A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour

Théophile Griveau-Billion and Ben Calderhead

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Abstract: We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable multivariate volatility estimator, which decomposes multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving the underlying variability. This unique decomposition goes beyond the classic one step ahead prediction; indeed, we investigate inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time.

Date: 2019-04, Revised 2020-01
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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