| Fluctuation identities with continuous monitoring and their application to the pricing of barrier options CE Phelan, D Marazzina, G Fusai, G Germano European Journal of Operational Research 271 (1), 210-223, 2018 | 40 | 2018 |
| Hilbert transform, spectral filters and option pricing CE Phelan, D Marazzina, G Fusai, G Germano Annals of Operations Research 282 (1), 273-298, 2019 | 34 | 2019 |
| Market structure dynamics during COVID-19 outbreak PF Procacci, CE Phelan, T Aste arXiv preprint arXiv:2003.10922, 2020 | 10 | 2020 |
| Pricing methods for α-quantile and perpetual early exercise options based on Spitzer identities CE Phelan, D Marazzina, G Germano Quantitative Finance 20 (6), 899-918, 2020 | 8 | 2020 |
| No-Arbitrage Deep Calibration for Volatility Smile and Skewness K Hoshisashi, CE Phelan, P Barucca arXiv preprint arXiv:2310.16703, 2023 | 2 | 2023 |
| Solution of Wiener-Hopf and Fredholm integral equations by fast Hilbert and Fourier transforms G Germano, CE Phelan, D Marazzina, G Fusai arXiv preprint arXiv:2106.05326, 2021 | 2 | 2021 |
| Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface K Hoshisashi, CE Phelan, P Barucca arXiv preprint arXiv:2411.02375, 2024 | | 2024 |
| Whack-a-Mole Learning: Physics-Informed Deep Calibration for Implied Volatility Surface K Hoshisashi, CE Phelan, P Barucca | | 2024 |
| The Structure and Impact of Fees on Investor and Manager Returns M Galas, D Brown, J Bryant, L Li, C Phelan, A Rutkowska, P Treleaven Available at SSRN 4785475, 2024 | | 2024 |
| Fourier transform methods for the pricing of barrier options and other exotic derivatives CE Phelan UCL (University College London), 2018 | | 2018 |
| ORCID: 0000-0001-9215-2586 and Germano, G.(2018). Hilbert transform, spectral filters and option pricing CE Phelan, D Marazzina, G Fusai Annals of Operations Research, doi 10, 0 | | |
| Improvement of numerical option pricing methods based on the Hilbert transform using spectral filtering CE Phelan, G Germano | | |
| Physics-Informed Neural Networks for Derivative-Constrained PDEs K Hoshisashi, CE Phelan, P Barucca ICML 2024 AI for Science Workshop, 0 | | |
