Ambiguity arbitrage

Saeed Badri, Aurélien Baillon () and Bertrand Tavin
Additional contact information
Saeed Badri: VU - Vrije Universiteit Amsterdam [Amsterdam]
Aurélien Baillon: EM - EMLyon Business School, GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - UJM EPE - Université Jean Monnet (EPSCPE) - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique
Bertrand Tavin: EM - EMLyon Business School, GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - UL2 - Université Lumière - Lyon 2 - UJM - Université Jean Monnet - Saint-Étienne - UJM EPE - Université Jean Monnet (EPSCPE) - EM - EMLyon Business School - CNRS - Centre National de la Recherche Scientifique

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Abstract: Standard asset pricing models assume common risk preferences across markets. However, market segmentation and market-specific ambiguity may undermine this assumption. We develop a behavioral model combining narrow bracketing with source theory, allowing for differential responses to ambiguity across markets. The model predicts systematic cross-market valuation discrepancies, which can be exploited through what we call ambiguity arbitrage: a strategy that buys and sells probabilistically equivalent digital options across markets to exploit differences in ambiguity attitudes, and that can be made asymptotically riskless. Estimating the model for U.S. equities, crude oil, and the EUR/USD exchange rate, we document substantial source-dependent heterogeneity. The resulting price gaps are too large to be risk premia. Ambiguity arbitrage would have generated large cumulative profits over 2008-2025.

Date: 2026-08-25
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