Online Program Home
My Program

Abstract Details

Activity Number: 478 - Scalable Bayesian Models for Time Series and Dynamic Networks: Making an Impact in Business and Socio-Economic Applications
Type: Invited
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #300162
Title: Online Learning and Variable Selection for High-Dimensional Time Series with Simultaneous Graphical Dynamic Linear Models
Author(s): Lutz F Gruber* and Mike West
Companies: QuantCo, Inc. and Duke University

Simultaneous graphical dynamic linear models (SGDLMs) define an ability to scale on-line Bayesian analysis and multivariate volatility forecasting to higher-dimensional time series. Advances in the methodology of SGDLMs involve a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. This Bayesian methodology for dynamic variable selection and Bayesian computation for scalability are highlighted in a case study evidencing the potential for improved short-term forecasting of large-scale volatility matrices. In financial forecasting and portfolio optimization with a 400-dimensional series of daily stock prices, analysis demonstrates SGDLM forecasts of volatilities and co-volatilities that contribute to quantitative investment strategies to improve portfolio returns. Performance metrics linked to the sequential Bayesian filtering analysis define a leading indicator of increased financial market stresses, comparable to but leading standard financial risk measures. Parallel computation using GPU implementations substantially advance the ability to fit and use these models.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program