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
Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling
AI总结：
本文利用基于随机矩阵理论的层次分解方法揭示CDS市场的中尺度结构，识别出传统行业/地区因子模型无法捕捉的发行人群组，并据此提出表现更优的违约风险模型。
摘要：
在金融市场（包括信用违约互换（CDS）市场）的分析与建模中，最具挑战性的方面之一，是存在一种涌现的中间层次结构，它介于个体金融实体的微观动力学与整个市场的宏观动力学之间。这种难以捉摸的中观组织层次通常通过因子模型来寻找，这些模型最终根据地理区域和经济行业对市场进行分解。然而，在更一般的层面上，中尺度结构的存在可以通过一种完全由数据驱动的方法来揭示，即寻找金融时间序列之间经验相关矩阵的模块化且可能具有层次性的组织结构。这种方法的关键要素在于为相关矩阵定义一个合适的零模型。近期研究表明，为网络开发的社区检测技术在应用于相关矩阵时会产生内在偏差。为此，人们开发了一种基于随机矩阵理论的方法，该方法能够识别系统的最优层次分解，将其划分为内部正相关且相互负相关的社区。在此技术基础上，本文解析了CDS市场的中尺度结构，并识别出无法追溯到标准行业/地区分类法的发行人群组，因此这些群组是标准因子模型无法触及的。我们利用这种分解引入了一种新的违约风险模型，并证明其表现优于更传统的替代方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
