An Application of Generative Adversarial Networks to One-Dimensional Value-at-Risk - Swallow, Rachel (Department of Statistical Sciences, 2024)
A generative adversarial network (GAN) is an implicit generative model made up of two neural networks. This minor dissertation applies GANs to recover target statistical distributions. GANs have a distinctive training architecture designed to create examples that reproduce target data samples. These models have been applied successfully in high-dimensional domains such as natural image generation and processing. Much less research has been reported on applications with low dimensional distributions, where properties of GANs may be better identified and understood. One such area in finance is the use of GANs for estimating value-at-risk (VaR). Through this financial application, this dissertation introduces readers to the concepts and practical implementations of GAN variants to generate one-dimensional portfolio returns over a single period. Large portions of the discussions should be accessible to anyone who has an entry-level statistics course. It is aimed at data science or finance students looking to better their understanding of GANs and the potential of these models for other financial applications. Five GAN loss variants are introduced and three of these models are practically implemented to estimate VaR. The GAN estimates are compared to more traditional VaR estimation techniques and all models are backtested. Most GAN models trained in this dissertation are able to capture key features of each of the distributions, however these models do not outperform historical VaR estimates.