Calibrating rough volatility models: a convolutional neural network approach

DOI10.1080/14697688.2019.1654126zbMath1466.91318arXiv1812.05315OpenAlexW2971337067WikidataQ127311045 ScholiaQ127311045MaRDI QIDQ4991028

Henry Stone

Publication date: 2 June 2021

Published in: Quantitative Finance (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.05315

zbMATH Keywords

estimationcalibrationconvolutional neural networksrough volatility

Mathematics Subject Classification ID

Fractional processes, including fractional Brownian motion (60G22) Learning and adaptive systems in artificial intelligence (68T05) Financial markets (91G15)

Related Items

Calibration to FX triangles of the 4/2 model under the benchmark approach, Optimal trade execution for Gaussian signals with power-law resilience, Deep calibration of financial models: turning theory into practice, Unbiased Deep Solvers for Linear Parametric PDEs, A martingale method for option pricing under a CEV-based fast-varying fractional stochastic volatility model, Deep Curve-Dependent PDEs for Affine Rough Volatility, Asymptotics for volatility derivatives in multi-factor rough volatility models, Predicting critical transitions in multiscale dynamical systems using reservoir computing, Fast Hybrid Schemes for Fractional Riccati Equations (Rough Is Not So Tough)

Uses Software

• Adam
• SqueezeDet

Cites Work

• Unnamed Item
• Unnamed Item
• Efficiently implementing the maximum likelihood estimator for Hurst exponent
• Estimating the Hurst parameter from short term volatility swaps: a Malliavin calculus approach
• Volatility is rough
• Pathwise large deviations for the rough Bergomi model
• Pricing under rough volatility
• Deep hedging
• Stochastic Calculus for Fractional Brownian Motion and Applications
• Fractional Brownian Motions, Fractional Noises and Applications