The recent crisis and the following flight to simplicity put most derivative businesses around the world under considerable pressure. We argue that the traditional modeling techniques must be extended to include product design. We propose a quantitative framework for creating products which meet...

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 hedging for a short position in a call option when...

Building on previous work of Kolm and Ritter (2019) and Cao et al. (2019), this paper explores the novel application of Deep Reinforcement Learning for Delta Hedging of options in an utility based framework where an agent is faced with a trade-off between hedging error and transaction costs...