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A study of Shanghai fuel oil futures price volatility based on high frequency data: Long-range dependence, modeling and forecasting

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  • Liu, Li
  • Wan, Jieqiu
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    Abstract

    In existing researches, the investigations of oil price volatility are always performed based on daily data and squared daily return is always taken as the proxy of actual volatility. However, it is widely accepted that the popular realized volatility (RV) based on high frequency data is a more robust measure of actual volatility than squared return. Due to this motivation, we investigate dynamics of daily volatility of Shanghai fuel oil futures prices employing 5-minute high frequency data. First, using a nonparametric method, we find that RV displays strong long-range dependence and recent financial crisis can cause a lower degree of long-range dependence. Second, we model daily volatility using RV models and GARCH-class models. Our results indicate that RV models for intraday data overwhelmingly outperform GARCH-class models for daily data in forecasting fuel oil price volatility, regardless the proxy of actual volatility. Finally, we investigate the major source of such volatile prices and found that trader activity has major contribution to fierce variations of fuel oil prices.

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    Bibliographic Info

    Article provided by Elsevier in its journal Economic Modelling.

    Volume (Year): 29 (2012)
    Issue (Month): 6 ()
    Pages: 2245-2253

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    Handle: RePEc:eee:ecmode:v:29:y:2012:i:6:p:2245-2253

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    Web page: http://www.elsevier.com/locate/inca/30411

    Related research

    Keywords: Fuel oil price; Volatility; Long-range dependence; Forecasting;

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    Cited by:
    1. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    2. Sévi, Benoît, 2013. "An empirical analysis of the downside risk-return trade-off at daily frequency," Economic Modelling, Elsevier, vol. 31(C), pages 189-197.

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