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Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models

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  • Gruber, Lutz F.
  • West, Mike

Abstract

Simultaneous graphical dynamic linear models (SGDLMs) define an ability to scale online 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 online 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.

Suggested Citation

  • Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    • Handle: RePEc:eee:ecosta:v:3:y:2017:i:c:p:3-22
    DOI: 10.1016/j.ecosta.2017.03.003
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    4. McAlinn, Kenichiro & West, Mike, 2019. "Dynamic Bayesian predictive synthesis in time series forecasting," Journal of Econometrics, Elsevier, vol. 210(1), pages 155-169.
    5. Baştürk, N. & Borowska, A. & Grassi, S. & Hoogerheide, L. & van Dijk, H.K., 2019. "Forecast density combinations of dynamic models and data driven portfolio strategies," Journal of Econometrics, Elsevier, vol. 210(1), pages 170-186.
    6. Mike West, 2020. "Bayesian forecasting of multivariate time series: scalability, structure uncertainty and decisions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 1-31, February.
    7. Nascimento, Diego C. & Pimentel, Bruno A. & Souza, Renata M.C.R. & Costa, Lilia & Gonçalves, Sandro & Louzada, Francisco, 2021. "Dynamic graph in a symbolic data framework: An account of the causal relation using COVID-19 reports and some reflections on the financial world," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

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