A hybrid wavelet-based physics-informed neural network for portfolio management
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| Format: | Preprint |
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2026
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| _version_ | 1866908906927685632 |
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| author | Yadav, Bahadur Mohanty, Mahaprasad Behera, Ratikanta Mohanty, Sanjay Kumar |
| author_facet | Yadav, Bahadur Mohanty, Mahaprasad Behera, Ratikanta Mohanty, Sanjay Kumar |
| contents | In this paper, we present a Hybrid Wavelet-based Physics-Informed Neural Networks (HW-PINNs) framework for portfolio management that provides a promising alternative to Physics-Informed Neural Networks (PINNs). Here, we first discuss the generalized framework of the Merton jump diffusion model and the associated HW-PINNs, followed by the one-dimensional case of a European option. Our work adapts the HW-PINN framework to the Merton jump-diffusion model for a European option, using a simplified direct coefficient optimization strategy, a mathematically corrected log-space formulation, and an efficient FFT -based computation of the integro-differential operator. Through numerical experiments across realistic market scenarios, we show that our proposed model achieves high accuracy and robustness, with a mean relative error of 0.27\% in low jump intensity scenarios compared to high-fidelity benchmarks. Our results validate that the implementation of this specific HW-PINN framework is a computationally efficient and reliable tool for pricing derivatives in markets with high jump risk. In addition, we further discuss risk analysis using Value at Risk (VaR) and Conditional Value at Risk (CVaR), which provide insights into downside risk across different market scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21834 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A hybrid wavelet-based physics-informed neural network for portfolio management Yadav, Bahadur Mohanty, Mahaprasad Behera, Ratikanta Mohanty, Sanjay Kumar Optimization and Control In this paper, we present a Hybrid Wavelet-based Physics-Informed Neural Networks (HW-PINNs) framework for portfolio management that provides a promising alternative to Physics-Informed Neural Networks (PINNs). Here, we first discuss the generalized framework of the Merton jump diffusion model and the associated HW-PINNs, followed by the one-dimensional case of a European option. Our work adapts the HW-PINN framework to the Merton jump-diffusion model for a European option, using a simplified direct coefficient optimization strategy, a mathematically corrected log-space formulation, and an efficient FFT -based computation of the integro-differential operator. Through numerical experiments across realistic market scenarios, we show that our proposed model achieves high accuracy and robustness, with a mean relative error of 0.27\% in low jump intensity scenarios compared to high-fidelity benchmarks. Our results validate that the implementation of this specific HW-PINN framework is a computationally efficient and reliable tool for pricing derivatives in markets with high jump risk. In addition, we further discuss risk analysis using Value at Risk (VaR) and Conditional Value at Risk (CVaR), which provide insights into downside risk across different market scenarios. |
| title | A hybrid wavelet-based physics-informed neural network for portfolio management |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2603.21834 |