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Transmisión de precios futuros de maíz del Chicago Board of Trade al mercado spot mexicano

Author

Listed:
  • Francisco Ortiz Arango

    (Universidad Panamericana, México)

  • Alma Nelly Montiel Guzmán

    (Instituto Politécnico Nacional, México)

Abstract

En México, el uso del programa de coberturas de la Agencia de Servicios a la Comercialización y Desarrollo de Mercados Agropecuarios (ASERCA) es un instrumento que ha sido utilizado por los productores de maíz (principalmente blanco) para la adquisición de productos derivados en el Chicago Board of Trade (CBOT), cuyo subyacente es el maíz amarillo calidad US#2. En un entorno de alta volatilidad en los precios del maíz, los precios del CBOT deberían ajustarse con los precios spot domésticos para incentivar a los productores mexicanos a participar en el programa, sin embargo, mediante un análisis de volatilidad estocástica multivariante durante el periodo de 2007 a 2012, se mostró que el precio de mercado de futuros de maíz no se encuentra fuertemente relacionado con los precios registrados en algunos estados del país, por lo que se puede inferir que la cobertura mediante el programa ASERCA no cumple adecuadamente con su propósito de proteger a los agricultores nacionales que siembran maíz blanco, a pesar de que su uso se ha incrementado.

Suggested Citation

  • Francisco Ortiz Arango & Alma Nelly Montiel Guzmán, 2017. "Transmisión de precios futuros de maíz del Chicago Board of Trade al mercado spot mexicano," Contaduría y Administración, Accounting and Management, vol. 62(3), pages 924-940, Julio-Sep.
  • Handle: RePEc:nax:conyad:v:62:y:2017:i:3:p:924-940
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    References listed on IDEAS

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    1. Karolyi, G Andrew & Stulz, Rene M, 1996. "Why Do Markets Move Together? An Investigation of U.S.-Japan Stock Return Comovements," Journal of Finance, American Finance Association, vol. 51(3), pages 951-986, July.
    2. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    3. Philipov, Alexander & Glickman, Mark E., 2006. "Multivariate Stochastic Volatility via Wishart Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 313-328, July.
    4. Alexander Philipov & Mark Glickman, 2006. "Factor Multivariate Stochastic Volatility via Wishart Processes," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 311-334.
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    More about this item

    Keywords

    Precios de maíz; Chicago Board of Trade; Volatilidad estocástica multivariante;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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