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Forecasting volatility with interacting multiple models

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  • Svec, Jiri
  • Katrak, Xerxis

Abstract

We examine the performance of Kalman filter techniques in forecasting volatility. We find that the simple implementation of an online Kalman filtering procedure that combines commonly used forecasting models with market-based estimates improves the accuracy of volatility forecasts. Furthermore, we demonstrate that the Interacting Multiple Model algorithm, which combines multiple Kalman filters, provides the most accurate volatility forecasts overall.

Suggested Citation

  • Svec, Jiri & Katrak, Xerxis, 2017. "Forecasting volatility with interacting multiple models," Finance Research Letters, Elsevier, vol. 20(C), pages 245-252.
    • Handle: RePEc:eee:finlet:v:20:y:2017:i:c:p:245-252
    DOI: 10.1016/j.frl.2016.10.005
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    More about this item

    Keywords

    Forecasting; Volatility; Kalman filter; Interacting multiple models;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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