Probabilistic Predictions of Option Prices with Modular Approximate Bayesian Inference

Worapree Maneesoonthorn, David T. Frazier and Gael M. Martin

Papers from arXiv.org

Abstract: A new approximate Bayesian inferential framework is proposed that exploits multiple information sources -- daily spot returns, high-frequency spot data and option prices -- and enables fast calculation of probabilistic predictions of future option prices. This approach operates directly from the theoretical option pricing model, and does not require an explicit statistical model, or likelihood, for the observed option prices. We demonstrate that our approach produces accurate probabilistic option-price predictions in realistic scenarios and, despite not explicitly modelling option-pricing errors via a statistical model, the method is shown to be robust to the presence of such errors. Predictive accuracy based on the Heston option pricing model is illustrated empirically for short-maturity options, with the rapidity of real-time updates of the predictive distributions highlighted.

Date: 2024-11, Revised 2026-05
New Economics Papers: this item is included in nep-ecm and nep-for
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