IDEAS home Printed from https://ideas.repec.org/a/caa/jnlage/v69y2023i3id371-2022-agricecon.html
   My bibliography  Save this article

How to reduce the extreme risk of losses in corn and soybean markets? Construction of a portfolio with European stock indices

Author

Listed:
  • Dejan Živkov

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

  • Biljana Stankov

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

  • Nataša Papić-Blagojević

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

  • Jelena Damnjanović

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

  • Željko Račić

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

Abstract

Because of the COVID-19 pandemic and the war in Ukraine, agricultural commodities had significant price increases, which inevitably implies high risk. In this article, we try to mitigate the extreme risk of corn and soybeans by constructing multivariate portfolios with developed and emerging European stock indices. We measured extreme risk via conditional value at risk. To address different goals that investors might prefer, we produced portfolios with the lowest risk and highest return-to-risk ratio. According to the results, corn and soybeans had relatively high portfolio shares. However, they are the riskiest assets because they have a very low pairwise correlation with the stock indices. Portfolios with emerging European indices had better risk-reducing results, considering both agricultural commodities because these indices are less risky than developed indices. In particular, the risk reductions of corn were 38% and 50% in the portfolios with developed and emerging stock indices, respectively, whereas, for soybeans, the results were 28% and 41%, respectively. In optimal portfolios, emerging European stock indices had the upper hand in most cases.

Suggested Citation

  • Dejan Živkov & Biljana Stankov & Nataša Papić-Blagojević & Jelena Damnjanović & Željko Račić, 2023. "How to reduce the extreme risk of losses in corn and soybean markets? Construction of a portfolio with European stock indices," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(3), pages 109-118.
  • Handle: RePEc:caa:jnlage:v:69:y:2023:i:3:id:371-2022-agricecon
    DOI: 10.17221/371/2022-AGRICECON
    as

    Download full text from publisher

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/371/2022-AGRICECON.html
    Download Restriction: free of charge

    File URL: http://agricecon.agriculturejournals.cz/doi/10.17221/371/2022-AGRICECON.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/371/2022-AGRICECON?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Saâdaoui, Foued & Ben Jabeur, Sami & Goodell, John W., 2022. "Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict," Finance Research Letters, Elsevier, vol. 49(C).
    2. Elliott, Lisa & Elliott, Matthew & Slaa, Chad Te & Wang, Zhiguang, 2020. "New generation grain contracts in corn and soybean commodity markets," Journal of Commodity Markets, Elsevier, vol. 20(C).
    3. Asier Minondo, 2021. "Impact of COVID-19 on the trade of goods and services in Spain," Applied Economic Analysis, Emerald Group Publishing Limited, vol. 29(85), pages 58-76, February.
    4. Naeem, Muhammad Abubakr & Hasan, Mudassar & Arif, Muhammad & Suleman, Muhammad Tahir & Kang, Sang Hoon, 2022. "Oil and gold as a hedge and safe-haven for metals and agricultural commodities with portfolio implications," Energy Economics, Elsevier, vol. 105(C).
    5. Nanjun ZHU & Yulin FENG, 2017. "Optimal Change-Loss Reinsurance Contract Design under Tail Risk Measures for Catastrophe Insurance," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 225-242.
    6. Alexakis, Christos & Bagnarosa, Guillaume & Dowling, Michael, 2017. "Do cointegrated commodities bubble together? the case of hog, corn, and soybean," Finance Research Letters, Elsevier, vol. 23(C), pages 96-102.
    7. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    8. Li, Jie & Huang, Huaxia & Xiao, Xiao, 2012. "The sovereign property of foreign reserve investment in China: A CVaR approach," Economic Modelling, Elsevier, vol. 29(5), pages 1524-1536.
    9. Lauren Chenarides & Carola Grebitus & Jayson L. Lusk & Iryna Printezis, 2021. "Food consumption behavior during the COVID‐19 pandemic," Agribusiness, John Wiley & Sons, Ltd., vol. 37(1), pages 44-81, January.
    10. Fei Luan & Weiguo Zhang & Yongjun Liu & Guoqiang Wang, 2022. "Robust International Portfolio Optimization with Worst-Case Mean-LPM," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, February.
    11. Feng Wu & Zhengfei Guan & Robert J. Myers, 2011. "Volatility spillover effects and cross hedging in corn and crude oil futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(11), pages 1052-1075, November.
    12. Christos Alexakis & Guillaume Bagnarosa & Michael Dowling, 2017. "Do cointegrated commodities bubble together? the case of hog, corn, and soybean," Post-Print hal-02002169, HAL.
    13. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mourad Zmami & Ousama Ben-Salha, 2023. "What factors contribute to the volatility of food prices? New global evidence," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(5), pages 171-184.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caspi, Itamar & Graham, Meital, 2018. "Testing for bubbles in stock markets with irregular dividend distribution," Finance Research Letters, Elsevier, vol. 26(C), pages 89-94.
    2. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    3. 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).
    4. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).
    5. Gomez-Gonzalez, Jose Eduardo & Sanin-Restrepo, Sebastian, 2018. "The maple bubble: A history of migration among Canadian provinces," Journal of Housing Economics, Elsevier, vol. 41(C), pages 57-71.
    6. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    7. Živkov, Dejan & Balaban, Suzana & Simić, Milica, 2024. "Hedging gas in a multi-frequency semiparametric CVaR portfolio," Research in International Business and Finance, Elsevier, vol. 67(PA).
    8. Xu, Qifa & Zhou, Yingying & Jiang, Cuixia & Yu, Keming & Niu, Xufeng, 2016. "A large CVaR-based portfolio selection model with weight constraints," Economic Modelling, Elsevier, vol. 59(C), pages 436-447.
    9. Fang, Ming & Lin, Yizhou & Chang, Chiu-Lan, 2023. "Positive and negative price bubbles of Chinese agricultural commodity futures," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 456-471.
    10. Fatima, Hira & Ahmed, Mumtaz, 2019. "Testing for Exuberance Behavior in Agricultural Commodities of Pakistan," MPRA Paper 95304, University Library of Munich, Germany.
    11. Wang, Xiao-Qing & Wu, Tong & Zhong, Huaming & Su, Chi-Wei, 2023. "Bubble behaviors in nickel price: What roles do geopolitical risk and speculation play?," Resources Policy, Elsevier, vol. 83(C).
    12. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    13. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    14. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    15. Giovanni Masala & Filippo Petroni, 2023. "Drawdown risk measures for asset portfolios with high frequency data," Annals of Finance, Springer, vol. 19(2), pages 265-289, June.
    16. Ke Zhou & Jiangjun Gao & Duan Li & Xiangyu Cui, 2017. "Dynamic mean–VaR portfolio selection in continuous time," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1631-1643, October.
    17. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    18. Rostagno, Luciano Martin, 2005. "Empirical tests of parametric and non-parametric Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures for the Brazilian stock market index," ISU General Staff Papers 2005010108000021878, Iowa State University, Department of Economics.
    19. Guglielmo Maria Caporale & Anamaria Diana Sova & Robert Sova, 2024. "The Covid‐19 pandemic and European trade flows: Evidence from a dynamic panel model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2563-2580, July.
    20. Hao Xu & Niu Niu & Dongmei Li & Chengjie Wang, 2024. "A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks," Sustainability, MDPI, vol. 16(10), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlage:v:69:y:2023:i:3:id:371-2022-agricecon. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.