Autoencoders are a self-supervised learning system where, during training, the output is an
approximation of the input. Typically, autoencoders have three parts: Encoder (which …

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 …

We propose a hybrid method for generating arbitrage-free implied volatility (IV) surfaces
consistent with historical data by combining model-free variational autoencoders (VAEs) …

We introduce a new approach for generating sequences of implied volatility (IV) surfaces
across multiple assets that is faithful to historical prices. We do so using a combination of …

Measuring risk is at the center of modern financial risk management. As the world economy
is becoming more complex and standard modelling assumptions are violated, the advanced …

Abstract Deep Generative Models (DGMs) are versatile tools for learning data
representations while adequately incorporating domain knowledge such as the specification …

Financial options are traded by various market participants, including market makers,
hedgers, and speculators. An implied volatility surface is used to price options contracts …

Financial markets are among the most complex entities in our environment, yet mainstream
quantitative models operate at predetermined scale, rely on linear correlation measures …

In this study, we propose a two-step framework to predict the implied volatility surface (IVS)
in a manner that excludes static arbitrage. First, we select features to represent the surface …

We propose a highly optimized latent factor representation of the yield curve obtained by
training a variational autoencoder (VAE) to curve data from multiple currencies. A curious …