Accurately forecasting the price of oil, the world's most actively traded commodity, is of great importance to both academics and practitioners. We contribute by proposing a functional time series based method to model and forecast oil futures. Our approach boasts a number of theoretical and practical advantages including effectively exploiting underlying process dynamics missed by classical discrete approaches. We evaluate the finite‐sample performance against established benchmarks using a model confidence set test. A realistic out‐of‐sample exercise provides strong support for the adoption of our approach with it residing in the superior set of models in all considered instances.
- crude oil
- functional time series
- futures contracts
- futures markets
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
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- Queen's Business School (QBS) - Senior Lecturer
- Institute of Electronics, Communications & Information Technology