Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

A global perspective on macroprudential policy interaction with systemic risk,real economic activity,and monetary intervention

The study empirically assesses how macroprudential policy interacts with systemic risk,industrial production,and monetary intervention on a global level from January 2006 to December 2018.We adopt the aggregate proxies of these variables,capturing their global effects,and use a novel econometric technique,namely,smooth local projections.The study finds that global macroprudential policy leads the monetary policy,exhibiting a countercyclical pattern concerning industrial production.The latter has an inverse bidirectional linkage with systemic risk.Thus,an ex-ante tight macroprudential policy can indirectly mitigate global systemic risk through its pro-growth effect on industrial production,although no convincing evidence exists for the direct impact of a macroprudential intervention on systemic risk.The study results endure several extensions and a robustness check,which builds on alternative measures of global systemic stress and real economic activity,thereby legitimizing the increased importance attached to the macroprudential policy since the 2007–2009 global financial crisis.

Population Ageing,Financial Leverage and Systemic Risk

Population ageing and high financial leverage are two common problems .faced by the worM＇s major economies. The recent financial crisis proved that the two issues could lead to systemic risk if not handled properly. Based on dynamic panel data from 1980 to 2012 in 119 countries, this paper examines the impact of population ageing on financial leverage J？om an empirical point of view and finds that there is a signOqcant inverted U-shape relationship between the two. In addition, empirical studies show that after passing the ＂turning point＂ of ageing, there will be a marked increase in the probability of financial crisis during the ＂deleveraging＂ process. It can be projected from the empirical conclusions of this paper that China will likely enter the range of a turning point between 2019 and 2028. After that, population ageing, deleveraging and asset price collapse may have a ＂resonance＂ effect to severely impact the stability of the financial system. Therefore, China should shift to more proactive macro financial regulations as quickly as possible, as dynamic and robust management of financial leverage and forward-looking control of bubbles could ensure that the financial system remain flexible enough to avoid systemic risk to the greatest extent.

Systemic Risk of Conventional and Islamic Banks: Comparison with Graphical Network Models

The main aim of this paper is to compare the stability, in terms of systemic risk, of conventional and Islamic banking systems. To this aim, we propose correlation network models for stock market returns based on graphical Gaussian distributions, which allows us to capture the contagion effects that move along countries. We also consider Bayesian graphical models, to account for model uncertainty in the measurement of financial systems interconnectedness. Our proposed model is applied to the Middle East and North Africa (MENA) region banking sector, characterized by the presence of both conventional and Islamic banks, for the period from 2007 to the beginning of 2014. Our empirical findings show that there are differences in the systemic risk and stability of the two banking systems during crisis times. In addition, the differences are subject to country specific effects that are amplified during crisis period.

Credit Default Swaps （CDSs） and Systemic Risks

The use of credit default swaps （CDSs） has become increasingly popular over time. Between 2002 and 2007, gross notional amounts outstanding grew from below S2 trillion to nearly S60 trillion. The recent crisis has revealed several shortcomings in CDS market practices and structure. In addition, management of counterparty risk has proved insufficient, as has in some instances the settlement of contracts following a credit event. However, past problems should not distract from the potential benefits of these instruments. In particular, CDSs help complete markets, as they provide an effective means to hedge and trade credit risk. CDSs allow financial institutions to better manage their exposures, and investors benefit from an enhanced investment universe. The purpose of this paper is to present a complete and practical exposition of the CDS market and to explore how the development of the CDS market has played an important role in the credit risk markets. Currently, the CDS market is transforming into a more stable system. Various measures are being put in place to help enhance market transparency and mitigate operational and systemic risk. In particular, central counterparties have started to operate, which will eventually lead to an improved management of individual as well as system-wide risks.

Research on Systemic Risk Spillover Effect of Chinese Listed Commercial Banks——Based on Quantile CoVaR Model

The banking industry plays an important role in China's financial market, and the systemic risk of the banking industry has astrong risk spillover effect. This paper measures the systemic risk spillover effect of Chinese listed commercial banks byconstructing the quantile CoVaR model. The study concluded that when an extreme risk event occurs, the overall risk spillovereffect of a single bank on the banking system is greater than the risk spillover effect of the banking system on a single bank, thevalue of VaR is smaller than the actual risk value when measuring the risk value of commercial banks and the CoVaR model ismore accurate in measuring systemic risk.

Systemic Risk in Chinese Commodity Futures Markets: A Graph Theory Analysis

This paper sets out to explore the contagion of systemic risk in Chinese commodity futures market based on specific tools of the graph-theory.More precisely,we use minimum spanning trees as a way to identify the most probable path for the transmission of prices shocks.In the sample of 30 kinds of Chinese commodity futures,we construct the MST and obtain the most probable and the shortest path for the transmission of a prices shock.We find that metal futures play an important role in commodity futures market and copper stands at the heart of the system(The core position of the system is very important for the transmission of system risk).And our results also reveal that when the risk occurs,the MST structure becomes smaller,leading to the most effective transmission path of risk becomes shorter.

Measuring Financial Systemic Risk:Net Liability Clearing Mechanism and Contagion Effect

Following the framework of E-N model(Eisenberg and Noe,2001),the authors consider a new clearing mechanism based on net liabilities among financial institutions since the liabilities between the counterparties should be deducted accordingly when clearing in practice.As the basis for systemic risk measurement,similar to the original E-N model,the authors first establish some good properties for the clearing payment vector according to a more general model.Then,the authors investigate how risk spreads among institutions through the liability network forming the risk contagion channel.Finally,the authors illustrate with a specific example that the original E-N clearing mechanism may misidentify the systemic important institutions,and theoretically show that it may also overestimate the risk compared with the netting clearing mechanism.

The study on systemic risk of rural finance based on macro-micro big data and machine learning

It’s the basic premise of promoting the healthy development of rural finance and strengthen-ing macro-prudential supervision to measure the systemic risk of rural finance accurately.We establish the dynamic factor CAPM and make an all-round and multi-angle quantitative study on the systemic risk of rural finance in China by constructing macro-micro index system and using machine learning to reduce the dimension of high-dimensional data.Our results show that the dynamic factor CAPM of using macro-micro big data can evaluate systemic risk of rural finance more comprehensively and systematically,and machine learning performs well in processing high-dimensional data.In addition,China’s rural financial systemic risk is stable compared with the Shanghai and Shenzhen main markets,but it is also susceptible to macro and micro influ-enced factors.Finally,it is pointed out that the early warning system of rural financial systemic risk could be constructed at macro and micro level,respectively.

Systematic Risk Analysis of Listed Financial Institutions from the Perspective of Complex Network

Based on the complex network theory,this paper studies the systemic financial risks in China’s financial market.According to the industry classification of the China Securities Regulatory Commission in 2012,the daily closing prices of 45 listed financial institutions are collected and the daily return rates of each financial institution are measured according to the logarithmic return rate calculation formula.In this paper,the risk spillover value ΔCoVaR is used to measure the contribution degree of each financial institution to systemic risk.Finally,the relationship between the risk spillover valueΔCoVaR and the node topology index of the risk transmission network is investigated by using a regression model,and some policy suggestions are put forward based on the regression results.

Do Cryptocurrencies In crease the Systemic Risk of the Global Financial Market?

The advance of cryptocurrencies has sparked wide concern over their interplay with the existing global financial market.This paper analyzes the risk spillover relation between cryptocurrencies and major financial assets,and unravels how cryptocurrencies could influence global financial systemic risk.We find that cryptocurrencies function as a separate risk source from traditional assets.Major legislative,financial and technological events in the cryptocurrency market may affect risk spillover dynamics.Although the overall penetration of cryptocurrencies is not yet deep,introducing cryptocurrency can significantly increase the systemic risk to traditional markets during low risk level episodes.

Understanding systemic risk induced by climate change

The systemic risk induced by climate change represents one of the most prominent threats facing humanity and has attracted increasing attention since the outbreak of the COVID-19 pandemic at the end of 2019.The existing literature highlights the importance of systemic risk induced by climate change,but there are still deficiencies in understanding its dynamics and assessing the risk.Aiming to bridge this gap,this study develops a theoretical framework and employs two cases to illustrate the concept,origin,occurrence,propagation,evolution,and assessment framework of systemic risk induced by climate change.The key findings include:1)systemic risk induced by climate change derives from the rapid growth of greenhouse gas,emissions,increasingly complex connections among different socioeconomic systems,and continuous changes in exposure and vulnerability;2)systemic risk induced by climate change is a holistic risk generated by the interconnection,interaction,and dynamic evolution of different types of single risks,and its fundamental,defining feature is cascading effects.The extent of risk propagation and its duration depend on the characteristics of the various discrete risks that are connected to make up the systemic risk;3)impact domains,severity of impact,and probability of occurrences are three core indicators in systemic risk assessment,and the impact domains should include the economy,society,homeland security,human health,and living conditions.We propose to deepen systemic risk research from three aspects:to develop theories to understand the mechanism of systemic risk;to conduct empirical research to assess future risks;and to develop countermeasures to mitigate the risk.

The joint impact of bankruptcy costs, fire sales and cross-holdings on systemic risk in financial networks

The paper presents a comprehensive model of a banking system that integrates network effects,bankruptcy costs,fire sales,and cross-holdings.For the integrated financial market we prove the existence of a price-payment equilibrium and design an algorithm for the computation of the greatest and the least equilibrium.The number of defaults corresponding to the greatest price-payment equilibrium is analyzed in several comparative case studies.These illustrate the individual and joint impact of interbank liabilities,bankruptcy costs,fire sales and cross-holdings on systemic risk.We study policy implications and regulatory instruments,including central bank guarantees and quantitative easing,the significance of last wills of financial institutions,and capital requirements.

Connectedness and systemic risk of the banking industry along the Belt and Road

This paper adopts the tail-event driven network(TENET)framework to explore the connectedness and systemic risk of the banking industry along the Belt and Road(B&R)based on weekly returns of 377 publicly-listed banks from 2014 to 2019.We conduct the connectedness analysis from four levels(i.e.,system,region,country and institution)and identify the systemic risk contribution of banks.We find that the dynamic total connectedness reached its peak during the outbreak of the abnormal fluctuations of Chinese stock market in 2015-2016 and its trough during the Brexit vote,and subsequently experienced several periodic fluctuations at a relatively high position.In the B&R banking system,the intra-regional tail risk spillovers are remarkably stronger than the inter-regional tail risk spillovers during the post-crisis period.In addition,the panel regressions estimated by the least squares dummy variable model show that the cross-border merger and acquisitions(M&As)and the merchandise trade export are important drivers for the tail-connectedness across the B&R countries.Our study provides regulators with insightful implications on the systemic risk supervision of the B&R banking industry.

How is Systemic Risk Amplified by Three Typical Financial Networks

Financial institutions are typically tied together via inter-liability,portfolio overlapping and share cross-holding.These connections among financial institutions constitute the three most common financial networks,which may lead to financial risk contagion and even systemic risk when some institutions suffer shock.In this paper,firstly,for a given shock,we prove the existence of the equilibrium clearing vector of the financial system characterized by these three typical financial networks.Then,we mathematically derive an analytical form to show how these three contagion channels jointly affect and amplify the loss of the non-default institutions,and explain how the lack of liquidity of external investment assets exacerbates the loss caused by portfolio overlapping.Finally,the influence of the characteristics of financial network on risk contagion is verified by numerical simulation.These results provide basis for understanding the financial systemic risk contagion in the real world.

The Formation Mechanism of Systemic Financial Risk under External Shocks

By incorporating both the fire sales contagion mechanism and the bankruptcy contagion mechanism into a bank network model,this paper examines how risks are generated under dynamic shocks.In particular,this paper constructs systemic risk indicators suitable for analyzing multiple rounds of contagion under different shocks(time dimension)and from institutions and assets(spatial dimension).Indicators that measure the indirect relevance between institutions and between assets are also innovatively built.It is found that due to deleveraging or bankruptcy among a large number of banks,the systemic risk exhibits an upward trend marked by intermittent jumps under varying intensities of shocks.Risks are generated mainly through the fire sales contagion mechanism of deleveraging under small shocks,and through the bankruptcy contagion mechanism under large shocks.In terms of influencing factors,a stronger indirect relevance,a lower leverage skewness and a higher leverage level in the banking system lead to higher risks.In particular,the influence of leverage skewness on systemic risk is stronger than that of leverage level.

Measuring systemic importance of banks considering risk interactions: An ANOVA-like decomposition method

The systemic importance of a bank is usually measured by its effect on the banking system,conditional on the insolvency of the bank and solvency of other banks.However,banks encounter different kinds of shocks simultaneously in reality.So that,the conditional re-sults give biased estimates of banks'systemic importance when potential risks are ignored.Researchers like Tarashev et al.proposed the Shapley value method to deal with risk in-teractions,but it suffers heavy computational costs.This paper proposes an ANOVA-like decomposition method to measure the systemic importance of banks in more compli-cated and realistic environments,which considers both interactions and individual effects of multiple shocks and provides a more exact estimation of systemic importance.It is found that the method proposed in this paper fits well in the network models.And meanwhile,a discussion between the method proposed in this paper and the Shapley value method is made based on the numerical example,which aims to demonstrate it's the advantages.The Shapley value method requires 2n subsystems,while the ANOVA-like decomposition method requires only n+1 model runs.In the application part,the pro-posed method is adopted to measure the systemic importance of 16 Chinese listed banks.With low computational costs,the model outputs the individual effect,interaction,and total effect of each bank.The results confirm that interactions of different shocks play a significant role in the systemic importance of a bank;thus,the total effect considering interactions should be adopted.

China’s Systemic Financial Risk:Basic Dimensions,Key Areas,and Evolving Trends

The COVID-19 pandemic,the regulation of real estate,and external uncertainties are the core variables in the recent evolution of China’s financial risks,and overall planning and structural deployment are the key guarantees for China’s financial stability.From an aggregate perspective,China’s systemic financial risk tended to ease overall in 2021,but remained high.The risk profile of China’s financial system in 2021 presented five important features.First,the macro leverage ratio fell slightly,but exposed the hidden dangers of balance sheet recession.Second,there was a certain blockage in the transmission of financial system liquidity to the real economy.Third,the fragility of the financial system was further exposed,the bond default balance reached a new high,the structural differentiation of bonds between state-owned and private enterprises became prominent,and private enterprise default became more serious.Fourth,the contagion effect of domestic cross-market financial risks remained significant.Fifth,the international political and economic situation was volatile,and spillover effects such as the rising prices of raw materials,the inauguration of a new US administration,and the shift of the Federal Reserve’s monetary policy were significantly strengthened.In terms of key risk areas,the risks of the real estate market,hidden government debt,and small-and medium-sized domestic banks were quite prominent.In 2022,pandemic prevention and control,economic recovery,and structural upgrading will remain the main themes of China’s development.China’s financial risks are generally under control,but the country will still face major risks such as a high macro leverage ratio,tight market liquidity,increasing debt vulnerability,significant spillover effects,and rising volatility in the international market.

The Causes of Contagion： Rational or Irrational Behavior？

The threats concerning financial stability seriously affect the overall functioning of the economy at a local, regional, national, and continental level instead of a global level, and therefore, the emphasis is laid on analyzing the causes and effects of such threats. Financial crises in the current decade, as well as those in the past have shown that a major cause of instability in the global market is the so-called financial contagion. This leads to a natural question： whether similar authorities could specify and mitigate these shocks through efficient calculation followed by stability measures taken by banking networks. To answer this question, an empirical research was conducted by analyzing the degree of contagion induced by markets in Central and Eastern Europe, based on an econometric model, involving over 17 European countries, from January 2006 to January 2013.

Analysis on the Problems and Countermeasures of Enterprise Relationship Marketing

Under the current economic situation,companies should fully realize the importance of relationship marketing,establish good and stable relationships with all related parties,strengthen exchanges and cooperation with each other,achieve win-win benefits,and promote corporate marketing activities.For the success of the company,we will conduct in-depth investigations in the economic market,improve the products in time,and make plans based on the current development situation.