Deep numerical schemes for systems of Ergodic BSDEs with applications to regime-switching forward utilities
arXiv:2606.24271v1 Announce Type: cross Abstract: In this paper, we introduce two neural-network-based numerical schemes for solving systems of coupled ergodic Backward Stochastic Differential Equations (eBSDEs), motivated by the approximation of optimal strategies within the framework of forward utilities in a regime-switching stochastic factor model. Our approach builds on the representation of such models through systems of eBSDEs introduced in [HLT20]. We first establish a link between the solution of the system of ergodic BSDEs and that of an associated multidimensional BSDE with random t
The increasing sophistication of neural networks and the demand for more robust financial models are driving the application of AI to complex stochastic equations.
This development allows for more accurate and efficient solutions to complex financial and economic models, impacting fields from quantitative finance to risk management.
The ability to solve systems of ergodic BSDEs with neural networks streamlines the approximation of optimal strategies in complex stochastic models, particularly those with regime-switching dynamics.
- · Quantitative finance professionals
- · AI researchers in finance
- · High-frequency trading firms
- · Risk management departments
- · Traditional numerical methods for BSDEs
- · Firms reliant on simpler financial models
More sophisticated and computationally efficient financial models become accessible for real-world applications.
Improved accuracy in financial estimations could lead to more stable markets and better investment strategies.
The methodology could be extended to other complex systems requiring solutions to high-dimensional stochastic differential equations, potentially impacting areas beyond finance.
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Read at arXiv cs.LG