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on European Economics |
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Issue of 2025–03–24
ten papers chosen by Simon Sosvilla-Rivero, Instituto Complutense de Análisis Económico |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42337 |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42334 |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42336 |
| By: | Ilias Aarab; Thomas Gottron |
| Abstract: | The rapidly increasing availability of large amounts of granular financial data, paired with the advances of big data related technologies induces the need of suitable analytics that can represent and extract meaningful information from such data. In this paper we propose a multi-layer network approach to distill the Euro Area (EA) banking system in different distinct layers. Each layer of the network represents a specific type of financial relationship between banks, based on various sources of EA granular data collections. The resulting multi-layer network allows one to describe, analyze and compare the topology and structure of EA banks from different perspectives, eventually yielding a more complete picture of the financial market. This granular information representation has the potential to enable researchers and practitioners to better apprehend financial system dynamics as well as to support financial policies to manage and monitor financial risk from a more holistic point of view. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.15611 |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42338 |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42335 |
| By: | World Bank |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42339 |
| By: | Arnone, Massimo; Costantiello, Alberto; Leogrande, Angelo |
| Abstract: | The paper deals only with the identification of the determinants of total risk exposure amount within the European banking system, while the importance of TREA within Basel III regulatory regimes is focused. The research provides the integration of an econometric investigation with high-end machine learning techniques for the identification of the influential financial variables of TREA. The most relevant financial determinants of TREA were identified as LCR, CRWEA, LA, and OREA. These also reflect complex interdependencies-for instance, the negative value of TREA and LCR would suggest that there were trade-offs made between risk-taking and liquidity management. Thus, the positive relationship with CRWEA, and even more so with derivatives over assets, underlines intrinsic risks from credit exposures and related to financial instruments' complexity. The report further iterates that there should be mechanisms for appropriate risk-weighting, adequate liquidity buffers, and proper operational controls so that the financial system can become significantly more stable and resilient. This work will put forward actionable recommendations to policy makers, regulators, and financial institutions on mitigating systemic vulnerabilities and further optimizing their strategies for compliance in view of an increasingly volatile financial landscape, leveraging from traditional econometric modeling insights with machine learning. |
| Date: | 2025–01–06 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:2u4jb_v1 |
| By: | World Bank |
| Keywords: | Macroeconomics and Economic Growth-Fiscal & Monetary Policy Social Protections and Labor-Labor Markets Macroeconomics and Economic Growth-Inflation |
| Date: | 2024–12 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wboper:42567 |
| By: | Arnone, Massimo; Leogrande, Angelo |
| Abstract: | The competitiveness of financed intermediaries cannot be based exclusively on financial sustainability, i.e. the ability to create profit, but it is also necessary to acquire a transversal vision of sustainability focused on the three ESG dimensions. The paper intends to propose a reflection on the main impacts of the integration of ESG factors on business decisionmaking and operational processes in the financial sector. In this context, we try to understand what role FinTech can play in favor of greater sustainability. Furthermore, through an empirical analysis, some determinants relating to social, environmental, and governance issues are identified which influence the volume of financial resources moved in the factoring market at a European level. Machine learning models are also proposed to estimate the volume. |
| Date: | 2024–06–27 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:753gf_v1 |