Bo Wu
I am currently a PhD candidate at the Chinese University of Hong Kong under the supervision of Prof. Lingfei Li. I possess extensive experience in traditional numerical algorithms, optimization methods, as well as machine learning and reinforcement learning techniques, all of which I have applied in various financial contexts. These skills have been instrumental in my research on portfolio optimization, derivatives pricing and hedging, volatility surface modeling, and algorithmic trading.
Interests: Quantitative Finance, Reinforcement Learning, Mathematics, Stochastic Control.
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Education
2021-2025, PhD, the Chinese University of Hong Kong, H.K. -Major: Financial Engineering.
2019-2020, MSc, Kings College London, U.K. -Major: Financial Mathematics.
2015-2019, BSc, Tongji University, China. -Major: Mathematics and Applied Mathematics.
Working experience
2024, Conduct research on large‑step volatility modeling, calibration, and pricing, and evaluate the adaptability of the model in practical applications. (HK)
2021, Internship as a quantitative researcher in Zheshang Fund Management Co., Ltd., working on research and development of futures quantitative trading strategies. (Shanghai)
2015, Internship as a fixed-income analyst in Reike Investment limited. (HK)
Research
2023, Reinforcement learning for continuous-time mean-variance portfolio selection in a regime-switching market. (Available at https://doi.org/10.1016/j.jedc.2023.104787)
Presentation: The 7th PKU-NUS Annual International Conference on Quantitative Finance and Economics, Shenzhen.
Presentation: Conference on Recent Advances on Quantitative Finance, HK.
2025, Model-Based Reinforcement Learning in Diffusion Environments: Value-Aware Estimation and Financial Application. (Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5030271)
2025, Value-Aware Estimation of Diffusion Systems for Continuous-Time Reinforcement Learning: Surrogate Loss and Financial Application. (Available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5124980)
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