With the exception of Bitcoin, there appears to be little or no literature on GARCH modeling of cryptocurrencies. This paper provides the first GARCH modeling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency and their fits are assessed in terms of...

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We...

The central research question to answer in this feasibility study is whether the Artificial Intelligence (AI) methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g. chess or Go. By...

Generative Adversarial Networks (GANs) have shown remarkable success as a framework for trainingmodels to produce realistic-looking data. In this work, we propose a GAN to produce realistic real-valued time series, with an emphasis on their application to financial data.Our aim is having a GAN,...

We study arbitrage opportunities in diverse markets as introduced by R. Fernholz in [Fer99]. By a change of measure technique we are able to generate a variety of diverse markets. The construction is based on an absolutely continuous, but nonequivalent measure change which implies the existence...

Modeling financial time series is challenging due to their high volatility and unexpected happenings on the market. Most financial models and algorithms trying to fill the lack of historical financial time series struggle to perform and are highly vulnerable to overfitting. As an alternative, we...