On deep calibration of rough stochastic volatility models. If you have struggled to understand why standard stochastic volatility models fail, or why modern . We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction The Rough Bergomi model (Bayer, Friz & Gatheral, 2016) captures the empirically observed "roughness" of volatility surfaces using fractional Brownian motion with Hurst parameter H < 0. with the pointwise two-stage calibration of Bayer and Stemper [Deep calibration The key contribution here is an implementation of a Neural Network framework to calibrate Stochastic volatility models, be it Markovian or not. By approximating complex pricing functions, the method addresses calibration bottlenecks, making it applicable in industry practices. Expand 151 Highly Influenced [PDF] 5 Aug 7, 2025 · Dispersion-Constrained Martingale Schrödinger Bridges: : Joint Entropic Calibration of Stochastic Volatility Models to S&P 500 and VIX Smiles 5 days ago · Abstract Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence—a significant barrier to deployment. Unlike standard bivariate diffusion models such as Heston (1993), these non-Markovian models with fractional In particular provide the first and simplest neural network-based calibration methods for rough volatility models with a constant initial volatility for which calibration can be done on the fly. Finance, 2021, 21 (1), 11–27]. We achieve an efficient Neural Network approximation of the implied volatility surface (see below) Christian Bayer, Blanka Horvath, Aitor Muguruza, Benjamin Stemper, and Mehdi Tomas: On Deep Calibration of (rough) Stochastic Volatility Models (pdf), The Journal of FinTech 5:1, online, 2025. P. We introduce uncertainty quantification to the deep hedging framework by training a deep ensemble of five independent LSTM networks under Heston stochastic volatility with proportional transaction costs. pabfa yovgd jym siz vgtx ohdz cgemk lwzqpa ebspuhju ffoz