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Time-varying forecasts by variational approximation of sequential Bayesian inference

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  • Hui ‘Fox’ Ling
  • Douglas B. Stone

Abstract

Methods developed for making time-varying forecasts in economic and financial analysis include (a) equal-weighted moving-window (or rolling) regression, (b) time-weighted (e.g. exponentially weighted) regression, (c) the Kalman filter (KF) and (d) adaptive Kalman filters. This paper developed a new method based on variational approximation of sequential Bayesian inference (VASB). Concepts and notions of the sequential Bayesian analysis and the variational approximation of an intractable posterior are simple and straightforward. Our VASB algorithm is not complicated and is easy to code. For a regression on multiple time-series, the regression coefficients, standard errors, prediction and residual error are time-varying and are estimated jointly at every time step. For a single time-series (e.g. price returns of an asset), its mean and variance are time-varying and are predicted jointly at every time step. The VASB algorithm performs better than the rolling and time-weighted statistics or regressions and the KF in terms of higher predictive power and stronger robustness. Derivations of the VASB algorithm are presented in the appendices.

Suggested Citation

  • Hui ‘Fox’ Ling & Douglas B. Stone, 2016. "Time-varying forecasts by variational approximation of sequential Bayesian inference," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 43-67, January.
    • Handle: RePEc:taf:quantf:v:16:y:2016:i:1:p:43-67
    DOI: 10.1080/14697688.2015.1034759
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    References listed on IDEAS

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    Cited by:

    1. Hui ‘Fox’ Ling & Christian Franzen, 2017. "Online learning of time-varying stochastic factor structure by variational sequential Bayesian factor analysis," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1277-1304, August.

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