MCFAM Seminar - Detecting asset price bubbles using deep learning

Speaker: Lukas Gonon

Abstract: In this talk we present a novel deep learning methodology 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. In addition, we provide a theoretical foundation of our approach in the framework of 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.

Joint work with Francesca Biagini, Andrea Mazzon and Thilo Meyer-Brandis.