Quantum-Inspired Tensor Neural Networks for Option Pricing
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arXiv
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| Format: | Preprint |
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2022
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| author | Patel, Raj G. Hsing, Chia-Wei Sahin, Serkan Palmer, Samuel Jahromi, Saeed S. Sharma, Shivam Dominguez, Tomas Tziritas, Kris Michel, Christophe Porte, Vincent Abid, Mustafa Aubert, Stephane Castellani, Pierre Mugel, Samuel Orus, Roman |
| author_facet | Patel, Raj G. Hsing, Chia-Wei Sahin, Serkan Palmer, Samuel Jahromi, Saeed S. Sharma, Shivam Dominguez, Tomas Tziritas, Kris Michel, Christophe Porte, Vincent Abid, Mustafa Aubert, Stephane Castellani, Pierre Mugel, Samuel Orus, Roman |
| contents | Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_14076 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Quantum-Inspired Tensor Neural Networks for Option Pricing Patel, Raj G. Hsing, Chia-Wei Sahin, Serkan Palmer, Samuel Jahromi, Saeed S. Sharma, Shivam Dominguez, Tomas Tziritas, Kris Michel, Christophe Porte, Vincent Abid, Mustafa Aubert, Stephane Castellani, Pierre Mugel, Samuel Orus, Roman Pricing of Securities Computational Engineering, Finance, and Science Machine Learning Quantum Physics Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory. |
| title | Quantum-Inspired Tensor Neural Networks for Option Pricing |
| topic | Pricing of Securities Computational Engineering, Finance, and Science Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2212.14076 |