IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v9y2021i1p30-d510809.html
   My bibliography  Save this article

The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US

Author

Listed:
  • Jittima Singvejsakul

    (Department of Agricultural Economy and Development, Faculty of Agriculture, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Chukiat Chaiboonsri

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Songsak Sriboonchitta

    (Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand
    Puey Ungphakorn Center of Excellence in Econometrics, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Bayesian extreme value analysis was used to forecast the optimal point in agricultural commodity futures prices in the United States for cocoa, coffee, corn, soybeans and wheat. Data were collected daily between 2000 and 2020. The estimation of extreme value can be empirically interpreted as representing crises or unusual time series trends, while the extreme optimal point is useful for investors and agriculturists to make decisions and better understand agricultural commodities future prices warning levels. Results from the Non-stationary Extreme Value Analysis (NEVA) software package using Bayesian inference and the Newton-optimal methods provided optimal interval values. These indicated extreme maximum points of future prices to inform investors and agriculturists to sell the contract and product before the commodity prices dropped to the next local minimum values. Thus, agriculturists can use this information as an advanced warming of alarming points of agricultural commodity prices to predict the efficient quantity of their agricultural product to sell, with better ways to manage this risk.

Suggested Citation

  • Jittima Singvejsakul & Chukiat Chaiboonsri & Songsak Sriboonchitta, 2021. "The Optimization of Bayesian Extreme Value: Empirical Evidence for the Agricultural Commodities in the US," Economies, MDPI, vol. 9(1), pages 1-10, March.
    • Handle: RePEc:gam:jecomi:v:9:y:2021:i:1:p:30-:d:510809
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/9/1/30/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/9/1/30/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Linyin Cheng & Amir AghaKouchak & Eric Gilleland & Richard Katz, 2014. "Non-stationary extreme value analysis in a changing climate," Climatic Change, Springer, vol. 127(2), pages 353-369, November.
    2. Torun Fretheim & Glenn Kristiansen, 2015. "Commodity market risk from 1995 to 2013: an extreme value theory approach," Applied Economics, Taylor & Francis Journals, vol. 47(26), pages 2768-2782, June.
    3. Maarten van Oordt & Philip Stork & Casper de Vries, 2013. "On agricultural commodities' extreme price risk," DNB Working Papers 403, Netherlands Central Bank, Research Department.
    4. Park, Eunchun & Maples, Josh, 2018. "Extreme Events and Serial Dependence in Commodity Prices," 2018 Annual Meeting, August 5-7, Washington, D.C. 274469, Agricultural and Applied Economics Association.
    5. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    6. Polyak, B.T., 2007. "Newton's method and its use in optimization," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1086-1096, September.
    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. Zheng, Yixing & Ramsey, Austin F., 2022. "Extreme Correlation Between Daily Basis Returns of Local Corn Markets in North Carolina," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322373, Agricultural and Applied Economics Association.

    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. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    2. M. Naresh Kumar & V. Sree Hari Rao, 2015. "A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 83-102, June.
    3. Hongxiang Yan & Hamid Moradkhani, 2016. "Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 203-225, March.
    4. Xia Yang & Jing Zhang & Wei-Xin Ren, 2018. "Threshold selection for extreme value estimation of vehicle load effect on bridges," International Journal of Distributed Sensor Networks, , vol. 14(2), pages 15501477187, February.
    5. Xin Chen & Zhangming Shan & Decai Tang & Biao Zhou & Valentina Boamah, 2023. "Interest rate risk of Chinese commercial banks based on the GARCH-EVT model," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
    6. Jian Zhou, 2012. "Extreme risk measures for REITs: a comparison among alternative methods," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 113-126, January.
    7. Gonzales-Martínez, Rolando, 2008. "Medidas de Riesgo Financiero y una Aplicación a las Variaciones de Depósitos del Sistema Financiero Boliviano [Risk Measures and an Application to the Withdrawals of Deposits in the Bolivian Financ," MPRA Paper 14700, University Library of Munich, Germany.
    8. Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    9. Mizgier, Kamil J. & Hora, Manpreet & Wagner, Stephan M. & Jüttner, Matthias P., 2015. "Managing operational disruptions through capital adequacy and process improvement," European Journal of Operational Research, Elsevier, vol. 245(1), pages 320-332.
    10. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    11. Davide Ferrari & Sandra Paterlini, 2009. "The Maximum Lq-Likelihood Method: An Application to Extreme Quantile Estimation in Finance," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 3-19, March.
    12. Eling, Martin & Wirfs, Jan Hendrik, 2016. "Cyber Risk: Too Big to Insure? Risk Transfer Options for a mercurial risk class," I.VW HSG Schriftenreihe, University of St.Gallen, Institute of Insurance Economics (I.VW-HSG), volume 59, number 59.
    13. David E. Giles & Qinlu Chen, 2014. "Risk Analysis for Three Precious Metals: An Application of Extreme Value Theory," Econometrics Working Papers 1402, Department of Economics, University of Victoria.
    14. Dey Ashim Kumar & Das Kumer Pial, 2020. "Predicting Federal Funds Rate Using Extreme Value Theory," Stochastics and Quality Control, De Gruyter, vol. 35(1), pages 1-15, June.
    15. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    16. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    17. Ahmed, Dilan & Soleymani, Fazlollah & Ullah, Malik Zaka & Hasan, Hataw, 2021. "Managing the risk based on entropic value-at-risk under a normal-Rayleigh distribution," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    18. Zhang, Ruijun & Lu, Jie & Zhang, Guangquan, 2011. "A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces," European Journal of Operational Research, Elsevier, vol. 215(1), pages 194-203, November.
    19. Allen, David E. & Singh, Abhay K. & Powell, Robert J., 2013. "EVT and tail-risk modelling: Evidence from market indices and volatility series," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 355-369.
    20. Chiara Lattanzi & Manuele Leonelli, 2019. "A changepoint approach for the identification of financial extreme regimes," Papers 1902.09205, arXiv.org.

    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:gam:jecomi:v:9:y:2021:i:1:p:30-:d:510809. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.