8 papers
GROMM: Generative Risk-neutral Option Market Models
Magnus Wiese, Hans Buehler, Blanka Horvath
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that it…
DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces
Hans Buehler, Blanka Horvath, Anastasis Kratsios +1
This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage. Our model is…
PIVOT: Bridging Black-Scholes Implied-Volatility and Price Objectives via Differentiable Jäckel Operator
Raeid Saqur, Yannick Limmer, Anastasis Kratsios +2
Modern option-learning systems operate in two coordinates: price space, where markets quote and no-arbitrage constraints are most naturally enforced, and implied volatility (IV) sp…
SANOS Smooth strictly Arbitrage-free Non-parametric Option Surfaces
Hans Buehler, Blanka Horvath, Anastasis Kratsios +2
We present a simple, numerically efficient but highly flexible non-parametric method to construct representations of option price surfaces which are both smooth and strictly arbitr…
Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
Hans Buehler, Blanka Horvath, Yannick Limmer +1
This paper addresses the challenge of model uncertainty in quantitative finance, where decisions in portfolio allocation, derivative pricing, and risk management rely on estimating…
Filtered not Mixed: Stochastic Filtering-Based Online Gating for Mixture of Large Language Models
Raeid Saqur, Anastasis Kratsios, Florian Krach +5
We propose MoE-F - a formalized mechanism for combining pre-trained Large Language Models (LLMs) for online time-series prediction by adaptively forecasting the best weighting…
Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
Yannick Limmer, Anastasis Kratsios, Xuwei Yang +2
An inherent challenge in computing fully-explicit generalization bounds for transformers involves obtaining covering number estimates for the given transformer class . Crude est…
Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
Blanka Horvath, Anastasis Kratsios, Yannick Limmer +1
The use of attention-based deep learning models in stochastic filtering, e.g. transformers and deep Kalman filters, has recently come into focus; however, the potential for these m…