Reading the Future of Oil: A Noncausal Approach to Supply News Shocks

Stéphane Auray (), Zakaria Moussa () and Arthur Thomas ()
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Stéphane Auray: ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - Groupe ENSAE-ENSAI - Groupe des Écoles Nationales d'Économie et Statistique, Rennes SB - Rennes School of Business
Zakaria Moussa: LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université
Arthur Thomas: LEDa - Laboratoire d'Economie de Dauphine - IRD - Institut de Recherche pour le Développement - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: This paper proposes a new strategy to identify oil supply news shocks by combining a Bayesian noncausal structural VAR with a Max-Share approach. The framework jointly resolves the problems of non-fundamentalness and recoverability that undermine standard (proxy) SVAR methods. Exploiting non-Gaussianity in a multivariate Student−t specification, we recover structural shocks from a two sided moving average representation and isolate the expectation driven component of oil supply innovations without external instruments. Applied to global oil market data, the model supports a non fundamental representation and detects anticipatory price and inventory movements consistent with rational expectations storage behavior. The identified shocks explain a substantial fraction of real oil price fluctuations, notably in the late 1970s–1980s and during the 2014–15 collapse, while the COVID-19 episode is predominantly demand driven. Decomposing global supply shows that these shocks are primarily OPEC-driven and generate stagflationary responses in output and inflation, underscoring the central role of expectations in oil market dynamics.

Keywords: Non- fundamentalness; Structural Non-causal VAR; Max-Share methodology; Global oil market; Oil supply news shocks (search for similar items in EconPapers)
Date: 2026-01-13
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