Detecting asset price bubbles using deep learning

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

Papers from arXiv.org

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: 2022-10, Revised 2024-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-fmk and nep-rmg
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2210.01726

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