Robust Reinforcement Learning with Dynamic Distortion Risk Measures
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- Paul Milgrom & Ilya Segal, 2002. "Envelope Theorems for Arbitrary Choice Sets," Econometrica, Econometric Society, vol. 70(2), pages 583-601, March.
- Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
- Saeed Marzban & Erick Delage & Jonathan Yu-Meng Li, 2023. "Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures," Quantitative Finance, Taylor & Francis Journals, vol. 23(10), pages 1411-1430, October.
- Silvana M. Pesenti & Sebastian Jaimungal & Yuri F. Saporito & Rodrigo S. Targino, 2023. "Risk Budgeting Allocation for Dynamic Risk Measures," Papers 2305.11319, arXiv.org, revised Oct 2024.
- Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
- Carole Bernard & Silvana M. Pesenti & Steven Vanduffel, 2024.
"Robust distortion risk measures,"
Mathematical Finance, Wiley Blackwell, vol. 34(3), pages 774-818, July.
- Carole Bernard & Silvana M. Pesenti & Steven Vanduffel, 2022. "Robust Distortion Risk Measures," Papers 2205.08850, arXiv.org, revised Mar 2023.
- Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
- David Wu & Sebastian Jaimungal, 2023. "Robust Risk-Aware Option Hedging," Papers 2303.15216, arXiv.org, revised Dec 2023.
- David Wu & Sebastian Jaimungal, 2023. "Robust Risk-Aware Option Hedging," Applied Mathematical Finance, Taylor & Francis Journals, vol. 30(3), pages 153-174, May.
- Jose Blanchet & Karthyek Murthy, 2019. "Quantifying Distributional Model Risk via Optimal Transport," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 565-600, May.
- Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
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This paper has been announced in the following NEP Reports:- NEP-BIG-2024-10-21 (Big Data)
- NEP-CMP-2024-10-21 (Computational Economics)
- NEP-RMG-2024-10-21 (Risk Management)
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