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

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Author Info

  • 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.

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Bibliographic Info

Article provided by Institute for Economic Forecasting in its journal Romanian Journal for Economic Forecasting.

Volume (Year): (2012)
Issue (Month): 4 (December)
Pages: 83-103

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Handle: RePEc:rjr:romjef:v::y:2012:i:4:p:83-103

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Related research

Keywords: High Frequency Data; Fat-tail; Skewness; Realized Volatility; Agricultural Futures;

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References

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