Forecasting energy prices: Quantile‐based risk models

Nicholas Apergis ()

Journal of Forecasting, 2023, vol. 42, issue 1, 17-33

Abstract: The goal of this paper is to use a new modelling approach to extract quantile‐based oil and natural gas risk measures using quantile autoregressive distributed lag mixed‐frequency data sampling (QADL‐MIDAS) regression models. The analysis compares this model to a standard quantile auto‐regression (QAR) model and shows that it delivers better quantile forecasts at the majority of forecasting horizons. The analysis also uses the QADL‐MIDAS model to construct oil and natural gas prices risk measures proxying for uncertainty, third‐moment dynamics, and the risk of extreme energy realizations. The results document that these risk measures are linked to the future evolution of energy prices, while they are linked to the future evolution of US economic growth.

Date: 2023
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https://doi.org/10.1002/for.2898

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:1:p:17-33

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