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Closed-form solutions for worst-case law invariant risk measures with application to robust portfolio optimization

Li, Jonathan Yu-Meng - In: Operations research 66 (2018) 6, pp. 1533-1541

Persistent link: https://www.econbiz.de/10011971655

Inverse optimization of convex risk functions

Li, Jonathan Yu-Meng - In: Management science : journal of the Institute for … 67 (2021) 11, pp. 7113-7141

Persistent link: https://www.econbiz.de/10012703797

Minimizing risk exposure when the choice of a risk measure is ambiguous

Delage, Erick; Li, Jonathan Yu-Meng - In: Management science : journal of the Institute for … 64 (2018) 1, pp. 327-344

Persistent link: https://www.econbiz.de/10011819577

Deep reinforcement learning for option pricing and hedging under dynamic expectile risk measures

Marzban, Saeed; Delage, Erick; Li, Jonathan Yu-Meng - In: Quantitative finance 23 (2023) 10, pp. 1411-1430

Persistent link: https://www.econbiz.de/10014419168

Equal risk pricing and hedging of financial derivatives with convex risk measures

Marzban, Saeed; Delage, Erick; Li, Jonathan Yu-Meng - In: Quantitative finance 22 (2022) 1, pp. 47-73

Persistent link: https://www.econbiz.de/10012872521

WaveCorr : deep reinforcement learning with permutation invariant convolutional policy networks for portfolio management

Marzban, Saeed; Delage, Erick; Li, Jonathan Yu-Meng; … - In: Operations research letters 51 (2023) 6, pp. 680-686

Persistent link: https://www.econbiz.de/10014465889

A General Wasserstein Framework for Data-driven Distributionally Robust Optimization : Tractability and Applications

Li, Jonathan Yu-Meng; Mao, Tiantian - 2022

Data-driven distributionally robust optimization is a recently emerging paradigm aimed at finding a solution that is driven by sample data but is protected against sampling errors. An increasingly popular approach, known as Wasserstein distributionally robust optimization (DRO), achieves this by...

Persistent link: https://www.econbiz.de/10014030345