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Forecasting the volatility of crude oil futures using intraday data

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  • Benoît Sévi

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We use the information in intraday data to forecast the volatility of crude oil at a horizon of 1-66days using a variety of models relying on the decomposition of realized variance in its positive or negative (semivariances) part and its continuous or discontinuous part (jumps). We show the importance of these decompositions in predictive (in-sample) regressions using a number of specifications. Nevertheless, an important empirical finding comes from an out-of-sample analysis which unambiguously shows the limited interest of considering these components. Overall, our results indicates that a simple autoregressive specification mimicking long memory and using past realized variances as predictors does not perform significantly worse than more sophisticated models which include the various components of realized variance.

Suggested Citation

  • Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Post-Print hal-01463921, HAL.
    • Handle: RePEc:hal:journl:hal-01463921
    DOI: 10.1016/j.ejor.2014.01.019
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