Dynamic factor volatility modeling: A bayesian latent threshold approach

Published

Journal Article

We discuss dynamic factor modeling of financial time series using a latent threshold approach to factor volatility. This approach models time-varying patterns of occurrence of zero elements in factor loadings matrices, providing adaptation to changing relationships over time and dynamic model selection. We summarize Bayesian methods for model fitting and discuss analyses of several FX, commodities, and stock price index time series. Empirical results show that the latent threshold approach can define interpretable, data-driven, dynamic sparsity, leading to reduced estimation uncertainties, improved predictions, and portfolio performance in increasingly high-dimensional dynamic factor models. © The Author, 2012. Published by Oxford University Press. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Nakajima, J; West, M

Published Date

  • December 1, 2012

Published In

Volume / Issue

  • 11 / 1

Start / End Page

  • 116 - 153

Electronic International Standard Serial Number (EISSN)

  • 1479-8417

International Standard Serial Number (ISSN)

  • 1479-8409

Digital Object Identifier (DOI)

  • 10.1093/jjfinec/nbs013

Citation Source

  • Scopus