R/getPortfolio.R

In IntroCompFinR: Introduction to Computational Finance in R

#' @title Create portfolio object
#' 
#' @author Eric Zivot
#' 
#' @description
#' Create a portfolio object from expected return vector, covariance matrix, and weight vector.
#' 
#' @details 
#' To specify a portfolio, an expected return vector and covariance matrix for the assets under 
#' consideration as well as a vector of portfolio weights are needed. The result of
#' \samp{getPortfolio} is a \samp{portfolio} object, which is list with components for the portfolio
#' expected return, portfolio standard deviation, and portfolio weights. There are \samp{print},
#' \samp{summary} and \samp{plot} methods. 
#' 
#' @param er \samp{N x 1} vector of expected returns
#' @param cov.mat \samp{N x N} return covariance matrix
#' @param weights \samp{N x 1} vector of portfolio weights
#' 
#' @return 
#'  \item{call}{captures function call}
#'  \item{er}{portfolio expected return}
#'  \item{sd}{portfolio standard deviation}
#'  \item{weights}{\samp{N x 1} vector of portfolio weights}
#' 
#' @examples
#' # construct the data
#' asset.names = c("MSFT", "NORD", "SBUX")
#' er = c(0.0427, 0.0015, 0.0285)
#' names(er) = asset.names
#' covmat = matrix(c(0.0100, 0.0018, 0.0011,
#'                   0.0018, 0.0109, 0.0026,
#'                   0.0011, 0.0026, 0.0199),
#'                 nrow=3, ncol=3)
#' r.free = 0.005
#' dimnames(covmat) = list(asset.names, asset.names)
#' er
#' covmat
#' r.free
#' 
#' # compute equally weighted portfolio
#' ew = rep(1,3)/3
#' equalWeight.portfolio = getPortfolio(er=er,cov.mat=covmat,weights=ew)
#' class(equalWeight.portfolio)
#' names(equalWeight.portfolio)
#' equalWeight.portfolio
#' summary(equalWeight.portfolio)
#' plot(equalWeight.portfolio, col="blue")
#' 
#' @export getPortfolio

getPortfolio <-
function(er, cov.mat, weights)
{
	call <- match.call()
	
	#
	# check for valid inputs
	#
	asset.names <- names(er)
	weights <- as.vector(weights)
	names(weights) = names(er)
  er <- as.vector(er) # assign names if none exist
	if(length(er) != length(weights))
		stop("dimensions of er and weights do not match")
 	cov.mat <- as.matrix(cov.mat)
	if(length(er) != nrow(cov.mat))
		stop("dimensions of er and cov.mat do not match")
	if(any(diag(chol(cov.mat)) <= 0))
		stop("Covariance matrix not positive definite")
		
	#
	# create portfolio
	#
	er.port <- crossprod(er,weights)
	sd.port <- sqrt(weights %*% cov.mat %*% weights)
	ans <- list("call" = call,
	      "er" = as.vector(er.port),
	      "sd" = as.vector(sd.port),
	      "weights" = weights) 
	class(ans) <- "portfolio"
	return(ans)
}