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2020
Journal Article
Titel

Quant GANs: Deep generation of financial time series

Abstract
Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.
Author(s)
Wiese, Magnus
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Knobloch, Robert
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Korn, Ralf
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Kretschmer, Peter
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Zeitschrift
Quantitative finance
Thumbnail Image
DOI
10.1080/14697688.2020.1730426
Language
English
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Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
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