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Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

Autor: Cao, Jay, Chen, Jacky, Farghadani, Soroush, Hull, John, Poulos, Zissis, Wang, Zeyu, Yuan, Jun

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfo

Externí odkaz: http://arxiv.org/abs/2205.05614

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Akademický článek

Administration of low intensity vibration and a RANKL inhibitor, alone or in combination, reduces bone loss after spinal cord injury-induced immobilization in rats

Autor: Peng, Yuanzhen, Bramlett, Helen M., Dietrich, W. Dalton, Marcillo, Alex, Sanchez-Molano, Juliana, Furones-Alonso, Ofelia, Cao, Jay J., Huang, Jenney, Li, Andrew A., Feng, Jian Q., Bauman, William A., Qin, Weiping

Publikováno v: In Bone Reports December 2024 23

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Deep Hedging of Derivatives Using Reinforcement Learning

Autor: Cao, Jay, Chen, Jacky, Hull, John, Poulos, Zissis

Publikováno v: The Journal of Financial Data Science Winter 2021, 3 (1) 10-27

This paper shows how reinforcement learning can be used to derive optimal hedging strategies for derivatives when there are transaction costs. The paper illustrates the approach by showing the difference between using delta hedging and optimal hedgin

Externí odkaz: http://arxiv.org/abs/2103.16409

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Deep Learning for Exotic Option Valuation

Autor: Cao, Jay, Chen, Jacky, Hull, John, Poulos, Zissis

A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that so

Externí odkaz: http://arxiv.org/abs/2103.12551

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