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Price Volatility Forecast for Agricultural Commodity Futures： The Role of High Frequency Data

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  • Huang, Wen

    (China Center for Economic Research, National School of Development, Peking University)

  • Huang, Zhuo

    (China Center for Economic Research, National School of Development at Peking University.)

  • Matei, Marius

    (School of Economics & Finance, University of Tasmania)

  • Wang, Tianyi

    (Research Center of Applied Finance, School of Banking and Finance, University of International Business and Economics)

Abstract

Realized measures of volatility based on high frequency data contain valuable information about the unobserved conditional volatility. In this paper, we use the Realized GARCH model developed by Hansen, Huang and Shek (2012) to estimate and forecast price volatility for four agricultural commodity futures. Empirical evidences, both in-sample and out-of-sample, show that the Realized GARCH model and its variants outperform the conventional volatility models that only use daily price data, such as GARCH and EGARCH. We also consider skewed student’s t-distribution to account for the skewness and fat-tail in the agricultural futures prices. The empirical performances are relatively close for models using three different realized measures, as the measurement equation in the Realized GARCH model can adjust to the different realized measures to some extent.

Suggested Citation

  • Huang, Wen & Huang, Zhuo & Matei, Marius & Wang, Tianyi, 2012. "Price Volatility Forecast for Agricultural Commodity Futures： The Role of High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 83-103, December.
    • Handle: RePEc:rjr:romjef:v::y:2012:i:4:p:83-103
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    Cited by:

    1. Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
    2. Dejan Živkov & Boris Kuzman & Jonel Subić, 2020. "What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(5), pages 215-225.
    3. Sanusi, Olajide I. & Safi, Samir K. & Adeeko, Omotara & Tabash, Mosab I., 2022. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 8(2), June.
    4. Dejan Živkov & Suzana Balaban & Marijana Joksimović, 2022. "Making a Markowitz portfolio with agricultural commodity futures," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(6), pages 219-229.

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    More about this item

    Keywords

    High Frequency Data; Fat-tail; Skewness; Realized Volatility; Agricultural Futures;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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