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.

翻译：随着大量细粒度金融数据的可用性迅速提高，以及大数据相关技术的进步，亟需能够对此类数据进行有效表征并从中提取有意义信息的分析方法。本文提出一种多层网络方法，将欧元区银行系统提炼为多个不同的层级。网络的每一层基于欧元区多种细粒度数据源，表征银行间特定类型的金融关系。由此构建的多层网络使我们能够从不同视角描述、分析和比较欧元区银行的拓扑结构与组织形态，最终形成更完整的金融市场图景。这种细粒度的信息表征方式有望帮助研究者和从业者更好地把握金融系统动态，并为金融政策的制定提供支持，从而以更全面的视角管理和监测金融风险。