Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models

Yingying Xu and Donald Lien

Journal of Forecasting, 2022, vol. 41, issue 2, 259-278

Abstract: This paper compares Generalized Autoregressive Score (GAS) models and GARCH‐type models on their forecasting abilities for crude oil and natural gas spot and futures returns from developing and developed markets over multiple horizons. The out‐of‐sample forecasting results based on two loss functions and the Diebold–Mariano predictive accuracy test for multiple models show that the GAS framework outperforms GARCH and EGARCH models, particularly for crude oil assets. For natural gas, no specific model retains an advantage over the other two models as the predictive accuracy changes over forecasting horizons and varies across markets. Meanwhile, the GAS model performs well in both developed and developing markets. The cumulated sum of squared forecast error differential (CSSFED) graphically monitors the evolution of the relative forecasting performance of different models and shows that the superiority of GARCH is vulnerable to extraordinary event shocks. Over the short‐term forecasting (less than or equal to 1 month ahead), the GAS framework shows a prominent advantage over GARCH and EGARCH models for crude oil assets.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:41:y:2022:i:2:p:259-278

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