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Environmental, social, and governance (ESG) factors are increasingly analysed to identify the risks and opportunities in contemporary economies. The banking sector influences the whole economy through the credit channel and balances its stability. The interplay of these elements motivated our main question, whether ESG scores impact European financial stability, measured for the banking sector. To this aim, we employ the cross-quantilogram methodology, which explores dependences at all levels of the distributions of two random variables. To determine the quantile dependence, we resort to methods of measuring systemic risk (Marginal Expected Shortfall—MES, CoVaR, and ΔCoVaR) for all commercial banks listed on European stock exchanges. While our approach provides a dashboard for analysis of the dependence of financial stability on ESG pillars, our findings indicate that such a connection is valid and cannot be identified with standard approaches that explore average distribution levels. We also document the differences in these impacts across the ESG pillars.

"Neural networks have been long used for time series analysis in various applications. The late boost in computer power and data engineering brought about a myriad of algorithms that are wrapped under the larger title of Data Science. The apparent proliferation of these algorithms is due to their employment for several applications that range from simple classification problems, such as spam e-mail identification, to pattern detection in images and videos and several forecasting applications. Heralding the world of machine learning, these algorithms are trained on large amounts of data with the objective to extract repetitive structures that are likely to persist. It is therefore explainable the recent attention that these algorithms are given from the perspective of economic applications. This paper employs a recurrent neural network algorithm on daily data for several stock market indices in order to detect anomalous behaviour. The approach is rooted in the jump-detection literature that has the objective to identify outlying realizations of log-returns for diverse stock market data. We show that this approach establishes economically significant patterns that can be considered as anomalies when compared to their past dynamics."

In this article, we aim to study systemic risk spillovers for European energy companies and to determine the spillover network of the energy sector with other economic sectors. To examine the spillovers within the energy sector, we employ three systemic risk measures. We then embed the results of these models into a Diebold–Yilmaz framework. Moreover, we consider an entropy procedure to extract a Bayesian formulation of its systemic risk spillover. This allows us to determine which company in our sample contributes the most to systemic risk, which company is the most vulnerable to systemic risk, and the place of the energy sector within risk networks. Our results reveal the fact that all companies manifest enhanced spillovers during 2008, early 2009, and 2020. These episodes are associated with the dynamics of the global financial crisis and the pandemic crisis. We notice that specific companies are risk drivers in the sector in both times of market turbulence and calm. Lastly, we observe that several economic sectors such as banks, capital goods, consumer services, and diversified financials generate relevant spillovers towards the energy sector.

The pattern of financial cycles in the European Union has direct impacts on financial stability and economic sustainability in view of adoption of the euro. The purpose of the article is to identify the degree of coherence of credit cycles in the countries potentially seeking to adopt the euro with the credit cycle inside the Eurozone. We first estimate the credit cycles in the selected countries and in the euro area (at the aggregate level) and filter the series with the Hodrick–Prescott filter for the period 1999Q1–2020Q4. Based on these values, we compute the indicators that define the credit cycle similarity and synchronicity in the selected countries and a set of entropy measures (block entropy, entropy rate, Bayesian entropy) to show the high degree of heterogeneity, noting that the manifestation of the global financial crisis has changed the credit cycle patterns in some countries. Our novel approach provides analytical tools to cope with euro adoption decisions, showing how the coherence of credit cycles can be increased among European countries and how the national macroprudential policies can be better coordinated, especially in light of changes caused by the pandemic crisis.

We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects of the entropy indicator for the systemic risk measures computed on the 24 sectors that compose the STOXX 600 index. Our results show that several sectors have a high proclivity for generating spillovers. In general, the largest influences are delivered by Capital Goods, Banks, Diversified Financials, Insurance, and Real Estate. We also bring detailed evidence on the sectors that are the most pregnable to spillovers and on those that represent the main contributors of spillovers.

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