| Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models B Horvath, A Muguruza, M Tomas Quantitative Finance 21 (1), 11-27, 2021 | 311 | 2021 |
| On deep calibration of (rough) stochastic volatility models C Bayer, B Horvath, A Muguruza, B Stemper, M Tomas arXiv preprint arXiv:1908.08806, 2019 | 127 | 2019 |
| A data-driven market simulator for small data environments H Buehler, B Horvath, T Lyons, IP Arribas, B Wood arXiv preprint arXiv:2006.14498, 2020 | 113 | 2020 |
| Short-time near-the-money skew in rough fractional volatility models C Bayer, PK Friz, A Gulisashvili, B Horvath, B Stemper Quantitative Finance 19 (5), 779-798, 2019 | 91 | 2019 |
| Volatility options in rough volatility models B Horvath, A Jacquier, P Tankov SIAM Journal on Financial Mathematics 11 (2), 437-469, 2020 | 76 | 2020 |
| Functional central limit theorems for rough volatility B Horvath, A Jacquier, A Muguruza, A Søjmark Finance and Stochastics 28 (3), 615-661, 2024 | 68 | 2024 |
| Higher order kernel mean embeddings to capture filtrations of stochastic processes C Salvi, M Lemercier, C Liu, B Horvath, T Damoulas, T Lyons Advances in Neural Information Processing Systems 34, 16635-16647, 2021 | 61 | 2021 |
| Non-adversarial training of neural sdes with signature kernel scores Z Issa, B Horvath, M Lemercier, C Salvi Advances in Neural Information Processing Systems 36, 11102-11126, 2023 | 53 | 2023 |
| Deep hedging under rough volatility B Horvath, J Teichmann, Ž Žurič Risks 9 (7), 138, 2021 | 48 | 2021 |
| Generating financial markets with signatures H Buehler, B Horvath, T Lyons, I Perez Arribas, B Wood Available at SSRN 3657366, 2020 | 41 | 2020 |
| Optimal stopping via distribution regression: a higher rank signature approach B Horvath, M Lemercier, C Liu, T Lyons, C Salvi arXiv preprint arXiv:2304.01479, 2023 | 31 | 2023 |
| Asymptotic behaviour of randomised fractional volatility models B Horvath, A Jacquier, C Lacombe Journal of Applied Probability 56 (2), 496-523, 2019 | 26 | 2019 |
| Clustering market regimes using the Wasserstein distance B Horvath, Z Issa, A Muguruza arXiv preprint arXiv:2110.11848, 2021 | 23 | 2021 |
| Mass at zero in the uncorrelated SABR model and implied volatility asymptotics A Gulisashvili, B Horvath, A Jacquier Quantitative Finance 18 (10), 1753-1765, 2018 | 18 | 2018 |
| Signature trading: A path-dependent extension of the mean-variance framework with exogenous signals O Futter, B Horvath, M Wiese arXiv preprint arXiv:2308.15135, 2023 | 15 | 2023 |
| Data anonymisation, outlier detection and fighting overfitting with restricted Boltzmann machines O Kondratyev, C Schwarz, B Horvath Outlier Detection and Fighting Overfitting with Restricted Boltzmann …, 2020 | 14 | 2020 |
| Hedging under rough volatility M Fukasawa, B Horvath, P Tankov arXiv preprint arXiv:2105.04073, 2021 | 13 | 2021 |
| Robust Hedging GANs B Horvath, Y Limmer Available at SSRN 4489029, 2023 | 12* | 2023 |
| Analytic option prices for the Black-Karasinski short rate model B Horvath, AJ Jacquier, C Turfus Available at SSRN 3253833, 2018 | 12 | 2018 |
| Dirichlet forms and finite element methods for the SABR model B Horvath, O Reichmann SIAM Journal on Financial Mathematics 9 (2), 716-754, 2018 | 12 | 2018 |
Antoine (Jack) JacquierImperial College LondonVerified email at imperial.ac.uk
