31 citations · 152 across the 38 of their papers we have counts for
6 papers · 1 filter
Fast Deep Hedging with Second-Order Optimization
Konrad Mueller, Amira Akkari, Lukas Gonon +1
Hedging exotic options in presence of market frictions is an important risk management task. Deep hedging can solve such hedging problems by training neural network policies in rea…
Computing Systemic Risk Measures with Graph Neural Networks
Lukas Gonon, Thilo Meyer-Brandis, Niklas Weber
This paper investigates systemic risk measures for stochastic financial networks of explicitly modelled bilateral liabilities. We extend the notion of systemic risk measures from B…
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
Lukas Gonon, Arnulf Jentzen, Benno Kuckuck +3
The approximation of solutions of partial differential equations (PDEs) with numerical algorithms is a central topic in applied mathematics. For many decades, various types of meth…
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty Detection
Nikita Zozoulenko, Thomas Cass, Lukas Gonon
The Mahalanobis distance is a classical tool used to measure the covariance-adjusted distance between points in . In this work, we extend the concept of Mahalanobis distanc…
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…
Universal randomised signatures for generative time series modelling
Francesca Biagini, Lukas Gonon, Niklas Walter
Randomised signature has been proposed as a flexible and easily implementable alternative to the well-established path signature. In this article, we employ randomised signature to…