Dynamic Stock Selection Strategies: A Structured Factor Model Framework

Carlos M. Carvalho, Hedibert F. Lopes, Omar Aguilar, Manuel Mendoza

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We propose a novel framework for estimating the time-varying covariation among stocks. Our work is inspired by asset pricing theory and associated developments in Financial Index Models. We work with a family of highly structured dynamic factor models that seek the extraction of the latent structure responsible for the cross-sectional covariation in a large set of financial securities. Our models incorporate stock specific information in the estimation of commonalities and deliver economically interpretable factors that are used both as a vehicle to estimate the large time-varying covariance matrix, and as a potential tool for stock selection in portfolio allocation problems. In an empirically oriented, high-dimensional case study, we showcase the use of our methodology and highlight the flexibility and power of the dynamic factor model framework in financial econometrics.

Original languageEnglish (US)
Title of host publicationBayesian Statistics 9
PublisherOxford University Press
Volume9780199694587
ISBN (Electronic)9780191731921
ISBN (Print)9780199694587
DOIs
StatePublished - Jan 19 2012
Externally publishedYes

Keywords

  • Dynamic factor models
  • Financial index models
  • Portfolio selection
  • Sparse factor models
  • Structured loadings

ASJC Scopus subject areas

  • Mathematics(all)

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