Forecasting Volatility in Mexican Crude Oil Markets Using Asymmetric CGARCH Models Based on Two Distributional Assumptions

Authors

  • Raúl de Jesús Gutiérrez Universidad Autónoma del Estado de México
  • Miriam Sosa Castro Universidad Nacional Autónoma de México

DOI:

https://doi.org/10.32826/cude.v42i120.88

Keywords:

Crude oil, Volatility forecasting, CGARCH models, Superior predictive ability test

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 government.

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Published

2019-02-12

Issue

Vol. 42 No. 120 (2019): Pages 207-284 (September - December 2019)

Section

Articles

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