3 papers
cs.LG2026
Neural Slack Variables for Shape Constraints
Ruben Wiedemann, Antoine Jacquier, Lukas Gonon
Enforcing functional inequality constraints such as monotonicity and convexity in neural networks is a fundamental challenge in many industrial and scientific applications. Classic…
q-fin.CP2025
Operator Deep Smoothing for Implied Volatility
Ruben Wiedemann, Antoine Jacquier, Lukas Gonon
We devise a novel method for nowcasting implied volatility based on neural operators. Better known as implied volatility smoothing in the financial industry, nowcasting of implied…
q-fin.CP2024
Deep learning interpretability for rough volatility
Bo Yuan, Damiano Brigo, Antoine Jacquier +1
Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their `black b…