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

Raúl de Jesús Gutiérrez and Miriam Sosa Castro
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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

Cuadernos de Economía - Spanish Journal of Economics and Finance, 2019, vol. 42, issue 120, 253-267

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

Keywords: Crude oil; Volatility forecasting; CGARCH models; Superior predictive ability test (search for similar items in EconPapers)
JEL-codes: C32 C52 E30 Q40 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:cud:journl:v:42:y:2019:i:120:p:253-267

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