Bayesian Forecasting and Portfolio Decisions using Dynamic Dependent Sparse Factor Models

Xiaocong Zhou, Jouchi Nakajima, Mike West
Duke University

Jun 11 2012

Published paper: International Journal of Forecasting, 30:963-980, 2014

(Original 2012 discussion paper is linked below)

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.

This work was supported by the National Science Foundation [DMS-1106516 to M.W.]. Any opinions, findings and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the National Science Foundation.


PDF icon 2012-09.pdf

BibTeX Citation: 

  author = {X. Zhou and J. Nakajima and M. West},
  title = {Bayesian forecasting and portfolio decisions using dynamic dependent
 sparse factor sparse},
  journal = {International Journal of Forecasting},
  year = {2014},
  volume = {30},
  pages = {963-980},
  doi = {},
  url = {}