Uncovering Predictability in the Evolution of the WTI Oil Futures Curve
Date
2019
Authors
Kearney, Fearghal
Shang, Han Lin
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Publisher
Taylor & Francis
Abstract
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, which resides in the superior set of models in all considered instances.
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European Financial Management
Type
Journal article
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2037-12-31
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