Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and APT Pricing Restrictions

Federico Nardari and John T. Scruggs

Journal of Financial and Quantitative Analysis, 2007, vol. 42, issue 4, 857-891

Abstract: We analyze a new class of linear factor models in which the factors are latent and the covariance matrix of excess returns follows a multivariate stochastic volatility process. We evaluate cross-sectional restrictions suggested by the arbitrage pricing theory (APT), compare competing stochastic volatility specifications for the covariance matrix, and test for the number of factors. We also examine whether return predictability can be attributed to time-varying factor risk premia. Analysis of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that three latent factors with multivariate stochastic volatility best explain excess returns for a sample of 10 size decile portfolios. The data strongly favor models constrained by APT pricing restrictions over otherwise identical unconstrained models.

Date: 2007
References: Add references at CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cup:jfinqa:v:42:y:2007:i:04:p:857-891_00

Access Statistics for this article

More articles in Journal of Financial and Quantitative Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().