mlsanityData to 13 Oct 2026

Climate-Dyna Deep Hedging for XVAs: Model-Based Reinforcement Learning, Residual Climate HVA, and Hedge-Instrument Discovery

arXiv 2608.01208 ↗First posted 2 Aug 2026q-fin.MF cs.LG q-fin.RMcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

For a trading desk, residual climate hedging valuation adjustment (HVA) is the climate cost left after its inherited hedge and any admissible overlay have been taken into account; it therefore cannot be inferred from a stand-alone stress loss. We obtain this residual by comparing paired climate-on and baseline worlds and reoptimizing the overlay for each hedge universe, which also turns hedge-instrument discovery into a valuation problem: an instrument is useful to the extent that it lowers the optimized residual cost. The linear-Gaussian case has an exact finite-horizon Riccati solution; Climate-Dyna starts from that hedge and learns the remaining nonlinear correction from paired world-model rollouts, with an independent gate deciding whether to deploy the update. In a public-data-calibrated semi-synthetic EU ETS study, crediting the inherited hedge lowers the mean climate charge from 1.517 to 0.906, and the learned overlay lowers it to 0.831 against a 0.821 exact floor; residual Dyna cuts regret by 93% relative to replay with one quarter as many trajectories, while adaptation from only 25 target transitions retains 60.7% of the exact-assisted gain.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 29 references that are in the map. First posted on arXiv 2 Aug 2026.

Builds on

6 of 29 references.

References · 6

  1. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 756
  2. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 754
  3. 2018Deep Hedgingcited by 106
  4. 2017QLBS: Q-Learner in the Black-Scholes(-Merton) Worldscited by 23
  5. 2023Adversarial Deep Hedging: Learning to Hedge without Price Process Modelingcited by 5
  6. 2024Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernelscited by 4

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