Hoshisashi, K;
Phelan, CE;
Barucca, P;
(2024)
Whack-a-mole Learning: Physics-Informed Deep Calibration for Implied Volatility Surface.
In:
Proceedings of the IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) 2024.
(pp. pp. 1-8).
IEEE
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Abstract
Calibrating the Implied Volatility Surface (IVS) using sparse market data is an essential task for option pricing in quantitative finance. The calibrated values must provide a solution to a specified partial differential equation (PDE) in addition to obeying no-arbitrage conditions modelled by individual differential inequalities. However, this leads to a multi-objective optimization problem, which emerges in Physics-Informed Neural Networks (PINNs) as well as in our generalized framework. In order to address this problem, we propose a novel calibration algorithm called Whack-a-mole Learning (WamL), which integrates self-adaptive and auto-balancing processes for each loss term. The developed algorithm realizes efficient reweighting mechanisms for each objective function, ensuring alignment with constraints of price derivatives to achieve smooth surface fitting while satisfying PDE and no-arbitrage conditions. In our tests, this approach enables the straightforward implementation of a deep calibration method that incorporates no-arbitrage constraints, providing an appropriate fit for uneven and sparse market data. WamL also enhances the representation of risk profiles for option prices, offering a robust and efficient solution for IVS calibration.
| Type: | Proceedings paper |
|---|---|
| Title: | Whack-a-mole Learning: Physics-Informed Deep Calibration for Implied Volatility Surface |
| Event: | 2024 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr) |
| Location: | Hoboken, NJ, USA |
| Dates: | 22nd-23rd October 2024 |
| ISBN-13: | 979-8-3503-5483-6 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CIFER62890.2024.10772909 |
| Publisher version: | https://doi.org/10.1109/cifer62890.2024.10772909 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Multi-objective Learning, Physics-Informed Neural Networks, Option pricing, Implied Volatility Surface, Partial Differential Equations |
| 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/10205506 |
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