Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917179276918784 |
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| author | Agram, Nacira Arharas, Ihsan Pucci, Giulia Rems, Jan |
| author_facet | Agram, Nacira Arharas, Ihsan Pucci, Giulia Rems, Jan |
| contents | We formulate a Contract for Difference (CfD) with early exit options as a two-player zero-sum Dynkin game, reflecting the strategic interaction between an electricity producer and a regulatory entity. The game incorporates penalties for early termination and mean-reverting price dynamics, with the value characterized through a doubly reflected backward stochastic differential equation (DRBSDE).
To compute the contract value and optimal stopping strategies, we develop a neural solver that approximates the DRBSDE solution using a sequence of neural networks trained on simulated trajectories. The method avoids discretizing the state space, supports time-dependent barriers, and scales to high-dimensional settings. We establish a convergence result and test the method on two scenarios: a benchmark symmetric game in 20 dimensions, and a CfD model with 24-dimensional electricity prices representing multiple European zones.
The results demonstrate that the proposed solver accurately captures the contract's value and optimal stopping regions, with consistent performance across dimensional settings. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_00880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs Agram, Nacira Arharas, Ihsan Pucci, Giulia Rems, Jan Optimization and Control Probability We formulate a Contract for Difference (CfD) with early exit options as a two-player zero-sum Dynkin game, reflecting the strategic interaction between an electricity producer and a regulatory entity. The game incorporates penalties for early termination and mean-reverting price dynamics, with the value characterized through a doubly reflected backward stochastic differential equation (DRBSDE). To compute the contract value and optimal stopping strategies, we develop a neural solver that approximates the DRBSDE solution using a sequence of neural networks trained on simulated trajectories. The method avoids discretizing the state space, supports time-dependent barriers, and scales to high-dimensional settings. We establish a convergence result and test the method on two scenarios: a benchmark symmetric game in 20 dimensions, and a CfD model with 24-dimensional electricity prices representing multiple European zones. The results demonstrate that the proposed solver accurately captures the contract's value and optimal stopping regions, with consistent performance across dimensional settings. |
| title | Deep Learning for Energy Market Contracts: Dynkin Game with Doubly RBSDEs |
| topic | Optimization and Control Probability |
| url | https://arxiv.org/abs/2503.00880 |