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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Generative Adversarial Networks for multivariate return simulation and robust portfolio optimization |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 63 |
| Date | 2019 |
| Abstract Text | Generative adversarial networks (GANs) provide a novel training approach to deep generative models by introducing two neural networks acting as agents in an adversarial game. The models have been successful in a wide range of applications in computer vision and audio synthesis and are highly regarded as a powerful approach for learning complex distributions when sampling from a distribution is of interest, rather than estimating an explicit form of the density; However, GANs are notably unstable and very difficult to train with acknowledged issues such as mode collapse and convergence problems. This work explores the ability and uses recurrent and convolutional based GANs to learn the underlying stochastic process of financial asset returns. Recent research has provided promising results in generating. univariate return series. This thesis extends the application to a multivariate setting with the goal of generating realistic multivariate sample paths of a basket of assets. Empirical experiments show that the models are able to capture the temporal as well as the cross-asset dependencies of the return series. Also, an application in a mean-variance optimization framework demonstrates that making use of the ability to sample synthetic samples with the same statistical properties as the realized paths, is useful for constructing more robust investment portfolios. |
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