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Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19

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Abstract

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets.

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Notes

  1. In this regard, Degiannakis et al. (2020) has recently used high frequency data on major agricultural commodities (corn, rough rice, soybeans, sugar, and wheat) from the CME/ICE within the heterogeneous autoregressive (HAR) model to forecast the realized volatility of those agricultural commodities.

  2. A recent paper dealing with the quantile spillovers among Asia–Pacific currencies has applied a comparable approach (Bouri et al. 2020).

  3. The authors show that it is very difficult to beat the forecasting ability of the 5-min realized variance measure.

  4. We thank an anonymous reviewer for making this important suggestion.

  5. Results of the GSDAF on the first differences of volatility series are available from the authors upon request.

  6. The TCI varies between 0 and 100.

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Acknowledgement

Najaf Iqbal gratefully acknowledges the support from Academic Financial Aid Project for Top Talents in Universities of Anhui via grant number gxbjZD14.

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Authors and Affiliations

  1. School of Finance, Anhui University of Finance and Economics, Anhui, China

    Najaf Iqbal

  2. School of Business, Lebanese American University, Beirut, Lebanon

    Elie Bouri

  3. Montpellier Business School, Montpellier, France

    Oksana Grebinevych & David Roubaud

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  1. Najaf Iqbal
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See Figs. 9, 10, 11, 12, 13, 14, 15

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Iqbal, N., Bouri, E., Grebinevych, O. et al. Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19. Ann Oper Res 330, 305–334 (2023). https://doi.org/10.1007/s10479-022-04522-9

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