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Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling
Quantitative Finance ( IF 1.3 ) Pub Date : 2021-04-08 , DOI: 10.1080/14697688.2021.1890807
I. Anagnostou 1, 2 , T. Squartini 3 , D. Kandhai 1, 2 , D. Garlaschelli 3, 4
Affiliation  

One of the most challenging aspects in the analysis and modelling of financial markets, including Credit Default Swap (CDS) markets, is the presence of an emergent, intermediate level of structure standing in between the microscopic dynamics of individual financial entities and the macroscopic dynamics of the market as a whole. This elusive, mesoscopic level of organisation is often sought for via factor models that ultimately decompose the market according to geographic regions and economic industries. However, at a more general level, the presence of mesoscopic structure might be revealed in an entirely data-driven approach, looking for a modular and possibly hierarchical organisation of the empirical correlation matrix between financial time series. The crucial ingredient in such an approach is the definition of an appropriate null model for the correlation matrix. Recent research showed that community detection techniques developed for networks become intrinsically biased when applied to correlation matrices. For this reason, a method based on Random Matrix Theory has been developed, which identifies the optimal hierarchical decomposition of the system into internally correlated and mutually anti-correlated communities. Building upon this technique, here we resolve the mesoscopic structure of the CDS market and identify groups of issuers that cannot be traced back to standard industry/region taxonomies, thereby being inaccessible to standard factor models. We use this decomposition to introduce a novel default risk model that is shown to outperform more traditional alternatives.



中文翻译:

揭示信用违约互换市场的中尺度结构以改进投资组合风险建模

包括信用违约互换 (CDS) 市场在内的金融市场分析和建模中最具挑战性的方面之一是在单个金融实体的微观动态和金融市场的宏观动态之间存在一种新兴的中间层次结构。整个市场。这种难以捉摸的、细观的组织水平通常是通过最终根据地理区域和经济行业分解市场的因​​素模型来寻找的。然而,在更一般的层面上,细观结构的存在可能会以完全数据驱动的方法来揭示,寻找金融时间序列之间经验相关矩阵的模块化和可能的分层组织。这种方法的关键要素是为相关矩阵定义适当的零模型。最近的研究表明,为网络开发的社区检测技术在应用于相关矩阵时会变得固有偏见。为此,开发了一种基于随机矩阵理论的方法,该方法将系统的最优层次分解确定为内部相关和相互反相关的社区。在此技术的基础上,我们解决了 CDS 市场的细观结构,并确定了无法追溯到标准行业/地区分类法的发行人群体,因此无法使用标准因子模型。我们使用这种分解来引入一种新的违约风险模型,该模型被证明优于更传统的替代方案。

更新日期:2021-04-08
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