Bayesian forecasting of stock returns on the JSE using simultaneous graphical dynamic linear models*

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 findings indicate that the SGDLM forecasts stock data similarly to decoupled DLMs, as evident from values of root mean square errors and mean absolute deviations. The SGDLM forecasts the returns quite accurately as seen from the slightly overestimated prediction interval coverages.

KEYWORDS:

• Bayesian forecasting
• high-dimensional time series
• dynamic linear models
• importance sampling
• mean-field variational Bayes

Disclosure statement

No potential conflict of interest was reported by the authors.

Acknowledgment

The authors appreciate the Editors and two anonymous reviewers for their comments on the first version of the paper. Their comments were crucial to defining the final version of the paper. This paper is based on results from the first author's MSc thesis (Kyakutwika, 2022).

Additional information

Funding

The first author received funding from the Postgraduate Scholarship Programme of Stellenbosch University. Opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to Stellenbosch University.