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Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast

Author

Listed:
  • José Eduardo Medina Reyes

    (Queen Mary University of London, UK)

  • Agustín Ignacio Cabrera Llanos

    (Instituto Politécnico Nacional, México)

  • Salvador Cruz Aké

    (Instituto Politécnico Nacional, México)

Abstract

El presente artículo compara los métodos de varianza condicional GARCH y EGARCH, con respecto a la propuesta Fuzzy Gaussian GARCH y Fuzzy Gaussian EGARCH. Se pronosticó la rentabilidad de cuatro tipos de cambio en periodicidad diaria desde enero 2015 a noviembre 2022 y fuera de muestra, enero 2019 y diciembre 2022. Los resultados revelan que los modelos Fuzzy GARCH y Fuzzy EGARCH estiman mejor el comportamiento de la volatilidad de las series del mercado cambiario en comparación con las técnicas tradicionales. Por lo que, la recomendación es estimar otras variables de alta volatilidad para verificar la eficiencia de las técnicas difusas, sin embargo, la principal limitación es que no es posible aplicar las pruebas econométricas tradicionales para técnicas difusas porque los parámetros no son estimados con el logaritmo de máxima verosimilitud. La estimación de los parámetros de los modelos GARCH y EGARCH con teoría difusa es la propuesta de originalidad. En conclusión, las metodologías difusas tienen menos error al pronosticar la volatilidad de los tipos de cambio dentro muestra y fuera de muestra.

Suggested Citation

  • José Eduardo Medina Reyes & Agustín Ignacio Cabrera Llanos & Salvador Cruz Aké, 2023. "Fuzzy Gaussian GARCH and Fuzzy Gaussian EGARCH Models: Foreign Exchange Market Forecast," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 18(3), pages 1-22, Julio - S.
    • Handle: RePEc:imx:journl:v:18:y:2023:i:3:p:2
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    References listed on IDEAS

    as
    1. Huarng, Kunhuang & Yu, Hui-Kuang, 2005. "A Type 2 fuzzy time series model for stock index forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 445-462.
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    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Yu, Hui-Kuang, 2005. "A refined fuzzy time-series model for forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(3), pages 657-681.
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    More about this item

    Keywords

    Lógica Difusa; GARCH; EGARCH; FUZZY GARCH; FUZZY EGARCH;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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