Multilayer information spillover networks between oil shocks and banking sectors: Evidence from oil-rich countries

There is no doubt that oil price shocks significantly affect oil-producing countries' macroeconomic fundamentals and financial stability, mainly in crisis times. The recent oil price shocks, coupled with the COVID-19 pandemic, motivated us to investigate the connectedness and risk transmission among oil shocks and banking sectors in the Gulf Cooperation Council (GCC) economies from June 30, 2006, to September 9, 2021. Thus, we construct multilayer information spillover networks between oil price shocks and GCC banking sectors. The empirical results show that the Bahrain banking sector depicts the highest connectedness and risk transmission with oil price shocks on the extreme risk spillover layer. In addition, Kuwait and the United Arab Emirates are highly connected to oil demand shocks. Furthermore, we find a substantial increase in extreme risk spillover and volatility spillover layers during the COVID-19 period. The results of this paper have some important implications for regional portfolio risk management, alleviating systemic risk, and developing hedging and investment strategies.     

Econometric measures of connectedness and systemic risk in the finance and insurance sectors

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.     

Extreme risk spillover network: application to financial institutions

Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test to quantify extreme risk spillovers, we propose an extreme risk spillover network for analysing the interconnectedness across financial institutions. We construct extreme risk spillover networks at 1% and 5% risk levels (which we denote 1% and 5% VaR networks) based on the daily returns of 84 publicly listed financial institutions from four sectors—banks, diversified financials, insurance and real estate—during the period 2006–2015. We find that extreme risk spillover networks have a time-lag effect. Both the static and dynamic networks show that on average the real estate and bank sectors are net senders of extreme risk spillovers and the insurance and diversified financials sectors are net recipients, which coheres with the evidence from the recent global financial crisis. The networks during the 2008–2009 financial crisis and the European sovereign debt crisis exhibited distinctive topological features that differed from those in tranquil periods. Our approach supplies new information on the interconnectedness across financial agents that will prove valuable not only to investors and hedge fund managers, but also to regulators and policy-makers.     

The extreme risk connectedness of the new financial system: European evidence

The paper empirically analyses the tail risk connectedness between FinTech and the banking sector in the European context over 2015–2022. For this purpose, we use the Tail-Event driven NETworks (TENET) risk model, i.e., we can capture the behaviour of extreme (negative and positive) risk spillover within the financial system. The results highlight how most tail risk spillovers are from banks to FinTech firms. Also, the findings suggest that the spillovers of cross-sector tail risk are more significant in downside (bearish) risk conditions than in upside (bullish) one. We find evidence of an asymmetric effect of extreme risk spillover to the real economy. Finally, we evaluate the monetary policy’s impact on extreme risk. Our findings highlight the importance of closer monitoring risk spillover between FinTech institutions and the European banking system to maintain financial stability.     

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.     

A simple indicator of systemic risk

We examine the relevance and effectiveness of stock return correlations among financial institutions as an indicator of systemic risk. By analyzing the trends and fluctuations of daily stock return correlations and default correlations among the 22 largest bank holding companies and investment banks from 1988 to 2008, we find that daily stock return correlation is a simple, robust, forward-looking, and timely systemic risk indicator. There is an increasing trend in stock return correlation among banks, whereas there is no obvious correlation trend among non-banks. We also disaggregate the stock returns into systematic and idiosyncratic components and find that the correlation increases are largely driven by the increases in correlations between banks’ idiosyncratic risks, which give rise to increasing systemic risk. Correlation spikes tend to predict or coincide with significant economic or market events, especially during the 2007–2008 financial crisis. Furthermore, we show that stock return correlations offer a perspective on the level of systemic risk in the financial sector that is not already captured by default correlations. Stock return correlations are not subject to data limitations or model specification errors that other potential systemic risk measures may face. Therefore, we recommend that regulators and businesses monitor daily stock return correlations among those large and highly leveraged financial institutions to track the level of systemic risk.     

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.     

Multilayer financial networks and systemic importance: Evidence from China

In this paper, we develop a multilayer network structure and reveal the relationship between network structure and systemic risk. Unlike many previous studies, our model considers both liability and cross-holding of shares between financial institutions simultaneously. We propose a new systemic risk measurement by exploring the dynamic mechanism of financial contagion in the multilayer network. We display the network structure of Chinese financial institutions, including connectivity and diversity, and identify the systemic importance of them. We demonstrate that the multilayer network plays a non-linear role in financial risk spreading. Using the panel regression model and several experiment evidences, we show that the systemic risk can be explained more effectively by the linkage diversity more than the connectivity at both the institutional level and the system level. Our results highlight the importance of considering contagion mechanisms that go beyond a simple single-layer network structure.     

Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index

We examine the quantile connectedness of returns between the recently developed S&P 500 Twitter Sentiment Index and various asset classes. Rather than a mean-based connectedness measure, we apply quantile-connectedness to explore connectedness of means and, especially, extreme left and right tails of distributions. Using mean-based connectedness measures, the level of return connectedness between the twitter sentiment index and all financial markets is a modest 46%. However, when applying a novel quantile-based connectedness approach, we find that levels of tail-connectedness are much stronger, up to 82%, at extreme upper and lower tails. This suggests that the impact of sentiment on financial markets is much stronger during extreme positive/negative sentiment shocks. Moreover, return connectedness measures are less volatile during extreme events. Net connectedness analysis shows that the Twitter sentiment index acts as a net transmitter of return spillovers, highlighting the leading role of investor sentiment on predicting other financial markets.     

Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S.

In this paper, we propose a novel approach to examine the risk spillovers between FinTech firms and traditional financial institutions, during a time of fast technological advances. Based on the stock returns of U.S. financial and FinTech institutions, we estimate pairwise risk spillovers by using the Granger causality test across quantiles. We consider the whole distribution: the left tail (bearish case), the right tail (bullish case) and the center of the distribution and construct three types of spillover networks (downside-to-downside, upside-to-upside, and center-to-center) and obtain network-based spillover indicators. We find that linkages in the network are stronger in the bearish case when the risk of spillover is higher. FinTech institutions' risk spillover to financial institutions positively correlates with financial institutions' increase in systemic risk. These results have important policy implications, as they underscore the importance of enhancing the supervision and regulation of FinTech companies, to maintain financial stability.     

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.     

Regulation and the connectedness of insurers to the banking sector: International evidence

Using variation across countries and time in the degree to which regulations restrict banks and insurers from engaging in the same activities, we find that property/liability insurers' connectedness to the banking sector declines when regulatory restrictions increase, but life insurers' connectedness to banks does not. The results suggest that the connectedness between life insurers and banks is largely due to these institutions sharing common underlying economic and financial risk factors that exist even when regulation restricts these institutions from engaging in each other's activities.     

Research on China's financial systemic risk contagion under jump and heavy-tailed risk

To accurately measure the dynamic characteristics of systemic risk contagion under the impact of extreme financial events, we construct a research framework that analyzes the contagion dynamics of systemic risk under extreme risk impact from the perspectives of both time and space. Based on the macro-jump CCA method, this paper extracts the heterogeneous volatility sequence of financial industries considering the thick tail of the distribution of financial assets returns. Then, the dynamic variation of systemic risk in the financial sectors is characterized from the time dimension. The volatility spillover network method is used to examine the spillover contagion of systemic risk among financial system sectors from the spatial dimension. Empirical studies have found that when considering the risk contagion level, the capital market service sector plays a risk‑leading role, followed by the currency service sector and the insurance sector. The measurement indicators that consider the jump risk and the tail risk have good early warning effects on extreme financial events. Seen from the spatial direction of risk spillover, the real estate sector exhibits the most obvious risk spillover effect on other sectors and can be regarded as the source of systemic risk, which suggests differentiated regulation.     

极端气候对商业银行风险承担的影响——来自中国地方性商业银行的经验证据

深化对气候相关金融风险的认识,对于促进绿色低碳发展,防范系统性金融风险具有重要意义。本文以2004—2018年期间281家中国地方性商业银行为样本,实证检验了极端气候对银行风险承担的影响及其机制。研究发现,极端强降水气候显著提升了银行风险承担,极端高温和极端低温气候对银行风险承担不存在明显影响。极端强降水主要通过给银行信贷主体带来经济损失,影响违约概率和银行信贷资产质量,进而影响银行风险承担水平;提高灾前的保险保障水平、强化碳减排机制以及确保银行资本的充足性均有利于弱化极端气候对银行风险承担的影响;相较于以地级和省会城市工商业和居民为主要服务对象的地方性商业银行,极端强降水对以“三农”为主要服务对象的县域地方性商业银行风险承担的影响更大。因此,提升商业银行应对极端气候风险意识,提高气候灾害保险保障水平,强化碳减排机制和银行资本充足管理,均有利于降低极端气候对银行风险的影响。     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience

The Australian financial sector (AFS) is highly concentrated and interconnected. Besides, Australian banks' lending portfolios are dominated by residential mortgage loans, and 70% of insurance companies' revenues arise from non-policyholder sources. The AFS also performed relatively well during the global financial crisis (GFC). Given these distinctive features, in this paper, we examine the systemic risk contribution of Australian banks, insurance companies, and other financial services providers. We use a flexible copula-based delta conditional value-at-risk (ΔCoVaR) method across different frequencies. Further, we study the systemic risk determinants in a panel setting. We find that the major Australian banks are systemically more important than all other financial institutions. Systemic risk is typically higher after the GFC than in the pre-crisis period, despite the introduction of more stringent capital requirements. In addition, the short-term ΔCoVaR is significantly higher than the medium- and long-term ΔCoVaRs. Finally, institution-specific characteristics and market-wide variables explain the cross-sectional and time-series variation in systemic risk, and their explanatory power varies across frequencies.     

Does financial connectedness predict crises?

The global financial crisis has reignited interest in models of crisis prediction. It has also raised the question whether financial interconnectedness—a possible source of systemic risk—can serve as an early warning indicator of crises. In this paper, we examine the ability of connectedness in the global network of financial linkages to predict systemic banking crises during the 1978–2010 period. Our results indicate that increases in a country’s own connectedness and decreases in its neighbours’ connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals. Our findings suggest that financial interconnectedness has early warning potential, especially for the 2007–2010 wave of systemic banking crises.     

Does COVID-19 open a Pandora's box of changing the connectedness in energy commodities?

With the rapid spread of coronavirus, the global financial markets have been undergoing tremendous changes, which bring investors more risks in the short term. Against such background, this study concentrates on the far-reaching energy commodities, aiming to explore the impact of COVID-19 on cross-market linkages. To capture the dynamic nature of interdependence, we applied the TVP-VAR based connectedness index method and individually focused on the total, net, and pairwise connectedness. The empirical results show that there is a dramatic rise in the total connectedness in energy markets following the outbreak of COVID-19, but this change only lasted about two months and then fell back to the prior level. Further analyzing the net spillover conditions, we find that the connectedness structure has also displayed some temporary changes. At last, the spillover networks indicate that there are only three pairwise connectedness relations have changed in direction before and after the outbreak of COVID-19. We also try to discuss the underlying COVID-19 shock propagation mechanism, and the results suggest the significant mediation effect of the financial panic risk. In general, our study offers several urgent and prominent implications to understand the financial impact of COVID-19.     

宏观审慎与货币政策双支柱框架研究——基于系统性风险视角

本轮国际金融危机之后,建立“宏观审慎政策专门盯住金融稳定目标,货币政策主要关注经济稳定目标”的双支柱成为国际社会的普遍共识。本文基于系统性风险视角,深入剖析系统性风险的累积和实现机制,从时间和空间两个维度梳理宏观审慎政策实现金融稳定的有效性,以及货币政策对系统性风险造成的潜在溢出性。目前从系统性风险的时间维度探讨双支柱政策的研究已较为丰富,可以总结为宏观审慎政策的“逆周期调节”机制和货币政策的“资本缺口”机制。从系统性风险的空间维度探讨双支柱政策的研究,也即对双支柱政策如何作用和改变金融机构内部关联网络的研究正成为研究热点。本文从政策工具和影响机制上对空间维度双支柱政策进行了系统梳理。基于以上分析,本文对双支柱政策的制定提出如下建议:时间维度宏观审慎政策要关注并消除货币政策对时间维度系统性风险的溢出性,同时要加强空间维度宏观审慎政策工具的创新力度。     

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。