Detecting asset price bubbles using deep learning

Francesca Biagini, Lukas Gonon, Andrea Mazzon and Thilo Meyer‐Brandis

Mathematical Finance, 2025, vol. 35, issue 1, 74-110

Abstract: In this paper, we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model‐independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. Under a given condition on the pricing of call options under asset price bubbles, we are able to provide a theoretical foundation of our approach for positive and continuous stochastic asset price processes. When such a condition is not satisfied, we focus on local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time‐dependent local volatility function to be a strict local martingale.

Date: 2025
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https://doi.org/10.1111/mafi.12443

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