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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Scenario generation via Generative Adversarial Networks |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 74 |
| Date | 2021 |
| Abstract Text | This thesis investigates the application of generative adversarial networks to asset return time series. We analyze the performance of different network architectures in the unconditional and conditional frameworks. The analysis starts with univariate return time series and progresses to multivariate return time series of various asset classes. Training the model brings numerous challenges, such as mode collapse and vanishing gradients. Thereby, we apply Wasserstein generative adversarial network with gradient penalty (WGAN-GP) that overcomes those challenges. The most fundamental issue and open research problem is evaluating the performance of the trained generator. Thereby, we investigate computing the likelihood of the out-of-sample data arising from an empirical distribution estimated from the generated data. We use the best performing models in a practical application of backtesting Value-at-Risk estimates from the learned distribution. The ability to sample from the distribution enables us to observe more samples of unlikely events and gives us an upper hand over traditional methods. Value-at-Risk estimation using WGANGP applied to univariate time-series shows promising results. The application of WGAN-GP to multivariate data turns out to be more challenging. However, the performance is still competitive with classical methods. The application of generative adversarial networks to finance is a relatively recent topic that requires further research. Nevertheless, we believe it is a promising candidate for modelling financial time series in the future. |
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