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Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models

Author

Listed:
  • Aldo Levy

    () (LIRSA - Laboratoire interdisciplinaire de recherche en sciences de l'action - CNAM - Conservatoire National des Arts et Métiers [CNAM])

  • M.H. Arouri
  • Amine Lahiani

    (LEO - Laboratoire d'économie d'Orleans - UO - Université d'Orléans - CNRS - Centre National de la Recherche Scientifique)

  • Duc Khuong Nguyen

    () (IPAG - IPAG Business School - Ipag)

Abstract

This paper extends previous studies by investigating the relevance of structural breaks and long memory in modeling and forecasting the conditional volatility of oil spot and futures prices using a variety of GARCH-type models. Our results can be summarized as follows. First, we provide evidence of parameter instability in five out of nine GARCH-based conditional volatility processes for energy prices. Second, long memory is effectively present in all the series considered and a FIGARCH model seems to better fit the data, but the degree of volatility persistence diminishes significantly after adjusting for structural breaks. Finally, the out-of-sample analysis shows that volatility models accommodating instability and long memory characteristics of the data provide the best volatility forecasts for most cases.

Suggested Citation

  • Aldo Levy & M.H. Arouri & Amine Lahiani & Duc Khuong Nguyen, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Post-Print halshs-01279906, HAL.
  • Handle: RePEc:hal:journl:halshs-01279906
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01279906
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    More about this item

    Keywords

    Oil markets; Volatility forecasting; Long memory; Structural breaks; GARCH-class models;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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