In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

Developing a generative model of realistic order flow in financial markets is a challenging
open problem, with numerous applications for market participants. Addressing this, we …

This paper provides a comprehensive overview of the evolution and latest advancements in
Generative AI models, alongside their economic impact and applications in asset …

Mean–variance optimization, introduced by Markowitz, is a foundational theory and
methodology in finance and optimization, significantly influencing investment management …

In the financial services industry, forecasting the risk factor distribution conditional on the
history and the current market environment is the key to market risk modeling in general and …

We build a time-causal variational autoencoder (TC-VAE) for robust generation of financial
time series data. Our approach imposes a causality constraint on the encoder and decoder …

Tail risk measures are fully determined by the distribution of the underlying loss beyond its
quantile at a certain level, with Value-at-Risk and Expected Shortfall being prime examples …

The use of machine learning for statistical modeling (and thus, generative modeling) has
grown in popularity with the proliferation of time series models, text-to-image models, and …

In this paper, we present CoMeTS-GAN (Correlated Multivariate Time Series GAN), a
framework based on Conditional Generative Adversarial Networks (C-GANs), designed to …