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arXiv · 2210.01726

Detecting asset price bubbles using deep learning

Francesca Biagini·Lukas Gonon·Andrea Mazzon·Thilo Meyer-Brandis
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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.

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Francesca Biagini, Lukas Gonon, Andrea Mazzon, Thilo Meyer-Brandis. 2022-10-04. Detecting asset price bubbles using deep learning. https://arxiv.org/abs/2210.01726

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