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Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis

Author

Listed:
  • Luis F. Espinoza-Audelo

    (Universidad Autónoma de Occidente, Unidad Regional Culiacán, Culiacán 80200, Mexico)

  • Maricruz Olazabal-Lugo

    (Universidad Autónoma de Occidente, Unidad Regional Culiacán, Culiacán 80200, Mexico)

  • Fabio Blanco-Mesa

    (Facultad de Ciencias Económicas y Administrativas, Escuela de Administración de Empresas, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150003, Colombia)

  • Ernesto León-Castro

    (Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Av. Alonso de Ribera 2850, Concepción 4030000, Chile)

  • Victor Alfaro-Garcia

    (Facultad de Contaduría y Ciencias Administrativas, Universidad Michoacana de San Nicolas de Hidalgo, Gral. Francisco J. Múgica S/N, C.U., Morelia 58030, Mexico)

Abstract

Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary to generate models that allow the incorporation of the knowledge and expectations of the markets and thus include in the results obtained not only the historical information, but also the present and future information. The present article introduces a new extension of the ordered weighted averaging (OWA) operator called the Bonferroni probabilistic ordered weighted average (B-POWA) operator. This operator is designed to unify in a single formulation the interrelation of the values given in a data set by the Bonferroni means and a weighted and probabilistic vector that models the attitudinal character, expectations, and knowledge of the decision-maker of a problem. The paper also studies the main characteristics and some families of the B-POWA operator. An illustrative example is also proposed to analyze the mathematical process of the operator. Finally, an application to corn price estimation designed to calculate the error between the price of an agricultural commodity using the B-POWA operator and a leading global market company is presented. The results show that the proposed operator exhibits a better general performance than the traditional methods.

Suggested Citation

  • Luis F. Espinoza-Audelo & Maricruz Olazabal-Lugo & Fabio Blanco-Mesa & Ernesto León-Castro & Victor Alfaro-Garcia, 2020. "Bonferroni Probabilistic Ordered Weighted Averaging Operators Applied to Agricultural Commodities’ Price Analysis," Mathematics, MDPI, vol. 8(8), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1350-:d:398006
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    References listed on IDEAS

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    1. Elkhan Richard Sadik-Zada & Wilhelm Loewenstein & Yadulla Hasanli, 2019. "Commodity Revenues, Agricultural Sector and the Magnitude of Deindustrialization: A Novel Multisector Perspective," Economies, MDPI, vol. 7(4), pages 1-15, November.
    2. Offer Lieberman & Peter Phillips, 2008. "Refined Inference on Long Memory in Realized Volatility," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 254-267.
    3. Željko Stević & Dragan Pamučar & Marko Subotić & Jurgita Antuchevičiene & Edmundas Kazimieras Zavadskas, 2018. "The Location Selection for Roundabout Construction Using Rough BWM-Rough WASPAS Approach Based on a New Rough Hamy Aggregator," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    4. Jarrett, Uchechukwu & Mohaddes, Kamiar & Mohtadi, Hamid, 2019. "Oil price volatility, financial institutions and economic growth," Energy Policy, Elsevier, vol. 126(C), pages 131-144.
    5. Richard E. Just & Gordon C. Rausser, 1981. "Commodity Price Forecasting with Large-Scale Econometric Models and the Futures Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 63(2), pages 197-208.
    6. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    7. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    8. Elkhan Richard Sadik-Zada, 2020. "Distributional Bargaining and the Speed of Structural Change in the Petroleum Exporting Labor Surplus Economies," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 32(1), pages 51-98, January.
    9. I-Hsuan Ethan Chiang & W. Keener Hughen & Jacob S. Sagi, 2015. "Estimating Oil Risk Factors Using Information from Equity and Derivatives Markets," Journal of Finance, American Finance Association, vol. 70(2), pages 769-804, April.
    10. Clinton, Joshua D. & Lewis, David E., 2008. "Expert Opinion, Agency Characteristics, and Agency Preferences," Political Analysis, Cambridge University Press, vol. 16(1), pages 3-20, January.
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    Cited by:

    1. Victor G. Alfaro-Garcia & Fabio Blanco-Mesa & Ernesto León-Castro & Jose M. Merigo, 2022. "Bonferroni Weighted Logarithmic Averaging Distance Operator Applied to Investment Selection Decision Making," Mathematics, MDPI, vol. 10(12), pages 1-13, June.
    2. Umut Asan & Ayberk Soyer, 2022. "A Weighted Bonferroni-OWA Operator Based Cumulative Belief Degree Approach to Personnel Selection Based on Automated Video Interview Assessment Data," Mathematics, MDPI, vol. 10(9), pages 1-33, May.

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