# Deep calibration of rough stochastic volatility models

arXiv: https://arxiv.org/abs/1810.03399 · HTML: https://mlsanity.com/paper/1810.03399/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 12 of 12)

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- [Pricing options on flow forwards by neural networks in Hilbert space](https://mlsanity.com/paper/2202.11606/) (2022)
- [Deep Calibration With Artificial Neural Network: A Performance Comparison on Option Pricing Models](https://mlsanity.com/paper/2303.08760/) (2023)
- [Applying Deep Learning to Calibrate Stochastic Volatility Models](https://mlsanity.com/paper/2309.07843/) (2023)
- [Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework](https://mlsanity.com/paper/2510.24074/) (2025)
- [A Risk-Neutral Neural Operator for Arbitrage-Free SPX-VIX Term Structures](https://mlsanity.com/paper/2511.06451/) (2025)
- [Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples](https://mlsanity.com/paper/2605.08559/) (2026)
- [Synthetic American Option Pricing via Jump-HMM-Driven Heston Implied Volatility](https://mlsanity.com/paper/2605.13998/) (2026)
- [Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces](https://mlsanity.com/paper/2606.16961/) (2026)
- [Amortizing the Calibration Triple: A Projection-Consistent Neural Operator for Local-Stochastic Volatility](https://mlsanity.com/paper/2608.01217/) (2026)
- [Asymptotically-informed neural networks for Black-Scholes implied volatility computation](https://mlsanity.com/paper/2609.05491/) (2026)
- [Shapley-based Structural Analysis of Neural Calibration for Stochastic Volatility Models](https://mlsanity.com/paper/2610.03076/) (2026)

## Abstract (arXiv)

Sparked by Al`os, Le'on, and Vives (2007); Fukasawa (2011, 2017); Gatheral, Jaisson, and Rosenbaum (2018), so-called rough stochastic volatility models such as the rough Bergomi model by Bayer, Friz, and Gatheral (2016) constitute the latest evolution in option price modeling. Unlike standard bivariate diffusion models such as Heston (1993), these non-Markovian models with fractional volatility drivers allow to parsimoniously recover key stylized facts of market implied volatility surfaces such as the exploding power-law behaviour of the at-the-money volatility skew as time to maturity goes to zero. Standard model calibration routines rely on the repetitive evaluation of the map from model parameters to Black-Scholes implied volatility, rendering calibration of many (rough) stochastic volatility models prohibitively expensive since there the map can often only be approximated by costly Monte Carlo (MC) simulations (Bennedsen, Lunde, & Pakkanen, 2017; McCrickerd & Pakkanen, 2018; Bayer et al., 2016; Horvath, Jacquier, & Muguruza, 2017). As a remedy, we propose to combine a standard Levenberg-Marquardt calibration routine with neural network regression, replacing expensive MC simulations with cheap forward runs of a neural network trained to approximate the implied volatility map. Numerical experiments confirm the high accuracy and speed of our approach.