Detecting asset price bubbles using deep learning
In this talk we present a novel deep learning methodology to detect financial asset bubbles by using observed call option prices. We start with an introduction to deep learning and asset price bubbles. We then examine the pitfalls of a naive approach for deep learning-based bubble detection and subsequently introduce our method. We provide theoretical foundations for the method in the context of local volatility models and show numerical results from experiments both on simulated and market data.
The talk is based on joint work with Francesca Biagini, Andrea Mazzon and Thilo Meyer-Brandis.