Quant GANs: deep generation of financial time series
By Magnus Wiese, R. Knobloch, Ralf Korn, Peter Kretschmer. Quantitative finance (Print), 2019
352 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/3cf57cad75d71bffac9fc4589d7b294d90558a13
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Citations in the corpus
- Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, methodology): “For example, Wiese et al. [31] and Li et al. [54] estimate statistical distances between the test data and generated data.”
- A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, methodology): “GANs have also been used in many other domains such as malware detection [176], chess game playing [177], network pruning [178, 179], spatial representation learning [180], mobile user profiling [181], data augmentation [182, 183], heterogeneous information networks [184], privacy-preserving [185 - ”
- Recent advances in reinforcement learning in finance (2021, background): “This leads to important questions of designing more sample-efficient RL algorithms for financial applications or developing good market simulators that could generate (unlimited) realistic market scenarios Wiese et al. (2020).”