Evaluation of a bidirectional GAN on high dimensional data

Abstract

In statistics and machine learning it is well known that as the dimensionality of a space increases, an exponentially greater amount of data is necessary to accurately analyze it. This is a problem currently faced by Svenska Handelsbanken AB. As they aim to simulate future markets, they require methods of estimating densities of historical markets in order to generate new data points on which to produce the simulations. This thesis investigated the ability of a novel machine learning algorithm to generate data that manages to capture tail dependencies that common statistical models fail to do. The performance was first measured on a simulated data set where the means and variances were already known, followed by measuring the performance on real market data. The results on the market data made it clear that the algorithm was not capable of capturing tail dependencies as desired as it generally generated points of much smaller variance than the original data. However, the results on the simulated data implied that on a data set of roughly only ten times the size, which in machine learning is not extremely large, the algorithm would likely generate data according to the original distribution much more consistently.