Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

Published

Journal Article

We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers. © 2014 International Institute of Forecasters.

Full Text

Duke Authors

Cited Authors

  • Zhou, X; Nakajima, J; West, M

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 30 / 4

Start / End Page

  • 963 - 980

International Standard Serial Number (ISSN)

  • 0169-2070

Digital Object Identifier (DOI)

  • 10.1016/j.ijforecast.2014.03.017

Citation Source

  • Scopus