The paper introduces a deep hedging approach using neural networks to optimize hedging strategies for derivatives in imperfect markets. The key takeaway is the development of the 'no-transaction band network' to address action dependence and improve efficiency in hedging, showcasing superior performance compared to traditional methods in terms of expected utility and price efficiency, and faster training. Future research focuses on addressing limitations such as non-linear transaction costs and discontinuous payoffs, as well as challenges in data availability and model explainability for real-world applications.

Read full paper: https://arxiv.org/abs/2103.01775

Tags: Deep Learning, AI for Science, Machine Learning

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