Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)

TL;DR

This paper introduces dynamic dependence network models for high-dimensional financial time series forecasting, enabling scalable analysis and improved portfolio decision-making through Bayesian methods and sparse graphical structures.

Contribution

It develops novel state-space models that decouple and recouple multivariate series, allowing efficient analysis and Bayesian model averaging for high-dimensional financial data.

Findings

Enhanced forecasting accuracy demonstrated in case studies

Improved portfolio decisions using Bayesian analysis

Significant computational efficiency gains

Abstract

We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these "dynamic dependence network" models shows how the individual series can be "decoupled" for sequential analysis, and then "recoupled" for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked-- for later recoupling-- through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models, and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currency, stock indices and commodity prices, the case study includes evaluations of multi-day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks.