dppalomar/covFactorModel: Covariance Matrix Estimation via Factor Models
Version 0.1.0

Estimation of covariance matrix via factor models with application to financial data. Factor models decompose the asset returns into an exposure term to some factors and a residual idiosyncratic component. The resulting covariance matrix contains a low-rank term corresponding to the factors and another full-rank term corresponding to the residual component. This package provides a function to separate the data into the factor component and residual component, as well as to estimate the corresponding covariance matrix. Different kind of factor models are considered, namely, macroeconomic factor models and statistical factor models. The estimation of the covariance matrix accepts different kinds of structure on the residual term: diagonal structure (implying that residual component is uncorrelated) and block diagonal structure (allowing correlation within sectors). The package includes a built-in database containing stock symbols and their sectors.

Getting started

Package details

MaintainerDaniel P. Palomar <[email protected]>
LicenseGPL-3 | file LICENSE
Version0.1.0
URL https://github.com/dppalomar/covFactorModel
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("dppalomar/covFactorModel")
dppalomar/covFactorModel documentation built on Aug. 22, 2018, 11:12 p.m.