| 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 | 123 | 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 | 92 | 2019 |
| Deep learning volatility B Horvath, A Muguruza, M Tomas arXiv preprint arXiv:1901.09647, 2019 | 81 | 2019 |
| How to build a cross-impact model from first principles: Theoretical requirements and empirical results M Tomas, I Mastromatteo, M Benzaquen Quantitative Finance 22 (6), 1017-1036, 2022 | 21 | 2022 |
| From microscopic price dynamics to multidimensional rough volatility models M Rosenbaum, M Tomas Advances in Applied Probability 53 (2), 425-462, 2021 | 18 | 2021 |
| A characterisation of cross-impact kernels M Rosenbaum, M Tomas arXiv preprint arXiv:2107.08684, 2021 | 8 | 2021 |
| Cross impact in derivative markets M Tomas, I Mastromatteo, M Benzaquen arXiv preprint arXiv:2102.02834, 2021 | 6 | 2021 |
| On deep calibration of (rough) stochastic volatility models. arXiv C Bayer, B Horvath, A Muguruza, B Stemper, M Tomas arXiv preprint arXiv:1908.08806, 2019 | 6 | 2019 |
| Pricing and calibration of stochastic models via neural networks M Tomas Master’s thesis, Department of Mathematics, Imperial College London, 2018 | 1 | 2018 |
| The Multivariate price formation process and cross-impact M Tomas Institut Polytechnique de Paris, 2022 | | 2022 |
Blanka HorvathUniversity of OxfordVerified email at maths.ox.ac.uk
