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

arXiv:2609.31570 · stat.ML, cs.LG, stat.AP, stat.CO · Submitted 2026-09-25 · Read on arXiv

stat.ML, cs.LG, stat.AP, stat.CO

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/SimonBreneis/approximations_to_fractional_

Terminology

• Calibration — 68× in this paper · explained in 7 episodes
• Explainability — 44× in this paper · explained in 3 episodes
• Monte Carlo — 25× in this paper
• Empirical Coverage — 13× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Loss Function — 10× in this paper · explained in 2 episodes
• Uncertainty Quantification — 9× in this paper · explained in 8 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Inverse Problem — 7× in this paper · explained in 2 episodes
• Information Gain — 6× in this paper · explained in 2 episodes
• Simulation-based Inference — 5× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Antithetic sampling — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Markovian Approximation — 1× in this paper · explained in 2 episodes
• Neural Surrogates — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Shapley values — 1× in this paper · explained in 2 episodes
• Simulation-Based Inference (SBI) — 1× in this paper · explained in 3 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes
• Uncertainty Decomposition — 1× in this paper · explained in 2 episodes

Sources

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Simulation-based inference via telescoping ratio estimation for trawl processes
• Fast inverse transform sampling in one and two dimensions
• The MAPS Algorithm: Fast model-agnostic and distribution-free prediction intervals for supervised learning
• Deep learning interpretability for rough volatility

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey