Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.

Accounting for model uncertainty in risk management and option pricing leads to infinite dimensional optimization problems which are both analytically and numerically intractable. In this article we study when this hurdle can be overcome for the so-called optimized certainty equivalent risk measure (OCE) -- including the average value-at-risk as a special case. First we focus on the case where the uncertainty is modeled by a nonlinear expectation penalizing distributions that are "far" in terms of optimal-transport distance (Wasserstein distance for instance) from a given baseline distribution. It turns out that the computation of the robust OCE reduces to a finite dimensional problem, which in some cases can even be solved explicitly. This principle also applies to the shortfall risk measure as well as for the pricing of European options. Further, we derive convex dual representations of the robust OCE for measurable claims without any assumptions on the set of distributions. Finally, we give conditions on the latter set under which the robust average value-at-risk is a tail risk measure.

It is well known that the minimal superhedging price of a contingent claim is too high for practical use. In a continuous-time model uncertainty framework, we consider a relaxed hedging criterion based on acceptable shortfall risks. Combining existing aggregation and convex dual representation theorems, we derive duality results for the minimal price on the set of upper semicontinuous discounted claims.

We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.

Until recently, analysis of optimal global climate policy has focused on mitigation. Exploration of policies to meet the 1.5{\deg}C target have brought carbon dioxide removal (CDR), a second instrument, into the climate policy mainstream. Far less agreement exists regarding the role of solar geoengineering (SG), a third instrument to limit global climate risk. Integrated assessment modelling (IAM) studies offer little guidance on trade-offs between these three instruments because they have dealt with CDR and SG in isolation. Here, I extend the Dynamic Integrated model of Climate and Economy (DICE) to include both CDR and SG to explore the temporal ordering of the three instruments. Contrary to implicit assumptions that SG would be employed only after mitigation and CDR are exhausted, I find that SG is introduced parallel to mitigation temporary reducing climate risks during the era of peak CO2 concentrations. CDR reduces concentrations after mitigation is exhausted, enabling SG phasing out.

We propose in this work a kinetic wealth-exchange model of economic growth by introducing saving as a non consumed fraction of production. In this new model, which starts also from microeconomic arguments, it is found that economic transactions between pairs of agents leads the system to a macroscopic behavior where total wealth is not conserved and it is possible to have an economic growth which is assumed as the increasing of total production in time. This last macroeconomic result, that we find both numerically through a Monte Carlo based simulation method and analytically in the framework of a mean field approximation, corresponds to the economic growth scenario described by the well known Solow model developed in the economic neoclassical theory. If additionally to the income related with production due to return on individual capital, it is also included the individual labor income in the model, then the Thomas Piketty's second fundamental law of capitalism is found as a emergent property of the system. We consider that the results obtained in this paper shows how Econophysics can help to understand the connection between macroeconomics and microeconomics.

We study the effect of religion and intense religious experiences on terrorism by focusing on one of the five pillars of Islam: Ramadan fasting. For identification, we exploit two facts: First, daily fasting from dawn to sunset during Ramadan is considered mandatory for most Muslims. Second, the Islamic calendar is not synchronized with the solar cycle. We find a robust negative effect of more intense Ramadan fasting on terrorist events within districts and country-years in predominantly Muslim countries. This effect seems to operate partly through decreases in public support for terrorism and the operational capabilities of terrorist groups.

We exhibit sufficient conditions such that components of a multidimensional SDE giving rise to a local martingale $M$ are strict local martingales or martingales. We assume that the equations have diffusion coefficients of the form $\sigma(M_t,v_t),$ with $v_t$ being a stochastic volatility term.

The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors.