Forecasting volatility of crude oil markets

• Published in: Energy economics Vol. 31; no. 1; pp. 119 - 125
• Main Authors: Kang, Sang Hoon, Kang, Sang-Mok, Yoon, Seong-Min
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 2009; Elsevier; Elsevier Science Ltd
• Series: Energy Economics
• Subjects: Applied sciences; CGARCH; Crude oil; Crude oil prices; Economic data; Economic models; Economics; Effectiveness; Efficiency, Administrative; Energy; Energy economics; Exact sciences and technology; FIGARCH; Forecasting; Forecasting techniques; Forecasts; Fossil fuels and derived products; GARCH models; General, economic and professional studies; Long memory; Markets; Mathematical models; Memory; Methodology. Modelling; Oil; Oil market; Oil price; Persistence; Persistence Long memory CGARCH FIGARCH; Petroleum industry; Prices; Stochastic models; Studies; Volatility; USA, Texas; United Arab Emirates, Dubai; Persistence; C52; CGARCH; E30; C32; FIGARCH; Q40; Long memory; Performance evaluation; Volatility; Autoregressive model; Modeling; Petroleum; Price; Oil economy; Comparative study
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2008.09.006

This article investigates the efficacy of a volatility model for three crude oil markets — Brent, Dubai, and West Texas Intermediate (WTI) — with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices.