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Bayesian Forecasting of Stock Returns on the JSE using Simultaneous Graphical Dynamic Linear Models

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  • Nelson Kyakutwika
  • Bruce Bartlett

Abstract

Cross-series dependencies are crucial in obtaining accurate forecasts when forecasting a multivariate time series. Simultaneous Graphical Dynamic Linear Models (SGDLMs) are Bayesian models that elegantly capture cross-series dependencies. This study forecasts returns of a 40-dimensional time series of stock data from the Johannesburg Stock Exchange (JSE) using SGDLMs. The SGDLM approach involves constructing a customised dynamic linear model (DLM) for each univariate time series. At each time point, the DLMs are recoupled using importance sampling and decoupled using mean-field variational Bayes. Our results suggest that SGDLMs forecast stock data on the JSE accurately and respond to market gyrations effectively.

Suggested Citation

  • Nelson Kyakutwika & Bruce Bartlett, 2023. "Bayesian Forecasting of Stock Returns on the JSE using Simultaneous Graphical Dynamic Linear Models," Papers 2307.08665, arXiv.org.
    • Handle: RePEc:arx:papers:2307.08665
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    References listed on IDEAS

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    1. 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.
    2. Mike West, 2020. "Reply to Discussion of “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 41-44, February.
    3. 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.
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