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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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    1. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    2. repec:dau:papers:123456789/871 is not listed on IDEAS
    3. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, October.
    4. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 10, pages 515-554, Elsevier.
    5. Hedibert F. Lopes & Ruey S. Tsay, 2011. "Particle filters and Bayesian inference in financial econometrics," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(1), pages 168-209, January.
    6. David A. Belsley, 1973. "The Applicability of the Kalman Filter in the Determination of Systematic Parameter Variation," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 2, number 4, pages 531-533, National Bureau of Economic Research, Inc.
    7. Pasricha, Gurnain Kaur, 2006. "Kalman Filter and its Economic Applications," MPRA Paper 22734, University Library of Munich, Germany.
    8. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    9. Koopman S.J. & Bos C.S., 2004. "State Space Models With a Common Stochastic Variance," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 346-357, July.
    10. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    11. Harry Mamaysky & Matthew Spiegel & Hong Zhang, 2008. "Estimating the Dynamics of Mutual Fund Alphas and Betas," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 233-264, January.
    12. Delphine Lautier & Alireza Javaheri & Alain Galli, 2003. "Filtering in finance," Post-Print halshs-00153006, HAL.
    13. Grimmer, Justin, 2011. "An Introduction to Bayesian Inference via Variational Approximations," Political Analysis, Cambridge University Press, vol. 19(1), pages 32-47, January.
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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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