A Bayesian Monte Carlo Variational Inference Estimation Procedure for Dynamic Factor Models on Stock Price Returns

Benedict Ryan Tiu, Dominic B. Dayta, Hans Jarett J. Ong, Brian Godwin Lim, Kazushi Ikeda

Abstract: Dynamic factor models (DFMs) provide a framework for distilling high-dimensional time series data into a small set of unobserved latent factors. Traditional statistical methods for estimating DFMs are often computationally intensive and can be inflexible when adapting to non-linear model extensions. In this work, we propose a modular Bayesian Monte Carlo variational inference (MCVI) estimation procedure for DFMs designed to prioritize flexibility and extensibility. We demonstrate that in the context of the Philippine Stock Exchange, the estimated latent factors and loadings qualitatively align with findings obtained through conventional methods. To illustrate the modularity of the framework, we extend the DFM to incorporate a diagonal BEKK-GARCH structure, which reveals non-trivial volatility clustering in the latent factors. While the adoption of a mean-field variational approximation entails certain trade-offs in posterior accuracy and computational overhead relative to specialized linear estimators, the decoupling of the model specification from the inference engine allows for the rapid integration of complex layers without requiring model-specific re-derivations. Overall, this work establishes an extensible Bayesian framework that facilitates more nuanced investigations into the dynamic and heteroskedastic nature of systematic risk in financial markets.

External IDs:dblp:journals/access/TiuDOLI26