Forecasting systemic impact in financial networks

We propose a methodology for forecasting the systemic impact of financial institutions in interconnected systems. Utilizing a five-year sample including the 2008/9 financial crisis, we demonstrate how the approach can be used for the timely systemic risk monitoring of large European banks and insurance companies. We predict firms’ systemic relevance as the marginal impact of individual downside risks on systemic distress. So-called systemic risk betas account for a company’s position within the network of financial interdependencies, in addition to its balance sheet characteristics and its exposure to general market conditions. Relying only on publicly available daily market data, we determine time-varying systemic risk networks, and forecast the systemic relevance on a quarterly basis. Our empirical findings reveal time-varying risk channels and firms’ specific roles as risk transmitters and/or risk recipients.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

Connectedness and risk spillovers in China’s stock market: A sectoral analysis

This paper shows how sectors in the Chinese stock market are connected and investigates risk spillovers across these sectors. Using graph theory and a recently developed time series technique, we are able to identify the systemically important sector in the market and the patterns of risk spillovers across sectors over time. Unlike standard econometric modeling, graph theory enables us to approach this question in a more reader-friendly way. The empirical results show that Industrial sector plays a central role and should thus be considered the systemically most important sector in the Chinese stock market. The spillover structure is found to be time-varying. While Industrial sector dominates the system for most of the time, other sectors such as Consumer Discretionary sector also occasionally appear as the central sector. Our empirical results also indicate that the simple correlation-based approach can produce equally useful information as more advanced econometric models.     

Contagion effect of systemic risk among industry sectors in China’s stock market

This paper constructs a tail event driven network to investigate the interdependence of tail risks among industries in the Chinese stock market from 2014 to 2019, and identifies systemically important industries that have made significant contributions to risk contagion by systemic risk decomposition technique. The empirical results suggest strong linkages among industry sectors. The risk profiles of certain industries under close supply–demand relationships are positively correlated, whereas the financial industry, particularly banking, proves to be the principal risk diversifier in the network, with the household appliance, food and drink industries performing likewise an important role in risk diversification. Based on the TENQR model, further study on additional information provided by the industrial chain structure demonstrates that the upstream industry dominates the spread of risks under extreme market conditions. Our findings are of constructive significance to the anticipative introduction of corresponding policies by regulatory authorities, and are also instructive to the investors’ allocation of assets.     

现代风险导向审计对国家审计风险控制的启示

我国发布的审计风险准则征求意见稿提出了新的审计风险模型:审计风险取决于重大错报风险和检查风险,昭示我国民间审计将全面实行现代风险导向审计模式。本文通过比较国家审计风险和民间审计风险的异同,以期发现民间审计实行的现代风险导向审计模式对控制国家审计风险值得借鉴的地方,以完善我国国家审计对风险控制的措施。     

中国影子银行部门系统性风险的形成、影响与应对

本文分析了影子银行风险传染机制及其影响,在违约风险基于会计账户传染的马尔科夫过程假设下,运用投入产出法构建影子银行系统性风险测度模型,以2007-2012年中国影子银行业务数据进行检验,结果显示：信托公司部门是主要的风险源,银行部门是系统性风险最主要的承担者,观测期内影子银行部门系统性风险整体呈现上升趋势。防控系统性风险应从影子银行业务风险隔离机制、资本与杠杆率监管、信息透明度、宏观审慎框架和风险应急机制等建设着手。     

Prices,debt and market structure in an agent-based model of the financial market

We develop an agent-based model in which heterogeneous and boundedly rational agents interact by trading a risky asset at an endogenously set price. Agents are endowed with balance sheets comprising the risky asset as well as cash on the asset side and equity capital as well as debt on the liabilities side. A number of findings emerge when simulating the model: we find that the empirically observable log-normal distribution of bank balance sheet size naturally emerges and that higher levels of leverage lead to a greater inequality among agents. Furthermore, greater leverage increases the frequency of bankruptcies and systemic events. Credit frictions, which we define as the stickiness of debt adjustments, are able to explain a key difference in the relation between leverage and assets observed for different bank types. Lowering credit frictions leads to an increasingly procyclical behavior of leverage, which is typical for investment banks. Nevertheless, the impact of credit frictions on the fragility of the model financial system is complex. Lower frictions do increase the stability of the system most of the time, while systemic events become more probable. In particular, we observe an increasing frequency of severe liquidity crises that can lead to the collapse of the entire model financial system.     

EXTREME VALUE THEORY IN FINANCE: A SURVEY

Extreme value theory is concerned with the study of the asymptotic distribution of extreme events, that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations. Statistical methods derived from it have been employed increasingly in finance, especially for risk measurement. This paper surveys some of those main applications, namely for testing different distributional assumptions for the data, for Value‐at‐Risk and Expected Shortfall calculations, for asset allocation under safety‐first type constraints, and for the study of contagion and dependence across markets under conditions of stress.     

经济政策不确定性、共同信息溢出与股市行业极端风险共振

采用QVAR-DY模型对中国股票市场10个一级行业极端风险共振进行测度,并结合国内外经济政策不确定性对信息环境的影响,通过构建TVP-FAVAR模型探索经济政策不确定性、共同信息溢出与行业风险共振的动态演变关系,分析我国行业风险极端共振的驱动因素。研究结果表明：经济政策不确定性降低了金融市场信息环境的质量,推升了共同信息溢出水平,进而加剧了行业间极端风险共振。进一步研究发现：共同信息溢出增强了经济政策不确定性作用效果,在私有信息模糊,共同信息突出的时期,经济政策不确定性对行业风险共振影响强度更大,作用时间更长。鉴于此,应密切关注国内外经济政策不确定性,加强宏观审慎和金融监管,积极稳妥地化解行业风险共振,从而促进经济高质量发展。     

强监管下金融实体行业间极端风险溢出效应测度与影响因素分析

利用动态与静态CoVaR模型、依据2009～2020年日频数据测度强监管下我国金融实体行业间极端风险溢出效应,并对影响因素进行实证分析。结果表明：强监管下金融实体行业间极端风险溢出效应整体呈下降趋势;房地产、化工、能源与金融行业间极端风险溢出效应明显降低,通信、公共事业等实体行业与金融业间极端风险溢出效应未发生明显变化。影响因素回归结果显示：金融强监管在实体行业对金融业极端风险输出以及金融业对实体行业极端风险输出中均具有抑制作用。因此,需从构建协同监管框架、加大关键领域风险防控以及强化实体行业内控能力等方面着手,预防金融实体行业间的极端风险。