尾部风险网络视角下的金融机构系统性风险贡献研究

金融机构的尾部风险关联模式及结构在金融系统性风险的形成演化中发挥重要作用。利用CoVaR指标及分位数回归方法,衡量金融机构之间的尾部风险传染强度,进而建立金融机构尾部风险动态网络。分析全连接网络及阈值法下过滤网络的全局和局部拓扑结构特征及其动态演化规律。建立面板数据回归模型,研究网络拓扑结构特征对金融机构系统性风险贡献的影响。实证研究发现,全连接网络的节点强度,能有效地衡量金融机构尾部风险传染强度及承受强度,并揭示其动态变化规律;各机构的尾部风险传染强度及承受强度排序匹配性存在差异;随着时间推进,各金融机构间的平均尾部风险传染路径缩短、系统性风险更易迅速累积和爆发;滞后一期的节点出度、节点入度及聚集系数越大,相应金融机构的系统性风险贡献越小;滞后一期的节点介数和节点接近中心度越大,相应金融机构的系统性风险贡献越大。研究结果对于金融机构的宏观审慎监管及系统性风险管理,提供了一个全新的基于金融机构尾部风险网络的视角。     

Assessing interbank contagion using simulated networks

This paper presents a new approach to randomly generate interbank networks while overcoming shortcomings in the availability of bank-by-bank bilateral exposures. Our model can be used to simulate and assess interbank contagion effects on banking sector soundness and resilience. We find a strongly non-linear pattern across the distribution of simulated networks, whereby only for a small percentage of networks the impact of interbank contagion will substantially reducoe average solvency of the system. In the vast majority of the simulated networks the system-wide contagion effects are largely negligible. The approach furthermore enables to form a view about the most systemic banks in the system in terms of the banks whose failure would have the most detrimental contagion effects on the system as a whole. Finally, as the simulation of the network structures is computationally very costly, we also propose a simplified measure—a so-called Systemic Probability Index—that also captures the likelihood of contagion from the failure of a given bank to honour its interbank payment obligations but at the same time is less costly to compute. We find that the SPI is broadly consistent with the results from the simulated network structures.     

突发公共卫生事件对系统性金融风险的冲击及传染效应研究

首先基于面板向量自回归模型考察了突发公共卫生事件对系统性金融风险的冲击影响,接着综合考虑突发公共卫生事件的影响及其所导致的收益率的非对称性构建单指标非对称CoVaR模型,最后借助LASSO惩罚函数与局部估计法进行求解,以此构建有向网络分析金融机构间的传染效应.研究发现:(1)突发公共卫生事件冲击会使系统性金融风险水平短...     

网络结构、资本缓冲与银行系统性风险传染效应——来自2007~2017年中国14个上市商业银行数据

银行体系稳健运行对国民经济健康发展具有重要意义。选取2007~2017年中国14个上市商业银行数据,利用债务矩阵构建银行网络,运用无残差完全分解模型将其分解为网络结构和资本缓冲两个因素,对我国银行系统性风险传染效应研究。结果表明:网络结构与银行系统性风险存在复杂的关联性,而资本缓冲与银行系统性风险有显著负相关关系;在金融危机期间,网络结构是诱导银行系统性风险波动的决定性因素,资本缓冲对降低银行系统性风险有重要作用。此外国有商业银行与其他银行债务关系更为紧密,对系统性金融风险贡献较大,中国银行处于网络结构中重要位置,中国工商银行和中国建设银行处于次重要位置。该结果为资本监管政策和宏观审慎政策有效实施提供了理论依据。     

基于复杂网络的金融风险跨市场传导机制研究——以金融危机时期(2007~2009年)数据为例

金融系统具有典型的非线性复杂系统的特征,其多层次和多重反馈特性使得金融风险跨市场传导效应更加复杂多变。选取2007~2009年金融危机时期的相关数据,构建金融网络,并采用最小生成树(MST)的方法对金融风险跨市场传导机制进行实证分析。结果表明:我国金融市场具有明显的小世界特征;金融危机期间金融市场内部各子市场间的关联程度显著加强;股票、债券、房地产和外汇市场是系统重要性市场,需要重点监控;对金融风险跨市场传导的潜在路径进行了识别,为宏观审慎监管提供了理论基础。     

Measuring bank contagion in Europe using binary spatial regression models

The recent European sovereign debt crisis clearly illustrates the importance of measuring the contagion effects of bank failures. Indeed, to better understand and monitor contagion risk, the European Central Bank has assumed the supervision of the largest banks in each of the member states. We propose a measure of contagion risk based on the spatial autocorrelation parameter of a binary spatial autoregressive model. Using different specifications of the interbank connectivity matrix, we estimate the contagion parameter for banks within the Eurozone, between 1996 and 2012. We provide evidence of high levels of systemic risk due to contagion during the European sovereign debt crisis.     

金融控股集团内部风险传染研究

金融控股集团特有的经济关系形成集团内部错综复杂的经济网络,本文基于集团内部的股权结构和关联交易形成有向网络,构建金融控股集团内部风险传染的模型。研究结果表明:持股对手数量超过某一临界值时,风险被集团成员共同分担,成员仅需承担较小的破产成本,集团稳定;持股对手数量固定时,集团内部风险传染情况与持股比重之间不存在单调关系。集团在持股对手数量和持股比重之间存在权衡,以此减小集团内部风险传播的可能性。此外,当集团成员爆发危机后,关联交易将增大集团成员的损失,从而扩大风险以及风险传播范围。本文为金融控股集团股权分配和关联交易规模提供了理论指导。     

Credit default swaps and systemic risk

We present a network model for investigating the impact on systemic risk of central clearing of over the counter (OTC) credit default swaps (CDS). We model contingent cash flows resulting from CDS and other OTC derivatives by a multi-layered network with a core-periphery structure, which is flexible enough to reproduce the gross and net exposures as well as the heterogeneity of market shares of participating institutions. We analyze illiquidity cascades resulting from liquidity shocks and show that the contagion of illiquidity takes place along a sub-network constituted by links identified as ’critical receivables’. A key role is played by the long intermediation chains inherent to the structure of the OTC network, which may turn into chains of critical receivables. We calibrate our model to data representing net and gross OTC exposures of large dealer banks and use this model to investigate the impact of central clearing on network stability. We find that, when interest rate swaps are cleared, central clearing of credit default swaps through a well-capitalized CCP can reduce the probability and the magnitude of a systemic illiquidity spiral by reducing the length of the chains of critical receivables within the financial network. These benefits are reduced, however, if some large intermediaries are not included as clearing members.     

The network structure and systemic risk in the global non-life insurance market

This paper contributes to the literature on systemic risk by assessing the systemic importance of insurers in the global non-life insurance market. First, we estimate the bilateral reinsurance claims matrix using the aggregate outstanding reinsurance data from ISIS and theoretically analyze the interconnectedness in the global reinsurance network using network indicators. The robustness of the estimated matrix is fully assured by sensitivity analysis. Second, we theoretically analyze the contagious defaults introducing the Eisenberg–Noe framework. Reinsurers play a dominant role in the reinsurance network and most of them are included in our data sample. The network analysis finds that some reinsurers with large centrality measures are central in the hierarchical structure of the network. The default analysis shows the occurrences of many stand-alone defaults and only one contagious default via the global reinsurance network after the global financial crisis. In addition, one stress test based on a hypothetical severe stress scenario predicts a few occurrences of contagious defaults in the future. It follows from these analyses that systemic risk via the global reinsurance network is relatively restricted in the global non-life insurance market. In conclusion, our methodology would help supervisory authorities develop an assessment approach for interconnectedness in the global reinsurance network and aid the implementation of insurer stress tests for default contagion.     

中国银行系统的宏观审慎监管研究

全球金融危机爆发后,对银行系统实行审慎监管已成为国内外学者及相关监管机构的共识。但目前银行系统的监管研究多为微观审慎监管,宏观审慎监管研究缺乏,尤其是对中国银行网络系统进行动态建模并进行宏观审慎监管的定量研究未见。本文首先利用中国2008至2015年16家上市银行的实际数据构建动态的中国银行网络系统模型,然后使用Component VaR、Incremental VaR、Shapley value EL以及ΔCoVaR四种风险分配机制研究中国银行网络系统的宏观审慎监管方法。研究表明:对中国银行网络系统进行宏观审慎监管能够有效提升其稳定性,并且四种机制相比之下,ΔCoVaR的监管效果最为显著,而Incremental VaR则相对较差。此外,通过宏观审慎资本与银行指标之间的相关性分析,发现Incremental VaR、Shapley value EL以及Component VaR机制下的宏观审慎资本与银行的总资产具有一定的相关性,此时宏观审慎资本可以根据银行的总资产来设置;而ΔCoVaR机制下则不相关,因此宏观审慎资本可以依据各银行的系统性风险贡献大小来设置。     

Structure and dynamics of the global financial network

In this paper, we study the evolution of the network topology for the global financial market. We evaluate the level of diversification and participation of developed and emerging economies in cross-border exposures and find that the gross exposure network is dense, the vulnerability matrix is sparse, and the network’s fragility changes over time. Prior to the financial crisis in 2008, the network was relatively fragile, whereas it became more resilient afterwards, showing a reduction in financial institutions’ risk appetite. Our results suggest that financial regulators should track down the network evolution in their systemic risk assessment.     

考虑潜伏期的关联信用风险传染机理研究

关联信用风险及其传染是现代信用风险管理的热点和难点问题。本文基于复杂网络的平均场理论,运用传染病模型,来刻画关联主体网络中关联信用风险的传染机理;并引入潜伏主体及其潜伏期,分析了网络中关联信用风险的传染过程;进一步,在关联主体BA无标度网络中,探讨了关联信用风险特征对传染过程中稳定状态的影响。研究结果表明:潜伏主体的平均潜伏期、感染主体的传染率和恢复率及传染力、信用主体的新生率和死亡率,均会影响关联信用风险传染阈值和稳定状态时感染主体密度。     

金融风险在股票市场的传染效应及联动行为分析

近年来金融危机频发并表现出了易传染性,引起了众多学者的高度关注。以动态条件相关模型研究美欧股市与中、日、韩股市间的时变相关性,并结合内生多重结构突变模型划分危机传染阶段,选用溢出指数模型分析股市间的风险溢出特性;随后,定义股市间相互影响的联动模式并构建不同传染阶段的加权有向网络图分析股市间的联动行为。研究表明:美欧股市对中日韩股市有明显的传染效应,被传染的速度和持续时间均不相同;金融传染和风险溢出展现出一定的不一致性,危机期间日股的风险溢出效应强于美股;传染效应在联动网络中表现为联动模式的高聚类性和高联动性,相比欧债危机,次贷危机时期股市间展现出更强的联动行为;日股与美欧股市在两次危机中均表现出最强的联动性,其所受影响也最大。     

Rollover risk and endogenous network dynamics

Using a dynamic network formation model, solved numerically, we study banks’ rollover decisions. We find that when the existence of linkages between market participants generates an informational externality, the newly formed network is conditioned by past architectures. Moreover, this inertia is strongly dependent on macroeconomic conditions, such as investors’ risk appetite. Simulations show that for intermediate values of the risk appetite’s parameter the financial network exhibits tipping points, i.e., the inability to maintain a threshold number of linkages may push the market into a gridlock. In this context, we study also how policy instruments, such as taxes and subsidies, affects debt rollover. Since a reduction in the policy level plays the same role as an improvement in economic fundamentals, the creation of interbank connections can be stimulated by it. Thus, in order to restart lending after a major stress situation in the interbank market a considerable reduction in the policy level is required, advising a counter-cyclical policy similar to the ones recently proposed with respect to capital requirements.     

考虑个体保护意识的关联信用风险传染机理研究

针对关联信用风险及其传染这一热点和难点问题,本文基于复杂网络异质平均场理论,运用风险传播动力学SIR经典模型,探讨风险信息促成的个体保护意识对关联信用风险传染的影响机理,并在BA无标度网络中进行数值仿真分析。研究结果表明:被感染个体数量、个体反应强度、有保护意识的易感个体比例与关联信用风险传染阈值正相关;考虑个体保护意识、增强易感个体反应强度以及提高有保护意识的易感个体比例能够有效抑制关联信用风险的传染速度和传染规模,并且能够延缓关联信用风险高峰期的到来。     

金融危机传染的非线性动力学模型

越来越多的证据表明全球金融市场是高度复杂,广泛联接的非线性动力系统网络,由于系统的非线性和复杂性特征使得金融危机极易通过系统间的耦合作用而发生传染.针对金融危机传染所表现出的非线性动力学特性,本文构建了以传染源国家和受传染国家股票收益率为变量的微分动力学传染模型.利用微分方程定性理论对模型的奇点进行讨论,得出极限环存在的条件及形式,并进而得出金融危机传染的三种情况:轻度传染、可控传染和强烈传染.     

Filtering for risk assessment of interbank network

Our paper contributes to the recent macroprudential policy addressing the resilience of financial systems in terms of their interconnectedness. We argue that beneath an interbank market, there is a fundamental latent network that affects the liquidity distributions among banks. To investigate the interbank market, we propose a framework that identifies such latent network using a statistical learning procedure. The framework reverse engineers overnight signals observed as banks conduct their reserve management on a daily basis. Our simulation-based results show that possible disruptions in funds supply are highly affected by the interconnectedness of the latent network. Hence, the proposed framework serves as an early warning system for regulators to monitor the overnight market and to detect ex-ante possible disruptions based on the inherent network characteristics.     

Dynamic structural percolation model of loss distribution for cyber risk of small and medium-sized enterprises for tree-based LAN topology

Cyber risk due to breach can be seen as a risk of a financial loss due to breach of an institution’s IT infrastructure by unauthorized parties and exploiting, taking possession of, or disclosing data assets, thus creating financial and/or reputation damage. In this paper, as a primary contribution to the existing body of actuarial literature, we propose a structural model of aggregate loss distribution for cyber risk of small and medium-sized enterprises under the assumption of a tree-based LAN topology. Up to our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. To achieve our goal, we contextualize the problem in the probabilistic graph-theoretical framework using percolation models. We assume that the IT network topology is represented by a random graph allowing for heterogeneous loss topology and providing instructive numerical examples.     

Optimal intervention under stress scenarios: A case of the Korean financial system

We consider optimal intervention methods under budget constraints when financial systems face economic shocks. We propose two policies formulated by mixed-integer linear programs where regulators inject cash into institutions. One is to minimize systemic losses, and the other is to minimize the number of defaulting institutions. Using publicly available data on the Korean financial system, we construct its entire network and apply stress scenarios to the system to compare the performances of intervention strategies and derive insights on their workings.     

Heterogeneous credit portfolios and the dynamics of the aggregate losses

We study the impact of contagion in a network of firms facing credit risk. We describe an intensity based model where the homogeneity assumption is broken by introducing a random environment that makes it possible to take into account the idiosyncratic characteristics of the firms. We shall see that our model goes behind the identification of groups of firms that can be considered basically exchangeable. Despite this heterogeneity assumption our model has the advantage of being totally tractable. The aim is to quantify the losses that a bank may suffer in a large credit portfolio. Relying on a large deviation principle on the trajectory space of the process, we state a suitable law of large numbers and a central limit theorem useful for studying large portfolio losses. Simulation results are provided as well as applications to portfolio loss distribution analysis.