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Measuring parametric and semiparametric downside risks of selected agricultural commodities

Author

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  • Dejan Živkov

    (Novi Sad School of Business, University of Novi Sad, Novi Sad, Serbia)

  • Marijana Joksimović

    (Faculty of Finances, Banking and Audit, Alfa University, Belgrade, Serbia)

  • Suzana Balaban

    (Faculty of Finances, Banking and Audit, Alfa University, Belgrade, Serbia)

Abstract

In this paper, we evaluate the downside risk of six major agricultural commodities - corn, wheat, soybeans, soybean meal, soybean oil and oats. For research purposes, we first use an optimal generalised autoregressive conditional heteroscedasticity (GARCH) model to create residuals, which we later use for measuring downside risks via parametric and semiparametric approaches. Modified value-at-risk (mVaR) and modified conditional value-at-risk (mCVaR) provide more accurate downside risk results than do ordinary value-at-risk (VaR) and conditional value-at-risk (CVaR). We report that soybean oil has the lowest mVaR and mCVaR because it has two very favourable features - skewness around zero and low kurtosis. The second-best commodity is soybeans. The worst-performing downside risk results are in wheat and oats, primarily because of their very high kurtosis values. On the basis of the results, we propose to investors and various agents involved with these agricultural assets that they reduce the risk of loss by combining these assets with other financial or commodity assets that have low risk.

Suggested Citation

  • Dejan Živkov & Marijana Joksimović & Suzana Balaban, 2021. "Measuring parametric and semiparametric downside risks of selected agricultural commodities," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(8), pages 305-315.
    • Handle: RePEc:caa:jnlage:v:67:y:2021:i:8:id:148-2021-agricecon
    DOI: 10.17221/148/2021-AGRICECON
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    References listed on IDEAS

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    1. Balteş Nicolae & Rodean (Cozma) Maria-Daciana, 2019. "Technical analysis in estimating currency risk portfolios: case study: commercial banks in Romania," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 32(1), pages 622-634, January.
    2. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
    3. Wyn Morgan & John Cotter & Kevin Dowd, 2012. "Extreme Measures of Agricultural Financial Risk," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 65-82, February.
    4. Huang, Wen & Huang, Zhuo & Matei, Marius & Wang, Tianyi, 2012. "Price Volatility Forecast for Agricultural Commodity Futures： The Role of High Frequency Data," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 83-103, December.
    5. Gema Fernández-Avilés & José-María Montero & Lidia Sanchis-Marco, 2020. "Extreme downside risk co-movement in commodity markets during distress periods: a multidimensional scaling approach," The European Journal of Finance, Taylor & Francis Journals, vol. 26(12), pages 1207-1237, August.
    6. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    7. Stergios Xouridas, 2015. "Agricultural Financial Risks Resulting from Extreme Events," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 192-220, February.
    8. Ji, Qiang & Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model," Energy Economics, Elsevier, vol. 75(C), pages 14-27.
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    1. Noureddine Benlagha & Wafa Abdelmalek, 2024. "Dynamic connectedness between energy and agricultural commodities: insights from the COVID-19 pandemic and Russia–Ukraine conflict," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 14(3), pages 781-825, September.
    2. Živkov, Dejan & Manić, Slavica & Gajić-Glamočlija, Marina, 2024. "How do precious and industrial metals hedge oil in a multi-frequency semiparametric CVaR portfolio?," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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