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Bayesian Statistics 9$
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José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 13 April 2021

Dynamic Stock Selection Strategies: A Structured Factor Model Framework *

Dynamic Stock Selection Strategies: A Structured Factor Model Framework *

(p.69) Dynamic Stock Selection Strategies: A Structured Factor Model Framework*
Bayesian Statistics 9

Carlos M. Carvalho

Hedibert F. Lopes

Omar Aguilar

Oxford University Press

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.

Keywords:   Dynamic Factor Models, Financial Index models, Portfolio selection, Sparse factor models, Structured loadings

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