This work is an answer to the EIOPA 2017 report. It follows from the latter that in order to assess the potential systemic risk we should take into account the build-up of risk and in particular the risk that arises in time, as well as the interlinkages in the financial sector and the whole economy. Our main tools used to analyse the systemic risk dynamics in the European insurance sector during the years 2005-2019 are the topological indices of minimum spanning trees (MST) and the deltaCoVaR measure. We address the following questions: 1) What is the contribution to systemic risk of each of the 28 largest European insurance companies whose list includes also those appearing on the G-SIIs list? 2) Does the analysis of the deltaCoVaR of those 28 insurance companies and the conclusions we draw agree with the our claims from our latest article [Wanat S., Denkowska A. 2019]. In clear: does the most important contribution to systemic risk come from the companies that have the highest betweenness centrality or the highest degree in the MST obtained?

Financial potential is an important part of enterprise activities. The technique of the enterprise's financial potential assessment is offered in the paper. It is presented by particular stages, where each stage is related to a certain task. The characteristics of the company's financial potential, based on the analysis of the related literature, are determined. The implementation of each task is carried out. Thus, the study proposes a mechanism for managing the financial potential of enterprises, which allows to emphasize the elements that can be useful for economic development. It is based on the general strategic principles of the enterprise management. The study results can be used to assess enterprise purposes and develop the formation goals of its financial potential. It can also help to forecast and separate main directions of accumulation, formation, and distribution of financial resources. It should be noted, that analysis and control over the financial potential formation strategy, as well as the use of analysis results for specifying the strategic directions of the enterprise development, are of high importance. Therefore, the management of the financial potential is a system of rational management of business financing, which includes the formation of financial relations, emerging as a result of finance resources flow.

We construct a data-driven statistical indicator for quantifying the tail risk perceived by the EURGBP option market surrounding Brexit-related events. We show that under lognormal SABR dynamics this tail risk is closely related to the so-called martingale defect and provide a closed-form expression for this defect which can be computed by solving an inverse calibration problem. In order to cope with the the uncertainty which is inherent to this inverse problem, we adopt a Bayesian statistical parameter estimation perspective. We probe the resulting posterior densities with a combination of optimization and adaptive Markov chain Monte Carlo methods, thus providing a careful uncertainty estimation for all of the underlying parameters and the martingale defect indicator. Finally, to support the feasibility of the proposed method, we provide a Brexit "fever curve" for the year 2019.

Investors often prefer the global minimum-variance portfolio over the Markowitz portfolio as this allows the estimation risk to be confined to the covariance matrix of returns. For high-dimensional data, however, it is well-known that the sample covariance matrix is ill-conditioned and leads to suboptimal portfolios. To address this issue, we review recently proposed covariance matrix estimators and suggest a multi-fold cross-validation technique for selecting the necessary tuning parameters within each method. Conducting an extensive empirical analysis, we show how the data-driven choice of specific tuning parameters with the proposed cross-validation improves the out-of-sample performance of the global minimum-variance portfolio.

Even when confronted with the same data, agents often disagree on a model of the real-world. Here, we address the question of how interacting heterogenous agents, who disagree on what model the real-world follows, optimize their trading actions. The market has latent factors that drive prices, and agents account for the permanent impact they have on prices. This leads to a large stochastic game, where each agents' performance criteria are computed under a different probability measure. We analyse the mean-field game (MFG) limit of the stochastic game and show that the Nash equilibrium is given by the solution to a non-standard vector-valued forward-backward stochastic differential equation. Under some mild assumptions, we construct the solution in terms of expectations of the filtered states. Furthermore, we prove the MFG strategy forms an $\epsilon$-Nash equilibrium for the finite player game. Lastly, we present a least-squares Monte Carlo based algorithm for computing the equilibria and show through simulations that increasing disagreement may increase price volatility and trading activity.

We prove strong existence and uniqueness, and H\"older regularity, of a large class of stochastic Volterra equations, with singular kernels and non-Lipschitz diffusion coefficient. Extending Yamada-Watanabe's theorem, our proof relies on an approximation of the process by a sequence of semimartingales with regularised kernels. We apply these results to the rough Heston model, with square-root diffusion coefficient, recently proposed in Mathematical Finance to model the volatility of asset prices.

We establish several closed pricing formula for various path-independent payoffs, under an exponential L\'evy model driven by the Variance Gamma process. These formulas take the form of quickly convergent series and are obtained via tools from Mellin transform theory as well as from multidimensional complex analysis. Particular focus is made on the symmetric process, but extension to the asymmetric process is also provided. Speed of convergence and comparison with numerical methods are also discussed; notable feature is the accelerated convergence of the series for short term options, which constitutes an interesting improvement of numerical Fourier inversion techniques.

The affiliation to an institution provides prestige and identity to researchers and determines access to resources and infrastructure. Institutions in turn seek to affiliate researchers to secure their knowledge and skills, benefiting the research conducted within these institutions and their position in national and international rankings. This study documents the phenomenon of researchers having multiple affiliations and discusses potential causes and consequences. We analyze affiliation information of 8.5M authors from 40 countries, who published 8.9M scientific articles in 14 disciplines since 1996. We find that multiple affiliations occur both within countries as well as across borders, and that more than 60% are within the academic research sector. The share of authors with multiple affiliations increased substantially over the past two decades and particularly since the mid-2000s. The increase is particularly pronounced in countries whose funding structures became more competitive. The rise of multiple affiliations points to fundamental changes in the organisation of science and challenges our measurements of where scientific activity takes place.