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Predicción de la volatilidad en los mercados del petróleo mexicano a través de modelos CgarCH asimétricos bajo dos supuestos distribucionales

Author

Listed:
  • Raúl de Jesús Gutiérrez

    (Profesor de tiempo completo de la Facultad de Economía, UAEMEX. Universidad Autónoma del Estado de México)

  • Miriam Sosa Castro

    (Profesora de Tiempo Completo de la Universidad Autónoma Metropolitana, Iztapalapa. México)

Abstract

In this paper, three symmetric and asymmetric CGARCH models are estimated to evaluate and improve volatility forecasts in Mexican crude oil markets under different distributional assumptions (Normal and Laplace). Empirical evidence shows that CGARCH and CGARCH-A2 models yield the most accurate one-five-and twenty-day out-of-sample volatility forecasts for Istmo and Maya crude oil returns in comparison to the traditional GARCH models, including the CGARCH-A1 based model. These results are supported using symmetric and asymmetric forecast error measures and the Hansen's (2005) superior predictive ability test. The improvement in volatility forecasting has important economics and financial implications for participants in Mexican crude oil markets, in particular the Mexican governmenttion-JEL: Q40; E30; C32; C5

Suggested Citation

  • Raúl de Jesús Gutiérrez & Miriam Sosa Castro, 2019. "Predicción de la volatilidad en los mercados del petróleo mexicano a través de modelos CgarCH asimétricos bajo dos supuestos distribucionales," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 42(120), pages 253-267, Diciembre.
    • Handle: RePEc:cud:journl:v:42:y:2019:i:120:p:253-267
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    More about this item

    Keywords

    Crude oil; Volatility forecasting; CGARCH models; Superior predictive ability test;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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