Modeling financial time-series with generative adversarial networks

Shuntaro Takahashi, Yu Chen and Kumiko Tanaka-Ishii

Physica A: Statistical Mechanics and its Applications, 2019, vol. 527, issue C

Abstract: Financial time-series modeling is a challenging problem as it retains various complex statistical properties and the mechanism behind the process is unrevealed to a large extent. In this paper, a deep neural networks based approach, generative adversarial networks (GANs) for financial time-series modeling is presented. GANs learn the properties of data and generate realistic data in a data-driven manner. The GAN model produces a time-series that recovers the statistical properties of financial time-series such as the linear unpredictability, the heavy-tailed price return distribution, volatility clustering, leverage effects, the coarse-fine volatility correlation, and the gain/loss asymmetry.

Keywords: Financial market; Stylized facts; Deep learning; Generative adversarial networks (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (40)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119307277

DOI: 10.1016/j.physa.2019.121261

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