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Forecasting Realized Volatility of Agricultural Commodity Futures with Infinite Hidden Markov HAR Models

Author

Listed:
  • Luo, Jiawen
  • Klein, Tony
  • Ji, Qiang
  • Hou, Chenghan
Abstract
We construct a set of HAR models with three types of infinite Hidden Markov regime switching structures. Particularly, jumps, leverage effects, and speculation effects are taken into account in realized volatility modeling. We forecast five agricultural commodity futures (Corn, Cotton, Indica Rice, Palm oil and Soybean) based on high frequency data from Chinese futures markets and evaluate the forecast performances with both statistical and economic evaluation measures. The statistical evaluation results suggest that HAR models with infinite Hidden Markov regime switching structures have better precision compared the benchmark HAR models based on the MZ-R², MAFE, and MCS results. The economic evaluation results suggest that portfolios constructed with infinite Hidden Markov regime switching HARs achieve higher portfolio returns for risk averse investors compared to benchmark HAR model for short-term volatility forecasts.

Suggested Citation

  • Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan, 2019. "Forecasting Realized Volatility of Agricultural Commodity Futures with Infinite Hidden Markov HAR Models," QBS Working Paper Series 2019/10, Queen's University Belfast, Queen's Business School.
    • Handle: RePEc:zbw:qmsrps:201910
    DOI: 10.2139/ssrn.3435054
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    References listed on IDEAS

    as
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    4. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
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    Cited by:

    1. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting the realized volatility of agricultural commodity prices: Does sentiment matter?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2088-2125, September.
    2. Xu, Yan & Liu, Tianli & Du, Pei, 2024. "Volatility forecasting of crude oil futures based on Bi-LSTM-Attention model: The dynamic role of the COVID-19 pandemic and the Russian-Ukrainian conflict," Resources Policy, Elsevier, vol. 88(C).
    3. Rangan Gupta & Christian Pierdzioch, 2024. "Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    4. Bonato, Matteo & Cepni, Oguzhan & Gupta, Rangan & Pierdzioch, Christian, 2024. "Financial stress and realized volatility: The case of agricultural commodities," Research in International Business and Finance, Elsevier, vol. 71(C).

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    More about this item

    Keywords

    Agriculture commodity futures; Realized volatility forecasts; Infinite Hidden Markov switching process; HAR models;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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