Daniele: Hi, Deep Learning Model; very lovely to meet you. Deep Learning Model: Hi Daniele, I cannot say it is a pleasure -- not sure what that means -- but this interaction is undoubtedly an outlier for me. But please call me 43420a6962c2. Daniele: Oh, ok, interesting name, I guess. Ok, 43420a6962c2, let's get cracking with this interview.

We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We address the high-dimensionality of market observed call prices through an arbitrage-free autoencoder that approximates efficient low-dimensional representations of the prices while maintaining no static arbitrage in the reconstructed surface. Given a multi-asset universe, we leverage the conditional invertibility property of normalizing flows and introduce a scalable method to calibrate the joint distribution of a set of independent simulators while preserving the dynamics of each simulator. Empirical results highlight the goodness of the calibrated simulators and their fidelity.