Corpus ID: 220347437

Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning

@article{Cannelli2020HedgingUR,
  title={Hedging using reinforcement learning: Contextual k-Armed Bandit versus Q-learning},
  author={Loris Cannelli and G. Nuti and M. Sala and O. Szehr},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.01623}
}
The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such as in the model of Black, Scholes and Merton, is not only unrealistic but it is also undesirable due to high transaction costs. Over the last decades stochastic optimal-control methods have been developed to balance between effective replication and losses. More recently, with the rise of artificial… Expand
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