Dimension Reduction in Martingale Optimal Transport: Geometry and Robust Option Pricing
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866908782556086272 |
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| author | Hiew, Joshua Zoen-Git Lim, Tongseok Pass, Brendan de Souza, Marcelo Cruz |
| author_facet | Hiew, Joshua Zoen-Git Lim, Tongseok Pass, Brendan de Souza, Marcelo Cruz |
| contents | This paper addresses the problem of robust option pricing within the framework of Vectorial Martingale Optimal Transport (VMOT). We investigate the geometry of VMOT solutions for $N$-period market models and demonstrate that, when the number of underlying assets is $d=2$ and the payoff is sub- or supermodular, the extremal model reduces to a single-factor structure in the first period. This structural result allows for a significant dimension reduction, transforming the problem into a more tractable format. We prove that this reduction is specific to the two-asset case and provide counterexamples showing it generally fails for $d \geq 3$. Finally, we exploit this monotonicity to develop a reduced-dimension Sinkhorn algorithm. Numerical experiments demonstrate that this structure-preserving approach reduces computational time by approximately 99\% compared to standard methods while improving accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_04947 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dimension Reduction in Martingale Optimal Transport: Geometry and Robust Option Pricing Hiew, Joshua Zoen-Git Lim, Tongseok Pass, Brendan de Souza, Marcelo Cruz Mathematical Finance Optimization and Control Probability This paper addresses the problem of robust option pricing within the framework of Vectorial Martingale Optimal Transport (VMOT). We investigate the geometry of VMOT solutions for $N$-period market models and demonstrate that, when the number of underlying assets is $d=2$ and the payoff is sub- or supermodular, the extremal model reduces to a single-factor structure in the first period. This structural result allows for a significant dimension reduction, transforming the problem into a more tractable format. We prove that this reduction is specific to the two-asset case and provide counterexamples showing it generally fails for $d \geq 3$. Finally, we exploit this monotonicity to develop a reduced-dimension Sinkhorn algorithm. Numerical experiments demonstrate that this structure-preserving approach reduces computational time by approximately 99\% compared to standard methods while improving accuracy. |
| title | Dimension Reduction in Martingale Optimal Transport: Geometry and Robust Option Pricing |
| topic | Mathematical Finance Optimization and Control Probability |
| url | https://arxiv.org/abs/2309.04947 |