Linkages and systemic risk in the European insurance sector: Some new evidence based on dynamic spanning trees

Dependencies and systemic risk in the European insurance sector: Some new evidence based on copula-DCC-GARCH model and selected clustering methods

The subject of the present article is the study of correlations between large insurance companies and their contribution to systemic risk in the insurance sector. Our main goal is to analyze the conditional structure of the correlation on the European insurance market and to compare systemic risk in different regimes of this market. These regimes are identified by monitoring the weekly rates of returns of eight of the largest insurers (five from Europe and the biggest insurers from the USA, Canada and China) during the period January 2005 to December 2018. To this aim we use statistical cluste…

A tail dependence-based MST and their topological indicators in modelling systemic risk in the European insurance sector

In the present work we analyse the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis we assume that the stock quotations of insurance companies reflect market sentiments which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. We propose herein a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and Minimum Spanning Trees (MST). Using the copula-DCC-G…

A Dynamic MST- deltaCovar Model Of Systemic Risk In The European Insurance Sector

This work is an answer to the EIOPA 2017 report. It follows from the latter that in order to assess the potential systemic risk we should take into account the build-up of risk and in particular the risk that arises in time, as well as the interlinkages in the financial sector and the whole economy. Our main tools used to analyse the systemic risk dynamics in the European insurance sector during the years 2005-2019 are the topological indices of minimum spanning trees (MST) and the deltaCoVaR measure. We address the following questions: 1) What is the contribution to systemic risk of each of t…

Social dynamics of financial networks

The global financial crisis in 2007-2009 demonstrated that systemic risk can spread all over the world through a complex web of financial linkages, yet we still lack fundamental knowledge about the evolution of the financial web. In particular, interbank credit networks shape the core of the financial system, in which a time-varying interconnected risk emerges from a massive number of temporal transactions between banks. The current lack of understanding of the mechanics of interbank networks makes it difficult to evaluate and control systemic risk. Here, we uncover fundamental dynamics of int…

共同崩盘，分化反弹：加密货币市场中的动态条件尾部依赖

Crashing Together, Rallying Apart: Dynamic Conditional Tail Dependence in Cryptocurrency Markets

本文利用动态Hüsler-Reiss极值图模型分析13种主要加密货币在2021-2025年间的尾部依赖结构，发现下行尾部完全连通且稳定，而上行尾部随时间变薄并重组为板块结构，标准风险模型低估崩盘概率约8倍。

金融泡沫时期的“投机影响网络”：对中国股票市场的应用

"Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets

我们引入投机影响网络（SIN）来解读金融泡沫期间行业（和/或公司）之间的因果关系。SIN的构建分为两步。首先，我们开发了一个隐马尔可夫模型（HMM）的政权之间切换的正常市场阶段表示的几何布朗运动（GBM）和泡沫政权表示的随机超指数Sornette-Andersen（2002）泡沫模型。HMM的校准提供了给定证券每次处于气泡状态的概率。在两种资产都符合泡沫条件的条件下，我们利用传递熵来量化一种资产$i$的收益对另一种资产$j$的影响，并由此引入证券间SIN的邻接矩阵。我们将我们的技术应用于2005-2008年期间的中国股票市场，在此期间，一个正常的阶段之后是一个壮观的泡沫，最终在一个巨大的调整。我们引入净投机影响强度（NSII）变量作为从$i$到$j$和从$j$到$i$的转移熵之间的差异，该变量用于一系列秩序回归以预测碰撞期间承受的最大损失（\%{MaxLoss}）。对其他部门影响最大的部门损失最大。用产业部门转移熵解释金融机构的最大损失具有明显的预测能力，但反之则不然。我们还表明，中国市场数据校准的泡沫状态变量对应的制度，当市场表现出强烈的价格加速，然后明显的价格制度的变化。我们的研究结果表明，SIN可能有助于显着的技能，一般的联系为基础的系统性风险措施和预警指标的发展。