Abstract:
In this talk we present deep learning techniques to detect financial asset bubbles by using observed call option prices and a model-independent algorithm. 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.
This talk is based on [1].
[1] Biagini, F., Gonon, L., Mazzon, A., Meyer -Brandis, T., Detecting asset price bubbles using deep learning, Preprint University of Munich, 2022.