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Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models

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  • Esben Høg
  • Leonidas Tsiaras

Abstract

The predictive accuracy of competing crude‐oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward‐looking information that is embedded in the prices of derivative contracts. Risk‐neutral densities, obtained from panels of crude‐oil option prices, are adjusted to reflect real‐world risks using either a parametric or a non‐parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non‐parametric adjustments of risk‐neutral density forecasts perform significantly better than their parametric counterparts. Goodness‐of‐fit tests and out‐of‐sample likelihood comparisons favor forecast densities obtained by option prices and non‐parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011

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  • Esben Høg & Leonidas Tsiaras, 2011. "Density forecasts of crude‐oil prices using option‐implied and ARCH‐type models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(8), pages 727-754, August.
    • Handle: RePEc:wly:jfutmk:v:31:y:2011:i:8:p:727-754
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    Cited by:

    1. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2020. "Forecasting commodity prices out-of-sample: Can technical indicators help?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 666-683.
    2. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    3. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.
    4. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    5. 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.
    6. Ricardo Crisóstomo & Lorena Couso, 2018. "Financial density forecasts: A comprehensive comparison of risk‐neutral and historical schemes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 589-603, August.
    7. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    8. Michael K. Adjemian & Valentina G. Bruno & Michel A. Robe, 2020. "Incorporating Uncertainty into USDA Commodity Price Forecasts," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 696-712, March.
    9. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.

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