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

55 Pages Posted: 8 May 2006

See all articles by John T. Scruggs

John T. Scruggs

Allianz Global Investors

Federico Nardari

University of Melbourne - Department of Finance

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 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 ten size decile portfolios. The data strongly favor models constrained by APT pricing restrictions over otherwise identical unconstrained models.

Keywords: Arbitrage Pricing Theory, Latent Factors, Multivariate Stochastic Volatility, Markov Chain Monte Carlo

JEL Classification: G12, C11, C15, C32

Suggested Citation

Scruggs, John T. and Nardari, Federico, Bayesian Analysis of Linear Factor Models with Latent Factors, Multivariate Stochastic Volatility, and Apt Pricing Restrictions. Journal of Financial and Quantitative Analysis, Forthcoming. Available at SSRN: https://ssrn.com/abstract=901024

John T. Scruggs (Contact Author)

Allianz Global Investors ( email )

555 Mission Street
Suite 1700
San Francisco, CA 94105
United States

Federico Nardari

University of Melbourne - Department of Finance ( email )

Faculty of Economics and Commerce
Parkville, Victoria 3010 3010
Australia

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