基于动态因子Copula模型的我国银行系统性风险度量

本文基于2007年至2019年我国14家上市银行的股票收益率,构建偏态t-分布动态因子Copula模型,利用时变荷载因子刻画单家银行与整个系统的相关性,计算联合风险概率作为系统性风险整体水平的度量,基于关联性视角提出了新的单家机构系统脆弱性和系统重要性度量指标——系统脆弱性程度和系统重要性程度。该方法充分考虑了银行个体差异性和系统的内在关联性以及收益率的厚尾性和非对称性,从而能够捕捉到更多的信息且兼具时效性。研究表明:银行机构在风险聚集时期相关程度更大,联合风险概率能够准确识别出系统性风险事件且在我国推行宏观审慎评估体系以后有明显降低;整体而言,大型商业银行系统重要性水平最高,同时风险抗压能力也最强;本文使用的度量方法降低了数据获取成本且更具时效性,有助于为宏观审慎差异化监管工作提供借鉴和参考。     

系统性风险在银行间的传染路径研究——基于持有共同资产网络模型北大核心CSSCISSCI

本文将系统重要性银行、系统脆弱性银行及传染风险等指标有机结合,提出并度量了系统重要性传染路径指标。系统重要性传染路径以风险生成银行为起点,以风险承受银行为终点,从微观机构角度刻画了系统性风险在风险生成银行与风险承受银行之间的传染,兼具深入理解系统性风险的生成机制及直接面向金融监管实践的优点。本文通过实证发现,银行体系的系统性风险总体呈上升趋势,系统性风险和各家银行的系统重要性程度均与规模因素具有较强的正相关关系。风险生成银行往往是那些遭受外生冲击较大、具有高度传染性的系统重要性银行,风险承受银行则通常是遭受外生冲击较小、且与风险生成银行持有资产结构类似(高度关联性)的系统重要性银行。本文对系统重要性传染路径指标的影响因素分析进一步论证了上述结论,并通过敏感性分析论证了多轮传染和资产价格相关性等改进的必要性。     

系统性金融风险度量:一个文献综述

本文对现有测度系统性金融风险的主要方法进行了系统回顾,主要包括基于系统重要性金融机构的风险分析、经济部门债务风险度量以及银行间同业拆借网络分析等方法。本文对主流方法存在的不足进行了分析,并对系统性风险及其度量提出了更加明确的含义,即系统性风险是金融体系或多数重要金融机构面临的共同风险因素,且这些风险因素及其潜在影响是系统性风险度量的核心。据此,本文认为对系统性风险及其传导渠道的正确分析和准确评估,是及时采取宏观审慎政策以增强金融体系稳健性的重要前提。     

保险系统性风险的宏观审慎监管体系探讨北大核心SSCI

保险系统性风险及其宏观审慎监管问题是国内外理论界与实务界普遍关注的问题。本文遵循从一般系统性风险到保险系统性风险的逻辑,分析了保险系统性风险及其宏观审慎监管的理论基础,探讨了保险系统性风险宏观审慎监管体系构建的核心理念、基本目标与主要内容。保险系统性风险的监管应全面考虑保险业务创新水平、保险机构系统重要性、保险部门整体脆弱性等特征因素,秉持层级化、多元化、融合化的监管理念,从"机构"与"时间"两个维度来设计出有效的宏观审慎监管工具,对保险系统性风险的损害进行恰当的政策响应与公共治理。     

银行风险与房地产风险的趋势异同与传导特征研究——基于中国股票市场的实证研究

运用标准差法、FAMA-FRENCH三因子模型法和动态因子Copula模型法等,对2015—2021年中国银行业和房地产业市场各14家主要公司股票收益率进行分析,比较两市场总体风险、系统性风险、异质性风险和市场联合违约概率,并对风险传导方向和效率进行量化检验。结果显示:两市场风险走势基本一致,房地产市场波动比银行市场更剧烈、频繁,房地产业平均系统性风险和平均异质性风险均高于银行业;两市场联合违约概率与风险走势密切相关,系统性风险和异质性风险共同作用提升了市场联合违约概率;风险在两市场间存在交叉传导、反复传导、长期传导的特征,银行业对市场稳定的影响比房地产业更大。因此认为针对银行业市场,须重点关注其可能向其他市场传导的系统性风险,从源头上提高金融系统稳定程度;针对房地产市场,既要从宏观角度把握金融市场风险,也要从微观角度警惕异质性风险。     

系统性风险与宏观审慎监管——基于美国银行业的实证研究

后危机时期,国际金融界开始反思金融监管体制的弊端,加强基于系统性风险的宏观审慎监管,维护金融稳定成为今后改革的方向。以2009年初美联储进行压力测试的美国18家大银行为研究对象,对其风险波动率和系统性风险予以度量。实证研究表明,2007年次贷危机爆发起,美国银行业的系统性风险从低谷走向高峰,至今虽有所缓释,但仍高于危机前的水平。因此,金融监管目标框架应该是:以控制系统性风险为中心,加强宏观审慎监管,保持微观审慎监管,确保金融体系的安全与健康。     

中国货币市场基金系统性风险研究

2008年金融危机暴露出货币市场基金具有传染风险,甚至有引发系统性危机的巨大潜力。鉴于系统性风险包括系统重要性和系统脆弱性两个方面,本文尝试在CoVaR的统一框架下,采用?CoVaR和Exposure-?CoVaR方法来全面测度2007～2018年中国货币市场基金的系统重要性与脆弱性。研究发现:首先,货币市场基金的系统重要性和脆弱性在时空两个维度均未错配,极端情形下风险将迅速呈螺旋式传染,对金融稳定造成严重隐患;其次,进一步相关和回归分析表明,货币市场基金规模与系统重要性正相关,与系统脆弱性负相关,投资者集中度则与系统重要性和脆弱性均呈正相关关系;最后,系统性风险指标具有持续性特征,且货币基金自身风险与系统重要性正相关,与系统脆弱性负相关。本文的研究结论为建立货币市场基金流动性风险的严监管政策提供了理论和经验支持,对继续加强货币市场基金宏观审慎监管具有非常重要的启示意义。     

流动性剩余、流动性风险与银行业系统性风险测量

全球性金融危机过后,世界范围内的政策制定者、经济学家以及时评人士对加强金融市场监管、防范系统性风险基本达成共识。本文从流动性角度出发,对我国商业银行部门系统性风险进行了度量,同时衡量了我国上市银行的系统重要性程度。通过对商业银行个体的资产负债表数据分析,本文提出一种基于系统相对流动性剩余的概率分布来度量流动性风险,以及基于绝对流动性剩余的方差贡献度来衡量银行系统重要性程度的方法,并对我国上市银行系统性风险进行了实证分析。本文的研究结果表明,从流动性风险视角来看,部分银行对我国商业银行体系的系统风险贡献程度较高。     

系统性风险防范与宏观审慎监管——后危机时代的金融监管思考

本文详述了系统性风险和宏观审慎的涵义,并从存款保险、最后贷款人、问题银行的市场退出以及金融监管政策协调四个方面阐述了系统性风险防范的一般制度安排;同时初步探索了宏观审慎监管的框架和工具问题,特别对系统重要性机构的监管和逆周期监管措施进行了探讨。关于宏观审慎监管和微观审慎监管的动态平衡、系统性风险防范和宏观审慎监管的跨境协调问题,本文也进行了初步分析。文章最后给出关于改善中国金融监管的几点建议。     

中国系统重要性银行的风险传染性研究

本文从资本市场的角度出发,运用Copula函数方法对中国14家上市银行之间的风险传染性进行分析,使用尾部相关系数作为度量风险传染性的指标。主要结论如下:(1)通过比较次贷危机前后尾部相关系数的变化,确定工商银行、建设银行、中国银行、交通银行、民生银行和中信银行6家银行为中国的系统重要性银行;(2)中国的系统重要性银行传染性非常强,一旦发生风险可能会对其他银行乃至整个金融业产生极大的破坏力,因此,这些银行是"大而不能倒"的,必须加强对这些银行的监管,做到宏观审慎监管与微观审慎监管相结合。     

中国金融部门间系统性风险溢出的监测预警研究——基于下行和上行ΔCoES指标的实现与优化

为了对我国金融部门间的系统性风险溢出进行实时监测和有效预警,本文基于Adrian and Brunnermeier (2016) 的CoES指标构想,在左尾视角的基础上进一步引入右尾视角,构建下行和上行ΔCoES分别作为系统性风险的同期度量指标和前瞻预警指标,并提出了更为有效合理且同时适用于下行和上行ΔCoES的计算方法。本文一方面采用下行和上行ΔCoES对我国银行、证券、保险三个金融部门间的系统性风险溢出进行监测预警研究,另一方面还基于我国的经验数据检验上行和下行ΔCoES的性质。研究结果显示,我国金融部门间具有显著的系统性风险溢出效应,且三个部门间的风险溢出存在非对称性,银行部门是系统性风险的主要发送者,证券部门是系统性风险的主要接收者;三个部门两两间的风险溢出水平表现出明显的协同性和周期性,且上行的风险溢出水平高于下行。同时,基于我国的经验数据发现,上行ΔCoES对下行ΔCoES具有显著的先导性、前瞻性,上行ΔCoES可以作为系统性风险的前瞻预警指标。此外,下行ΔCoES能够引领ΔCoVaR和基于MES估计方法计算的短期ΔCoES指标,表明本文构建的下行ΔCoES实时性更强,更适合作为系统性风险的实时监测指标。     

Measuring systemic importance of financial institutions: An extreme value theory approach

This paper proposes a set of market-based measures on the systemic importance of a financial institution or a group of financial institutions, each designed to capture different aspects of systemic importance of financial institutions. Multivariate extreme value theory approach is used to estimate these measures. Using six big Canadian banks as the proxy for Canadian banking sector, we apply these measures to identify systemically important banks in Canadian banking sector and major risk contributors from international financial institutions to Canadian banking sector. The empirical evidence reveals that (i) the top three banks, RBC Financial Group, TD Bank Financial Group, and Scotiabank, are more systemically important than other banks, while we also find that the size of a financial institution should not be considered as a proxy of systemic importance; (ii) compared to the European and Asian banks, the crashes of the U.S. banks, on average, are the most damaging to Canadian banking sector, while the risk contribution to the Canadian banking sector from Asian banks is quite lower than that from banks in the U.S. and euro area; (iii) the risk contribution to Canadian banking sector exhibits “home bias”, that is, cross-country risk contribution tends to be smaller than domestic risk contribution.     

Tail dependence and indicators of systemic risk for large US depositories

In this study, we investigate the extreme loss tail dependence between stock returns of large US depository institutions. We find that stock returns exhibit strong loss dependence even in their limiting joint extremes. Motivated by this result, we derive extremal dependence-based systemic risk indicators. The proposed systemic risk indicators reflect downturns in the US financial industry very well. We also develop a set of firm-level average extremal dependence measures. We show that these firm-level measures could have been used to identify the firms that were more vulnerable to the 2007–2008 financial crisis. Additionally, we explore the performance of selected systemic risk indicators in predicting the crisis performance of large US depository institutions and find that the average stock return correlations are also good predictors of crisis period returns. Finally, we identify factors predictive of extremal dependence for the US depository institutions in a panel regression setting. Strength of extremal dependence increases with asset size and similarity of financial fundamentals. On the other hand, strength of extremal dependence decreases with capitalization, liquidity, funding stability and asset quality. We believe the proposed indicators have the potential to inform the prudential supervision of systemic risk.     

The impact of central bank transparency on systemic risk—Evidence from Central and Eastern Europe

The aim of this paper is to analyze the impact of central bank transparency on systemic risk in emerging banking markets using a sample composed of 34 banks from Central and Eastern Europe for a period spanning from 2005 through 2012. Results indicate a positive and significant relationship between central bank transparency and financial institutions’ contribution to systemic risk. On the other side, increased central bank transparency significantly reduces the idiosyncratic risk of banks. The relationship is influenced by the restrictiveness of regulatory framework. We argue that a more transparent central bank is beneficial for the banking sector from a microprudential perspective. However, it may create incentives for financial institutions to engage in risky activities and through herd behavior may increase individual contribution to the risk of the banking system.     

微观层面系统性金融风险指标的比较与适用性分析——基于中国金融系统的研究

准确测度金融机构对整体系统性金融风险的边际贡献是加强宏观审慎监管的基本前提。本文对常用的系统性金融风险指标进行了比较分析,并以“能否涵盖规模、高杠杆率和互联紧密性三方面信息”、“排序结果是否与银保监会认定的系统重要性银行名单相吻合”、“是否具有宏观经济活动预测力”三方面对上述指标在我国金融体系的适用性进行了综合评价。结果显示,SRISK更适于作为我国微观层面系统性金融风险的测度。同时,本文发现,“LRMES约等于1-exp(-18*MES)”的经验关系不具有普适性,不适用于我国金融体系。     

政策连续性与商业银行系统性风险

本文基于2007年第一季度至2019年第四季度中国A股32家上市银行非平衡面板数据,从“冲击”和“传染”两个维度考察了政策连续性对银行系统性风险的影响。实证结果表明,政策连续性程度的提高通过降低银行个体风险和减弱银行个体与系统的关联性进而显著降低了银行系统性风险。进一步分析发现,政策连续性降低了银行被动风险承担水平而非主动风险承担意愿,减弱了银行间接关联程度而非直接关联程度。异质性分析表明,经济下行和货币宽松时期,政策连续性对系统性风险的降低效应更大,并且本身破产风险越高、信息透明度越低的银行,其系统性风险受政策连续性的影响越大。区分不同类型的政策发现,货币政策、财政政策、汇率与资本项目政策的连续性上升均能显著降低银行系统性风险,其中货币政策连续性对银行系统性风险的影响力度最大。     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Systemic risk and asymmetric responses in the financial industry

To date, an operational measure of systemic risk capturing nonlinear tail-comovements between system-wide and individual bank returns has not yet been developed. This paper proposes an extension of the CoVaR methodology in Adrian and Brunnermeier (2011) to capture the asymmetric response of the banking system to positive and negative shocks to the market-valued balance sheets of individual banks. Building on a comprehensive sample of U.S. banks in the period 1990–2010, the evidence in this paper shows that ignoring asymmetries that feature tail-interdependences may lead to a severe underestimation of systemic risk. On average, the relative impact on the system of a fall in individual market value is sevenfold that of an increase. Moreover, the downward bias in systemic-risk measuring from ignoring this asymmetric pattern increases with bank size. In particular, the conditional tail-comovement between the banking system and a bank that is losing market value belonging to the top size-sorted decile is nearly 5.5 times larger than the unconditional tail-comovement versus 3.3 times for banks in the bottom decile. The asymmetric model also produces much better fitting, with the restriction that gives rise to the standard symmetric model being rejected for most firms in the sample, particularly, in the segment of large-scale banks. This result is important from a regulatory and supervisory perspective, since the asymmetric generalization enhances the capacity to monitor systemic interdependences.     

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange, and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007 to 2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor of four higher now than before the financial crisis of 2007–2008. We find that systemic risk contributions of individual transactions can be up to a factor of one thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data-driven quantification of systemic risk on national scales that reveal its true levels.