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Found 7 papers, 3 papers with code
Calibration of the Bass Local Volatility model
The Bass local volatility model introduced by Backhoff-Veraguas--Beiglb\"ock--Huesmann--K\"allblad is a Markov model perfectly calibrated to vanilla options at finitely many maturities, that approximates the Dupire local volatility model.
Quantitative Fundamental Theorem of Asset Pricing
In this paper we provide a quantitative analysis to the concept of arbitrage, that allows to deal with model uncertainty without imposing the no-arbitrage condition.
Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer
Several problems in stochastic analysis are defined through their geometry, and preserving that geometric structure is essential to generating meaningful predictions.
SPATE-GAN: Improved Generative Modeling of Dynamic Spatio-Temporal Patterns with an Autoregressive Embedding Loss
In this study, we propose a novel loss objective combined with COT-GAN based on an autoregressive embedding to reinforce the learning of spatio-temporal dynamics.
Conditional COT-GAN for Video Prediction with Kernel Smoothing
The resulting kernel conditional COT-GAN algorithm is illustrated with an application for video prediction.
COT-GAN: Generating Sequential Data via Causal Optimal Transport
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data.
Model-independent pricing with insider information: a Skorokhod embedding approach
In this paper, we consider the pricing and hedging of a financial derivative for an insider trader, in a model-independent setting.
