2023/07/07 by Nelson Kyakutwika, Kyakutwika, Nelson, Bruce Bartlett +1
Computer Science · Economics, Econometrics and Finance · #Bayesian Methods and Mixture Models #Complex Systems and Time Series Analysis #FOS: Economics and business #Financial Risk and Volatility Modeling #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2307.08665
openalex publication_date 2023/07/07 · openalex created_date 2023/07/19 · openalex updated_date 2026/07/28
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.