The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) is considered by many market participants as a common measure of market risk and investors' sentiment, representing the market's expectation of the 30-day-ahead looking implied volatility obtained from real-time prices of options...

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...

This paper studies a dynamic market microstructure model, in which a strategic market maker competes with an informed trader. We include the presence of noise traders and limit order traders in our setup. Our model is a N-period model. We give necessary and sufficient conditions for an...

We present an order flow model framework for limit order driven markets. Different from previous models we explicitly model a reference price process that “sweeps” the limit order book as it fluctuates up and down. Our framework allows us to use any stochastic process to model this reference...

Is there an informational gain by training a Deep Reinforcement Learning agent for automated stock trading using other time series than the one to be traded? In this work, we implement a DRL algorithm in a solid framework within a model-free and actor-critic approach and learn it with 21 global...

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,...

Since the early beginning of investing as it was commonly seen as a form of gambling for the rich and wealthy, the idea of Harry Markowitz was revolutionising the way of thinking and how portfolios should be constructed. However, today the traditional mean-variance portfolios are still not fully...

Financial trading has been widely analyzed for decades with market participants and academics always looking for advanced methods to improve trading performance. Deep reinforcement learning (DRL), a recently reinvigorated method with significant success in multiple domains, still has to show its...

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...