Bayesian Forecasting and Portfolio Decisions using Simultaneous Graphical Dynamic Linear Models

Authors: 
Lutz Gruber, Mike West
Technical University of Munich, Duke University

Mar 14 2015

The recently introduced class of simultaneous graphical dynamic linear models (SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting to higher-dimensional time series. This paper advances the methodology of SGDLMs, developing and embedding a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. The advances include developments in Bayesian computation for scalability, and a case study in exploring the resulting potential for improved short-term forecasting of large-scale volatility matrices. A case study concerns financial forecasting and portfolio optimization with a 400-dimensional series of daily stock prices. Analysis shows that the SGDLM forecasts volatilities and co-volatilities well, making it ideally suited to contributing to quantitative investment strategies to improve portfolio returns. We also identify performance metrics linked to the sequential Bayesian filtering analysis that turn out to define a leading indicator of increased financial market stresses, comparable to but leading the standard St. Louis Fed Financial Stress Index (STLFSI) measure. Parallel computation using GPU implementations substantially advance the ability to fit and use these models.

Keywords: 

Bayesian forecasting, dynamic graphical models, GPU computing, high-dimensional time series, massively parallel computing, multivariate stochastic volatility, portfolio optimization, target return portfolios

Manuscript: 

PDF icon 2015-01_0.pdf

BibTeX Citation: 

@techreport{Gruber2015,
  author = {L. F. Gruber and M. West},
  title = {Bayesian forecasting and portfolio decisions using simultaneous graphical dynamic linear models},
  year = {2015},
  note = {Submitted for publication},
  url = {http://stat.duke.edu/research/papers/2015-01}
}