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Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models

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  • Fredj Jawadi
  • Zied Ftiti
  • Waël Louhichi

Abstract

This study forecasts the volatility of two energy futures markets (oil and gas), using high-frequency data. We, first, disentangle volatility into continuous volatility and jumps. Second, we apply wavelet analysis to study the relationship between volume and the volatility measures for different horizons. Third, we augment the heterogeneous autoregressive (HAR) model by nonlinearly including both jumps and volume. We then propose different empirical extensions of the HAR model. Our study shows that oil and gas volatilities nonlinearly depend on public information (jumps), private information (continuous volatility), and trading volume. Moreover, our threshold augmented HAR model with heterogeneous jumps and continuous volatility outperforms HAR model in forecasting volatility.

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  • Fredj Jawadi & Zied Ftiti & Waël Louhichi, 2020. "Forecasting energy futures volatility with threshold augmented heterogeneous autoregressive jump models," Econometric Reviews, Taylor & Francis Journals, vol. 39(1), pages 54-70, January.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:1:p:54-70
    DOI: 10.1080/07474938.2019.1690190
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    Cited by:

    1. Bu, Ruijun & Hizmeri, Rodrigo & Izzeldin, Marwan & Murphy, Anthony & Tsionas, Mike, 2023. "The contribution of jump signs and activity to forecasting stock price volatility," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 144-164.

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