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Price Volatility Dependence Structure Change Among Agricultural Commodity Futures Due to Extreme Event: An Analysis with the Vine Copula

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13199))

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Abstract

Since the COVID-19 spreads, global food prices have continued to rise and become more volatile because of food security panic, global food supply chain disruption, and unfavorable weather conditions for cultivation. This paper aims to study and compare the dependence structure in price volatility among agricultural commodity futures before and during the COVID-19 pandemic, with different vine copulas, namely the R-vine, C-vine, and D-vine. The daily closing prices of the agricultural commodity futures are used in the investigation, including Corn, Wheat, Oat, Soybean, Rice, Sugar, Coffee, Cocoa, and Orange, traded in the Chicago Board of Trade (CBOT) from January 2016 to July 2021. The conditional volatilities were estimated using the best fit GARCH model with the student-t distribution. The empirical results highlight the dependence structures captured by the C-vine, D-vine, and R-vine copula-based models before and during the COVID-19 pandemic. Although the C-vine copula structures of the two different periods are unchanged, the details of the copula family in such a structure differ. In the case of D-vine and R-vine copulas, the details of the copula families and their vine structures of two different periods are significantly different, meaning that COVID-19 impacts the price volatility dependence structure among the agricultural commodity futures examined. Based on the AIC, the most appropriate dependence structure for pre-COVID-19 period is the C-vine copula, while the during-COVID-19 period is the D-vine copula. The dependence structure of agricultural commodity futures prices can be used in other risk analysis and management methods such as value at risk (VaR), portfolio optimization, and hedging.

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Acknowledgments

This research work was partially supported by Chiang Mai University.

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Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Konnika Palason & Tanapol Rattanasamakarn

  2. Center of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Roengchai Tansuchat

Authors
  1. Konnika Palason
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  2. Tanapol Rattanasamakarn
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  3. Roengchai Tansuchat
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Correspondence to Roengchai Tansuchat .

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Editors and Affiliations

  1. Osaka Prefecture University, Sakai, Osaka, Japan

    Katsuhiro Honda

  2. University of Hyogo, Kobe, Japan

    Tomoe Entani

  3. Osaka Prefecture University, Sakai, Japan

    Seiki Ubukata

  4. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  5. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

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Palason, K., Rattanasamakarn, T., Tansuchat, R. (2022). Price Volatility Dependence Structure Change Among Agricultural Commodity Futures Due to Extreme Event: An Analysis with the Vine Copula. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_30

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  • DOI: https://doi.org/10.1007/978-3-030-98018-4_30

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