Journal article

Bayesian analysis of linear factor models with latent factors, multivariate stochastic volatility, and APT pricing restrictions

F Nardari, JT Scruggs

Journal of Financial and Quantitative Analysis | Published : 2007

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 ..

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University of Melbourne Researchers

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