel.R 2013-06-27 22:14:44 UTC (rev 2455)
+++ pkg/FactorAnalytics/R/fitStatisticalFactorModel.R 2013-06-27 22:39:32 UTC (rev 2456)
@@ -303,7 +303,7 @@
dimnames(f) <- list(dimnames(data)[[1]], paste("F", 1:k, sep = "."))
names(alpha) <- data.names
resid <- t(t(data) - alpha) - f %*% B
- r2 <- (1 - colSums(res^2)/colSums(xc^2))
+ r2 <- (1 - colSums(resid^2)/colSums(xc^2))
if (ckeckData.method == "xts" | ckeckData.method == "zoo" ) {
f <- xts(f,index(data.xts))
Added: pkg/FactorAnalytics/R/summary.FundamentalFactorModel.r
===================================================================
--- pkg/FactorAnalytics/R/summary.FundamentalFactorModel.r (rev 0)
+++ pkg/FactorAnalytics/R/summary.FundamentalFactorModel.r 2013-06-27 22:39:32 UTC (rev 2456)
@@ -0,0 +1,18 @@
+#' summary method for FundamentalFactorModel
+#'
+#' Generic function of summary method for fitTimeSeriesFactorModel.
+#'
+#' @param fit it object created by fitFundamentalFactorModel.
+#'
+#' @author Yi-An Chen
+#'
+#'
+#'
+#' @export
+#'
+
+summary.FundamentalFactorModel <- function(fit) {
+dim(fit$factors)
+print(fit$factors)
+
+}
\ No newline at end of file
Added: pkg/FactorAnalytics/man/summary.FundamentalFactorModel.Rd
===================================================================
--- pkg/FactorAnalytics/man/summary.FundamentalFactorModel.Rd (rev 0)
+++ pkg/FactorAnalytics/man/summary.FundamentalFactorModel.Rd 2013-06-27 22:39:32 UTC (rev 2456)
@@ -0,0 +1,18 @@
+\name{summary.FundamentalFactorModel}
+\alias{summary.FundamentalFactorModel}
+\title{summary method for FundamentalFactorModel}
+\usage{
+ summary.FundamentalFactorModel(fit)
+}
+\arguments{
+ \item{fit}{it object created by
+ fitFundamentalFactorModel.}
+}
+\description{
+ Generic function of summary method for
+ fitTimeSeriesFactorModel.
+}
+\author{
+ Yi-An Chen
+}
+
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