covFactorModel-package: covFactorModel: Covariance matrix estimation via factor...

In dppalomar/covFactorModel: Covariance Matrix Estimation via Factor Models

Description

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 idiosynchratic 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 symbol and their sectors.

Functions

factorModel, covFactorModel, getSectorInfo

Help

For a quick help see the README: GitHub-README.

For more details see the vignette: GitHub-html-vignette, and GitHub-pdf-vignette.

Author(s)

Rui ZHOU and Daniel P. Palomar

References

R. S. Tsay, Analysis of Financial Time Series. John Wiley & Sons, 2005.

dppalomar/covFactorModel documentation built on May 17, 2019, 2:14 a.m.