Malliavin-Informed Policy Gradients for Continuous-Time Portfolio Optimization Neural Variance Reduction under Black-Scholes, Heston, and Rough Bergomi Dynamics
The paper develops a Malliavin-informed policy-gradient estimator using stochastic control variates to reduce variance in continuous-time reinforcement learning for dynamic portfolio selection.
Featured in No. 135 on 16 Oct 2026 · 2 days after release
- Released
- 14 Oct 2026
- First featured
- No. 135 · 16 Oct 2026
- Published in
- Not yet, as far as Semantic Scholar knows
- Fanfare
- 2 of 5
- Identifier
- SSRN 7598820
- Authors
- Abdou Kaba
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