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Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆

Parisa Davar et al.

The Journal of Finance and Data Science2025https://doi.org/10.1016/j.jfds.2025.100165article
ABDC B
Weight
0.37

What the paper says

This paper tackles the problem of mitigating catastrophic risk (which is risk with very low frequency but very high severity) in the context of a sequential decision making process. This problem is particularly challenging due to the scarcity of observations in the far tail of the distribution of cumulative costs (negative rewards). A policy gradient algorithm is developed, that we call POTPG. It is based on approximations of the tail risk derived from extreme value theory. Numerical experiments highlight the out-performance of our method over common benchmarks, relying on the empirical distribution. An application to financial risk management, more precisely to the dynamic hedging of a financial option, is presented.

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https://doi.org/https://doi.org/10.1016/j.jfds.2025.100165

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@article{parisa2025,
  title        = {{Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆}},
  author       = {Parisa Davar et al.},
  journal      = {The Journal of Finance and Data Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jfds.2025.100165},
}

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Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients☆

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.