Analysis of Linear Factor Models with Multivariate Stochastic Volatility for Stock and Bond Returns

46 Pages Posted: 18 Jul 2003

See all articles by John T. Scruggs

John T. Scruggs

Allianz Global Investors

Federico Nardari

University of Melbourne - Department of Finance

Date Written: February 18, 2003

Abstract

We explore high-dimensional linear factor models in which the covariance matrix of excess asset returns follows a multivariate stochastic volatility process. We test crosssectional restrictions suggested by the arbitrage pricing theory, compare competing stochastic volatility specifications for the covariance matrix, test for the number of factors, and analyze possible sources of model misspecification. Estimation and testing of these models is feasible due to recent advances in Bayesian Markov chain Monte Carlo (MCMC) methods. We find that five latent factors with multivariate stochastic volatility best explain excess returns for a sample of seventeen stock and bond portfolios. Analysis of cumulative latent factor shocks suggests that APT pricing restrictions, coupled with constant factor risk premia, do not adequately explain cross-sectional variation in average portfolio excess returns.

Keywords: Arbitrage Pricing Theory, Factor Model, Multivariate Stochastic Volatility, Markov chain Monte Carlo, Gibbs sampling

JEL Classification: G12, C11, C15, C32

Suggested Citation

Scruggs, John T. and Nardari, Federico, Analysis of Linear Factor Models with Multivariate Stochastic Volatility for Stock and Bond Returns (February 18, 2003). EFA 2003 Annual Conference Paper No. 668. Available at SSRN: https://ssrn.com/abstract=423980 or http://dx.doi.org/10.2139/ssrn.423980

John T. Scruggs

Allianz Global Investors ( email )

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

Federico Nardari (Contact Author)

University of Melbourne - Department of Finance ( email )

Faculty of Economics and Commerce
Parkville, Victoria 3010 3010
Australia

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