Multi-Asset Spot and Option Market Simulation
This work addresses the challenge of scalable and realistic market simulation for financial applications, though it appears incremental by building on existing normalizing flow techniques.
The authors tackled the problem of simulating realistic multi-asset spot and option markets by developing a method based on normalizing flows and an arbitrage-free autoencoder to handle high-dimensional call prices, resulting in calibrated simulators that preserve dynamics and show empirical fidelity.
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.