Deep Hedging: Learning to Simulate Equity Option Markets

Autor:	Wiese, Magnus, Bai, Lianjun, Wood, Ben, Buehler, Hans
Rok vydání:	2019
Předmět:	Quantitative Finance - Computational Finance Computer Science - Machine Learning Quantitative Finance - Mathematical Finance Quantitative Finance - Statistical Finance Statistics - Machine Learning
Zdroj:	NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy
Druh dokumentu:	Working Paper
Popis:	We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.01700 Zobrazit plný text záznamu View this record from Arxiv