We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly...

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

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...

Momentum trading strategies are thoroughly described in the academic literature and used in many trading strategies by hedge funds, asset managers, and proprietary traders. Baz et al. (2015) describe a momentum strategy for different asset classes in great detail from a practitioner's point 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...