Hoshisashi, Kentaro;
Phelan, Carolyn E;
Barucca, Paolo;
(2024)
Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface.
In:
Proceedings of the 5th ACM International Conference on AI in Finance.
(pp. pp. 847-855).
Association Computing Machinery (ACM): Brooklyn, NY, USA.
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Abstract
Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications. This task requires not only fitting market data but also satisfying a specified partial differential equation (PDE) and no-arbitrage conditions modelled by differential inequalities. This paper proposes a novel PhysicsInformed Neural Networks (PINNs) approach called Whack-a-mole Online Learning (WamOL) to address this multi-objective optimisation problem. WamOL integrates self-adaptive and auto-balancing processes for each loss term, efficiently reweighting objective functions to ensure smooth surface fitting while adhering to PDE and no-arbitrage constraints and updating for intraday predictions. In our experiments, WamOL demonstrates superior performance in calibrating intraday IVS from uneven and sparse market data, effectively capturing the dynamic evolution of option prices and associated risk profiles. This approach offers an efficient solution for intraday IVS calibration, extending PINNs applications and providing a method for real-time financial modelling.
| Type: | Proceedings paper |
|---|---|
| Title: | Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface |
| Event: | ICAIF '24: 5th ACM International Conference on AI in Finance |
| ISBN-13: | 979-8-4007-1081-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3677052.3698601 |
| Publisher version: | https://doi.org/10.1145/3677052.3698601 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution International 4.0 License. |
| Keywords: | Physics-Informed Neural Networks, Implied Volatility Surface, Derivative Constrained PDE, No-arbitrage constraints, Self-adaptive loss balancing, Whack-a-mole Online Learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10201408 |
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