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.Comment: 28 pages, 4 figures, to appear in European Financial Managemen