Under the complete market model assumption, risk neutral approach for pricing and hedging financial derivatives have been the standard solution for quite a while. With the rise of machine learning in the past decade, even in an incomplete market, efficient hedging of financial derivatives becomes feasible. This project aims to revisit the two recent groundbreaking works, [1] and [2], and explore potential extensions in actuarial contexts.
References:
[1] Buehler, H., Gonon, L., Teichmann, J., and Wood, B. (2019). Deep hedging, Quantitative Finance 19, 1271-1291.
[2] Buehler, H., Gonon, L., Teichmann, J., and Wood, B., Mohan, B., and Kochems, J. (2019). Deep hedging: hedging derivatives under generic market frictions using reinforcement learning, available at SSRN: https://ssrn.com/abstract=3355706.
[2] Buehler, H., Gonon, L., Teichmann, J., and Wood, B., Mohan, B., and Kochems, J. (2019). Deep hedging: hedging derivatives under generic market frictions using reinforcement learning, available at SSRN: https://ssrn.com/abstract=3355706.
Students: Yuxuan Li
Supervisor: Alfred Chong and Haoen Cui