This is a review about financial dependencies which merges efforts in econophysics and financial economics during the last few years. We focus on the most relevant contributions to the analysis of asset markets’ dependencies, especially correlational studies, which in our opinion are beneficial for researchers in both fields. In econophysics, these dependencies can be modeled to describe financial markets as evolving complex networks. In particular, we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial datasets. In financial economics these dependencies can describe asset comovement and spill-overs. In particular, several models are presented that show how network and factor model approaches are related to modeling of multivariate volatility and asset returns, respectively. Finally, we sketch out how these studies can inspire future research and how they contribute to support researchers in both fields to find a better and a stronger common language.

这是一篇关于金融依赖的综述，融合了过去几年经济物理学和金融经济学的研究成果。我们专注于对资产市场依赖性分析最相关的贡献，尤其是相关研究，我们认为这对这两个领域的研究人员都有好处。在经济物理学中，可以对这些依赖关系进行建模，以将金融市场描述为不断发展的复杂网络。特别是，我们展示了一种描述依赖关系的有用方法是通过信息过滤网络，该网络能够在复杂的金融数据集中检索相关且有意义的信息。在金融经济学中，这些依赖关系可以描述资产联动和溢出效应。尤其，提出了几个模型，分别显示了网络和因子模型方法如何与多元波动率和资产回报的建模相关。最后，我们概述了这些研究如何激发未来的研究，以及它们如何有助于支持这两个领域的研究人员找到更好、更强大的共同语言。