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Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities

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  • František Čech
  • Jozef Baruník

Abstract

Using a flexible panel quantile regression framework, we show how the future conditional quantiles of commodities returns depend on both ex post and ex ante uncertainty. Empirical analysis of the most liquid commodities covering main sectors, including energy, food, agriculture, and precious and industrial metals, reveal several important stylized facts. We document common patterns of the dependence between future quantile returns and ex post as well as ex ante volatilities. We further show that the conditional returns distribution is platykurtic. The approach can serve as a useful risk management tool for investors interested in commodity futures contracts.

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  • František Čech & Jozef Baruník, 2019. "Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(9), pages 1167-1189, September.
    • Handle: RePEc:wly:jfutmk:v:39:y:2019:i:9:p:1167-1189
    DOI: 10.1002/fut.22017
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    Cited by:

    1. Ning Zhang & Yujing Gong & Xiaohan Xue, 2023. "Less disagreement, better forecasts: Adjusted risk measures in the energy futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(10), pages 1332-1372, October.
    2. Gong, Xu & Xu, Jun & Liu, Tangyong & Zhou, Zicheng, 2022. "Dynamic volatility connectedness between industrial metal markets," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    3. Zhang, Ning & Gong, Yujing & Xue, Xiaohan, 2023. "Less disagreement, better forecasts: adjusted risk measures in the energy futures market," LSE Research Online Documents on Economics 118451, London School of Economics and Political Science, LSE Library.

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