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What the investors need to know about forecasting oil futures return volatility

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  • Wang, Yudong
  • Liu, Li
  • Ma, Feng
  • Wu, Chongfeng

Abstract

In this paper, we evaluate the usefulness of GARCH-class models in forecasting densities of crude oil futures from an investor perspective. Volatility forecasts are taken as the key inputs in calculating predictive densities. We find that FIEGARCH accommodating both long memory and asymmetric effect provides more accurate density forecasts than the other GARCH-class models most of the time. GARCH-based dynamic trading strategies perform significantly better than the benchmark of the static strategy even after accounting for the transaction cost. The gains of utility of GARCH-based strategies over the benchmark strategy are as high as 18%–20% p.a.

Suggested Citation

  • Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.
  • Handle: RePEc:eee:eneeco:v:57:y:2016:i:c:p:128-139
    DOI: 10.1016/j.eneco.2016.05.004
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    More about this item

    Keywords

    Crude oil; Futures; Density; GARCH; Portfolio;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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