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Commodity price volatility under regulatory changes and disaster

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  • Marvasti, Akbar
  • Lamberte, Antonio

Abstract

We find that the EGARCH model best describes the dynamics of U.S. Gulf of Mexico red snapper daily dockside prices and find their reaction to shocks to be asymmetric, though news has an impact on volatility level in a direction contrary to that of financial asset prices. We also find that volume contains useful information for predicting volatility. However, unlike financial asset prices, though consistent with fish commodities prices, red snapper price volatility diminishes when the volume is high. Also, the effect of expected changes on transaction volume is more dominant than that of unexpected changes. Explicitly accounting for oil spill closures and the Individual Fishing Quotas (IFQ) program in other species as variance shift parameters significantly reduces volatility and improves the market efficiency response to shocks.

Suggested Citation

  • Marvasti, Akbar & Lamberte, Antonio, 2016. "Commodity price volatility under regulatory changes and disaster," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 355-361.
    • Handle: RePEc:eee:empfin:v:38:y:2016:i:pa:p:355-361
    DOI: 10.1016/j.jempfin.2016.07.008
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    References listed on IDEAS

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    Cited by:

    1. Wu, Nan & Wen, Fenghua & Gong, Xu, 2022. "Marionettes behind co-movement of commodity prices: Roles of speculative and hedging activities," Energy Economics, Elsevier, vol. 115(C).
    2. Afees A. Salisu & Kazeem Isah & Ibrahim D. Raheem, 2018. "Testing the predictability of commodity prices in stock returns: A new perspective," Working Papers 061, Centre for Econometric and Allied Research, University of Ibadan.
    3. Ruth Beatriz Mezzalira Pincinato & Frank Asche & Atle Oglend, 2020. "Climate change and small pelagic fish price volatility," Climatic Change, Springer, vol. 161(4), pages 591-599, August.
    4. Zhou, Liyun & Huang, Jialiang, 2020. "Excess co-movement of agricultural futures prices: Perspective from contagious investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    5. Zhou, Liyun & Zhang, Rixin & Huang, Jialiang, 2019. "Investor trading behavior on agricultural future prices," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 365-379.
    6. Salisu, Afees A. & Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach," Resources Policy, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Price volatility; GARCH; Time series; Regulatory change; Disaster;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery

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