{"repo":"BianchiGiacomo/deepLearningVolatility","free":true,"listed":false,"github":"https://github.com/BianchiGiacomo/deepLearningVolatility","clone":"git clone https://github.com/BianchiGiacomo/deepLearningVolatility.git","description":"Neural network framework for volatility surface approximation and calibration. Supports rough Heston/Bergomi, random grids, multi-regime architectures.","language":"Python","stars":14,"topics":["deep-learning","financial-engineering","neural-networks","option-pricing","quantitative-finance","rough-volatility","volatility-framework","volatility-modeling","rough-bergomi","rough-heston"],"license":"MIT","category":"machine-learning","readme_excerpt":"Deep Learning Volatility Framework for volatility surface approximation with neural networks. Experience sub-basis-point accuracy with order-of-magnitude speedup over Monte Carlo methods. It includes dataset generators ( random grid ), neural pricers (grid, pointwise, multi‑regime), Monte Carlo engines for rough/classical models, and post‑processing tools. Status: research project in progress (APIs may change). --- Key features - Neural pricers - Grid‑based (dense surface on a T×K grid) - Pointwise (single queries ( $\\theta$, T, K)) with random grid and time buckets - Multi‑regime (short/mid/long) with automatic routing - Data generation : Monte Carlo with absorption handling for rough models (long‑term regime) - Supported processes (excerpt): Rough Bergomi, Rough Heston, Lifted Heston, GBM, jump‑diffusion processes (Kou/Merton) - Post‑processing : surface interpolation and smile repair modules - Examples : scripts for stability analysis, MC debugging, and long‑term absorption --- Results: Pointwise Network Performance Process : Rough Bergomi model ($H=0.25$, $\\eta=2.00$, $\\rho=-0.80$, $\\xi 0=0.15$) trained on 7,000 random grid surfaces. Neural network predictions (red dashed) vs Monte Carlo reference (blue solid) with 95% confidence intervals. Performance : MAE = 0.00078, CI Coverage = 89.5% --- Try It Now - Interactive Demo Experience the framework in action with our interactive demo: What you'll see: - Pre-trained neural networks generating volatility surfaces in milliseco","default_branch":null,"files":null,"tree":[],"storefront":"/r/BianchiGiacomo","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/BianchiGiacomo/deepLearningVolatility/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}