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Uncovering predictability in the evolution of the WTI oil futures curve
Author(s) -
Kearney Fearghal,
Shang Han Lin
Publication year - 2020
Publication title -
european financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.311
H-Index - 64
eISSN - 1468-036X
pISSN - 1354-7798
DOI - 10.1111/eufm.12212
Subject(s) - predictability , futures contract , west texas intermediate , econometrics , sample (material) , commodity , computer science , set (abstract data type) , process (computing) , economics , financial economics , mathematics , statistics , finance , chemistry , operating system , chromatography , programming language
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.