Uncovering Predictability in the Evolution of the WTI Oil Futures Curve
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Kearney, Fearghal; Shang, Han Lin
Description
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...[Show more]
dc.contributor.author | Kearney, Fearghal | |
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dc.contributor.author | Shang, Han Lin | |
dc.date.accessioned | 2019-10-16T04:49:24Z | |
dc.identifier.issn | 1354-7798 | |
dc.identifier.uri | http://hdl.handle.net/1885/177003 | |
dc.description.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. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en_AU | |
dc.publisher | Taylor & Francis | |
dc.rights | © 2019 John Wiley & Sons, Ltd | |
dc.source | European Financial Management | |
dc.title | Uncovering Predictability in the Evolution of the WTI Oil Futures Curve | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 26 | |
dc.date.issued | 2019 | |
local.identifier.absfor | 150202 - Financial Econometrics | |
local.identifier.absfor | 010401 - Applied Statistics | |
local.identifier.ariespublication | u1027566xPUB115 | |
local.publisher.url | https://www.routledge.com/ | |
local.type.status | Published Version | |
local.contributor.affiliation | Kearney, Fearghal, Queen’s Management School Queen’s University Belfast | |
local.contributor.affiliation | Shang, Hanlin, College of Business and Economics, ANU | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 1 | |
local.bibliographicCitation.startpage | 1 | |
local.bibliographicCitation.lastpage | 20 | |
local.identifier.doi | 10.1111/eufm.12212 | |
dc.date.updated | 2022-02-20T07:21:20Z | |
local.identifier.scopusID | 2-s2.0-85062707454 | |
Collections | ANU Research Publications |
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