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D. Brigo

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#machine learning Preprint Sep 2026

Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach

A simulation-based inference framework for rough Heston (rHeston) calibration that learns the posterior distribution of the model parameters conditional on an IV surface and introduces Hellinger-SHAP, an information-theoretic explainability method for posterior inference.

D. Brigo, Raphael Huser, Dan Leonte · 0 citations

Preprint Jul 2026

On the boundaries, asymptotic law and Bernoulli-Doob representation of homogeneous bounded martingales

We study homogeneous diffusion martingales evolving in a bounded state space $D=[a,b]$, where $a$ and $b$ are zeros of the diffusion coefficient. We call a process of the form $Z_t=\mathbb{E}[B\mid\mathcal{F}_t]$, with $B$ a Bernoulli random variable, a Bernoulli-Doob martingale. Our main results establish a complete e...

D. Brigo, Frédéric Vrins · 1 citation