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Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness

Author

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  • Mishra Arunendra

    (Department of Food Business Management and Entrepreneurship, National Institute of Food Technology Entrepreneurship and Management, Sonipat (Delhi NCR) – 131028, India)

  • Kumar R Prasanth

    (Department of Food Business Management and Entrepreneurship, National Institute of Food Technology Entrepreneurship and Management, Sonipat (Delhi NCR) – 131028, India)

Abstract

In this article the dynamic connectedness between the five agricultural commodities is examined by implementing the Diebold and Yılmaz (VAR based) and Time--Varying Parameter Vector Autoregressions (TVP-VAR) measures for understanding the time-varying variance-covariance mechanism using daily data for the period of 2005 to 2019. The findings reveal that at an overall level all the commodity prices are less susceptible to significant volatility shocks from other commodities specifically before the introduction of the pan-India electronic trading portal (eNAM). Cotton prices do not show any variation due to spillover from others for the entire study period. The volatility spillover is visible post eNAM period particularly for the commodity stock prices. Whereas at an overall level the total directional connectedness has gone down in the post eNAM era. The network analysis suggests that the commodity stock prices show a stronger association as compared to market prices. Generally commodity prices show volatility connectedness but with respect to their own market which means strong spillover is missing among both the markets.

Suggested Citation

  • Mishra Arunendra & Kumar R Prasanth, 2021. "Agricultural commodities: An integrated approach to assess the volatility spillover and dynamic connectedness," Economics and Business Review, Sciendo, vol. 7(4), pages 28-53, December.
    • Handle: RePEc:vrs:ecobur:v:7:y:2021:i:4:p:28-53:n:2
    DOI: 10.18559/ebr.2021.4.3
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    References listed on IDEAS

    as
    1. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    4. Sim, Nicholas & Zhou, Hongtao, 2015. "Oil prices, US stock return, and the dependence between their quantiles," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 1-8.
    5. Dahl, Roy Endré & Jonsson, Erlendur, 2018. "Volatility spillover in seafood markets," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 44-59.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    dynamic connectedness; TVP-VAR; price volatility; volatility spillover; agricultural commodities; network diagrams;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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