Risk Of Contagion Research Articles

Dynamic spillover and systemic importance analysis of global clean energy companies: A tail risk network perspective

This paper conducts tail risk spillover and systemic importance analysis based on the firm-level data of global clean energy system. The tail risk network is constructed based on the ΔCoVaR method, where the correlation is calculated from three perspectives: market, sector and institution. Moreover, the systematically important institutions are identified through a PageRank-based approach. We find that two industries contribute the largest in the risk transmission process, and most of the identified important institutions come from these industries as well. Also, it is observed that the COVID-19 pandemic and the Russia–Ukraine conflict would render different impacts on the risk contagion.

Time-Frequency Volatility Spillovers among Major International Financial Markets: Perspective from Global Extreme Events

In the context of the gradual intensification of the Russia-Ukraine conflict and the continuous spread of the COVID-19 pandemic, this paper concentrates on the impact of global extreme events such as the COVID-19 pandemic and the Russia-Ukraine conflict on the risk spillovers among major international financial markets. First, to measure the impact of the extreme events the on the volatility spillovers among major international financial markets in the time-frequency domain, we combine the TVP-VAR-based connectedness method and BK frequency connectedness approach and focus on the total, directional, and net volatility spillovers. Second, the network visualization method is applied to outline the structural change in the risk contagion, paths, and roles among international financial markets during different periods of global extreme events. The empirical results indicate that the risk spillovers (total, directional, and net spillovers) among international financial markets and the roles played by each market in the process of risk contagion have changed significantly in different periods of global extreme events. Furthermore, volatility spillovers among international financial markets are driven mainly by the high-frequency component (short-term spillovers) during the full sample time. However, the effects of the extreme events also persist in the medium and long terms. Our findings may help understand the dynamics among international financial markets under extreme shocks and provide significant implications for portfolio managers, investors, and government agencies in times of extreme events.

Exploration of industrial risk contagion characteristics and mechanism under geopolitical events: evidence from China

ABSTRACT Based on the TVP-VAR-DY and TVP-VAR-BK models, this article examines the characteristics and mechanisms of systemic risk contagion in the Chinese industries under geopolitical events by selecting data spans from 1 January 2010 to 31 August 2022. First, dynamic analysis of full-sample risk contagion shows that there is a significant climb in total risk during geopolitical events. Then the static analysis of risk contagion in the full sample specifically shows the correlation between risk contagion and industry chain between the financial and real sectors. Besides, the sub-sample analysis illustrates that during geopolitical events such as the Sino-US Trade War, the COVID-19 Pandemic and the Russia-Ukraine Conflict, Chinese industrial stock indexes show short-term risk spillovers from key industries related to geopolitical events, and gradually spread along the industrial chain in the long run compared to the Chinese ‘Stock Market Crash’. Through further mechanistic tests, we find that the irrational behaviour of investors in the market exacerbates short-term risk contagion, while the financial distress of real firms due to financing constraints exacerbates long-term risk contagion. In addition, geopolitical risk, economic uncertainty, and policy uncertainty as macro variables also have an impact on the short-run and long-run risk contagion.

Customers’ litigation risk and suppliers’ cash holding decision: From the perspective of risk contagion

This study investigates the contagion effect of litigation risk through supply chain. We find strong evidence that customers’ litigation risk increases suppliers’ cash holding, and this contagion effect only exists when customers get involved in lawsuits as defendant. We further find that customers’ litigation risk reduces trade credit for customers, increases financing cost and operating risk for both customers and suppliers, which are underlying channels. Heterogeneity analysis shows that the risk contagion effect is more significant in samples with short supplier-customer geographic distance, private suppliers, and suppliers with strong bargaining power.

Computational mathematics applied in the identification of the risk of indirect contagion of COVID-19 in university classrooms

This exploratory study evaluated the risk of contagion from airborne diseases, such as coronaviruses, in schools. For three days, the concentration of carbon dioxide in two university classrooms was monitored for 90 minutes, while the students took their math classes. We use these values to validate a first-order model for carbon dioxide concentration and calculate the air exchange rate indirectly (avoiding the need for expensive measurement equipment). The air exchange rate obtained allowed us to assess whether the usual ventilation systems (both natural and mechanical) are sufficient to guarantee a low risk of contagion of aerosols due to respiration. The results show that the risk of contagion is low if three factors are considered: the level of conversation within the classroom, the usage of a moisture extraction system, and the lecture duration. The risk is low if the lecture time is less than 50 minutes, the level of conversation is moderate, and a moisture extraction system is available. If these conditions are not met the risk is considerably higher even if mechanical ventilation is employed.

Mutual coupling between stock market and cryptocurrencies

We examine the relationship between the top five cryptos and the U.S. S&P500 index from January 2018 to December 2021. We use the novel General-to-specific Vector Autoregression (GETS VAR) and traditional Vector Autoregression (VAR) model to analyze the short- and long-run, cumulative impulse-response, and Granger causality test between S&P500 returns and the returns of Bitcoin, Ethereum, Ripple, Binance and Tether. Additionally, we used the Diebold and Yilmaz (DY) spillover index of variance decomposition to validate our findings. Evidence from the analysis suggests positive short- and long-run effects of historical S&P500 returns on Bitcoin, Ethereum, Ripple, and Tether returns––and negative short- and long-run effects of the historical returns of Bitcoin, Ethereum, Ripple, Binance, and Tether on S&P500 returns. Alternatively, evidence suggests a negative short- and long-run effect of historical S&P500 returns on Binance returns. The cumulative test of impulse-response indicates a shock in historical S&P500 returns stimulates a positive response from cryptocurrency returns while a shock in historical crypto returns triggers a negative response from S&P500 returns. Empirical evidence of bi-directional causality between S&P500 returns and crypto returns suggest the mutual coupling of these market. Although, S&P500 returns have high-intensity spillover effects on crypto returns than crypto returns have on S&P500. This contradicts the fundamental attribute of cryptocurrencies for hedging and diversification of assets to reduce risk exposure. Our findings demonstrate the need to monitor and implement appropriate regulatory policies in the crypto market to mitigate the potential risks of financial contagion.

Exposure to risk communication, compliance with preventive measures and information-sharing behavior among students during the COVID-19 pandemic

PurposeDuring the coronavirus disease 2019 (COVID-19) pandemic, public health risk communication has been a vital work. Students account for a large proportion of the population and are often highly mobile; thus, they face a high risk of contagion and spreading the disease. Therefore, risk communication to this group during the pandemic has been essential. This research examines the relationship between risk communication to students and their appropriate behaviors (compliance with COVID-19 preventive measures and COVID-19 information sharing).Design/methodology/approachThis research used structural equation model (SEM) and generalized structural equation model (GSEM) to analyze the data collected from students during the COVID-19 pandemic.FindingsThe findings revealed that risk communication in mass media was positively associated with students' compliance with preventive measures and sharing pandemic-related information. Additionally, their compliance behavior was positively associated with their information-sharing behavior.Practical implicationsThis research offers some implications regarding the containment of a highly-infectious virus, especially for the context when the risk of outbreak is high and an effective vaccine is not available, by focusing on risk communication and compliance and information-sharing behaviors.Originality/valueThis research is one of the early attempts to examine the risk communication to students, their compliance with preventive measures and their information-sharing behavior during a pandemic.

An agent-based model of supply chain financing: agents’ strategies, performance, and risk contagions

ABSTRACT Supply chain finance (SCF), which has gained prominent attention from academicians, is a complex process involving various participants. However, previous studies have not provided a comprehensive framework integrating these participants or a consensus on how participants’ strategies influence SCF. Here, a comprehensive framework is proposed to investigate SCF through an agent-based approach. We chose the Chinese market to proxy key model parameters. We find that although banks prefer large businesses, trade credit, and partial credit guarantee still play a pair of main complementary financing channels for small and medium enterprises (SMEs). The cautious strategies of agents indeed significantly decrease the risk contagions in the supply chain. However, marketisation negatively moderates this effect for SMEs. From the banks’ perspective, although longer-term credit policies bring lower risk contagions, they increase the uncertainty of the banks’ risk. In addition, our extended framework also shows that the market-oriented financial system benefits either large business or SMEs as well as smooths the risk fluctuation for banks. This study not only verifies some economic phenomena but also provides a new systematic angle for understanding SCF. We also discuss in detail this study’ important practical insights and theoretical contribution.

Modeling a traffic light warning system for acute respiratory infections

The high morbidity of acute respiratory infections constitutes a crucial global health burden. In particular, for SARS-CoV-2, non-pharmaceutical intervention geared to enforce social distancing policies, vaccination, and treatments will remain an essential part of public health policies to mitigate and control disease outbreaks. However, the implementation of mitigation measures directed to increase social distancing when the risk of contagion is a complex enterprise because of the impact of NPI on beliefs, political views, economic issues, and, in general, public perception. The way of implementing these mitigation policies studied in this work is the so-called traffic-light monitoring system that attempts to regulate the application of measures that include restrictions on mobility and the size of meetings, among other non-pharmaceutical strategies. Balanced enforcement and relaxation of measures guided through a traffic-light system that considers public risk perception and economic costs may improve the public health benefit of the policies while reducing their cost. We derive a model for the epidemiological traffic-light policies based on the best response for trigger measures driven by the risk perception of people, instantaneous reproduction number, and the prevalence of a hypothetical acute respiratory infection. With numerical experiments, we evaluate and identify the role of appreciation from a hypothetical controller that could opt for protocols aligned with the cost due to the burden of the underlying disease and the economic cost of implementing measures. As the world faces new acute respiratory outbreaks, our results provide a methodology to evaluate and develop traffic light policies resulting from a delicate balance between health benefits and economic implications.

An adaptive endogenous network model for dynamic interbank lending market with central bank

In this paper, an adaptive endogenous model is introduced to explain the formation and evolution mechanism of interbank lending market with central bank. We investigate the evolution of structure of interbank lending market and the impact of structure of bank assets, bank adaptive behaviors, liquidity and dispersion on the resilience of interbank lending market, respectively. Simulation results demonstrate that the structure of the initial interbank lending market has scale-free and small-world features, which keeps in the evolution of the network. Moreover, we find risk gathering requires relatively long time but the outbreak is concentrated and percentage of defaults is a nonmonotonic function of percentage of initial liquid assets. Furthermore, we discover that moderate liquidity hoarding helps to reduce risk contagion effect in the initial stage of contagion while excessive hoarding of liquidity increases the fragility of the network.

On the employment and health impact of the COVID-19 shock on Italian regions: a value chain approach

ABSTRACT We evaluate the exposure of Italian regions to employment and the health risk associated with the spread of COVID-19. First, we estimate the degree of participation of Italian regions in a plurality of value chains linked to consumption, investment and exports. Second, we investigate the different levels of contagion risk associated with each value chain and the possibility of reducing such risk through remote work. We find that regions are affected differently by lockdown policies because of their highly heterogeneous embeddedness in different value chains, and their diverse sectoral contributions to each of them.

GENDER DIFFERENCES IN BELIEVABILITY AND USEFULNESS OF COVID-19 RELATED INFORMATION ON SOCIAL MEDIA

Abstract: COVID-19 is one of the most threatening pandemics in recent times. People worldwide became terrified because of the risk of contagion, the high rate of fatality, particularly among the elderly, and the effects on people's social, economic, political, and cultural lives. Despite the risk associated with the Pandemic, the Nigerian populaces have different attitudes regarding the usefulness of information and believability, especially on social media. This study examines gender differences in Covid-19 attitudes towards related information on social media among undergraduate students of Ahmadu Bello University Zaria. The study employed a qualitative approach, and interviews were used to collect data. Purposive sampling techniques were used in selecting respondents. The participants consist of six female students and six male students. Overall, twelve students were selected to respond to the interview questions. Purposive sampling techniques were used in selecting respondents. The data were analyzed using thematic analysis. The findings showed an insignificant gender difference in Covid-19 attitudes and beliefs among students. The result further indicated that females do not value Covid-19 related information posted on social media by individuals. 
 Keywords: Attitude; COVID-19; Disinformation; Fake News; Gender; Nigeria; Social Media

Systemic risk prevention policies targeting systemically important banks: Does clustering pattern matter?

It is well known that strengthening the soundness of individual banks that are too large or too interconnected is essential for maintaining financial stability. The clustering among homogeneous banks may also cause financial fragility but has received less attention. This paper discusses the policy improvement for preventing systemic risk from the perspective of the clustering pattern of systemically important banks (SIBs) based on a network optimization model. The results show that the clustering pattern of SIBs is closely related to systemic risk contagion. Remarkably, networks with fewer connections among SIBs exhibit lower systemic risk than those featuring apparent clustering of SIBs. The potential mechanism is that the systemic vulnerability of small and medium-sized banks is greatly reduced in the disassortative networks. The proposed tools based on this-inter-SIBs exposure limits and pairwise capital requirements-can guide network optimization and significantly reduce systemic risk. Furthermore, combining existing capital surcharges for SIBs (focusing on the stability of individual SIBs) and proposed network-based tools (focusing on the cluster structure of the network) will be an effective way to enhance financial stability over existing policies.

Time-frequency connectedness among traditional/new energy, green finance, and ESG in pre- and post-Russia-Ukraine war periods

This paper examines how the continuing war between Russia and Ukraine affects the transmission of returns spillovers among traditional/new energy, green finance, and ESG. We evaluate the dynamic and directional risk connectedness across time and at various frequency bands using Diebold and Yilmaz and Baruník and Křehlík approaches. First, there is a noticeable rise in overall connectedness, from 19.36% to 29.99%, and it is more pronounced in the short time. Second, new energy, ESG, and green stocks are closely linked. Third, new energy, ESG, and green stock are main risk transmitters functioning in the long- and short-term respectively after the war. Fourth, the oil market becomes more vulnerable to other markets, changing from a net transmitter to a net receiver. These findings can offer important empirical implications for governments and market participants to copy with the contagion risk during the Russia-Ukraine war.

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from “hydropower–gold” to “smartgrid–zinc”, and the systematically influential markets correspondingly become smartgrid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits.

Research on tail risk contagion in international energy markets—The quantile time-frequency volatility spillover perspective

From the perspective of volatility spillover network, this paper constructs the spillover index based on the quantile vector autoregression (QVAR) model to capture tail risk spillover effects in international energy markets under different shock sizes and frequency domains. Using the traditional and clean energy markets data covering 2010 to 2022, the empirical results show that the spillover index based on the QVAR model can better capture the tail risk spillover effects with different shock sizes, while the conditional mean-based spillover index may underestimate or misjudge the true level of tail risk spillover among markets. In the extreme state, the tail risk spillover effect among energy markets is significantly enhanced compared to the normal state. From the perspective of frequency domain, it is found that the total tail risk spillovers among international energy markets are dominated by long-term risk spillovers. At the same time, the asymmetry of tail risk spillover effect is more remarkable in the extreme state in the long-term. In addition, the traditional energy markets are the main sources of tail risk spillover, and mainly play the role of net risk spillover. In contrast, the fuel cell, geothermal energy and solar energy markets in the clean energy market have greater net spillover-in effect and play the net receiver role in the interconnectedness network.

Psychosocial effects of COVID-19 in Ecuadorian university professors

The objective of this work is to analyze the psychosocial effects of COVID-19 on Ecuadorian university professors, for which a quantitative research of descriptive and correlational nature was developed. Two instruments were applied, based on the Depression, Anxiety and Stress Scales and the Burnout questionnaire to the selected sample, in three public universities of easy access and linkage with the researchers. The population consisted of professors with appointment: 682 from the Technical University of Manabí, 678 from the Lay University Eloy Alfaro de Manabí, and 131 from the State University of Southern Manabí, from which a sample of 246, 246 and 98 professors was selected respectively. From the results it is derived that depression, anxiety, and stress were configured in psychosocial effects, which have had an impact during the COVID-19 pandemic on the professors of the universities studied, due to the pressure originated by new emerging methodological structures, the acquisition of other competences associated to technology, health and the risks of contagion, death and by the pedagogical attention through virtuality, unexpectedly arisen in times of pandemic.

Systemic risk contagion of green and Islamic markets with conventional markets.

Financial markets are exposed to extreme uncertain circumstances escalating their tail risk. Sustainable, religious, and conventional markets represent three different markets with various characteristics. Motivated with this, the current study measures the tail connectedness between sustainable, religious, and conventional investments by employing a neural network quantile regression approach from December 1, 2008 to May 10, 2021. The neural network recognized religious and conventional investments with maximum exposure to tail risk following the crisis periods reflecting strong diversification benefits of sustainable assets. The Systematic Network Risk Index spots Global Financial Crisis, European Debt Crisis, and COVID-19 pandemic as intensive events yielding high tail risk. The Systematic Fragility Index ranks the stock market in the pre-COVID period and Islamic stocks during the COVID sample as the most susceptible markets. Conversely, the Systematic Hazard Index nominates Islamic stocks as the chief risk contributor in the system. Given these, we portray various implications for policymakers, regulatory bodies, investors, financial market participants, and portfolio managers to diversify their risk using sustainable/green investments.

Psychological Distress During COVID-19 Confinement in Persons After Metabolic Surgery

Background: People suffering from obesity often experience distress and psychopathological symptoms that decrease after undergoing metabolic surgery; the confinement caused by the COVID-19 pandemic impacted the whole population in this regard. Given that individuals undergoing metabolic surgery are at increased risk of developing these disorders, it is important to identify them in order to prevent or treat them appropriately. Therefore, this study aims to determine the relationship between COVID-19-related psychological distress and psychopathological symptoms in people who underwent metabolic surgery. Methods: A cross-sectional study was conducted including 102 participants more than six months after undergoing metabolic surgery. Sociodemographic information was collected, as well as psychological distress related to COVID-19 and psychopathological symptoms measured using the SCL-90R. A Structural Equation Model was developed to evaluate the effects and correlation between variables. Results: 90.2% of the participants were women, 84% had undergone gastric sleeve surgery while the rest had undergone Roux-en-Y gastric bypass surgery. The model obtained showed a significant correlation between the subscales of the SCL 90-R and COVID-19 related psychological distress and both were negatively correlated with the age of the participants. The COVID-19-related psychological distress factor had a significant effect on fear of contagion, perception and knowledge of risk of contagion, with risk perception being the most explained with 95.8% of variance explained. In addition, the final model showed adequate goodness-of-fit indicators. Conclusions: Psychological distress caused by pandemic and confinement is evidenced by higher scores on the SCL-90R instrument in persons with metabolic surgery. However, further studies and psychometric testing with more homogeneous samples in terms of sex and surgical technique are required.

Cardiovascular risk and the COVID-19 pandemic: A retrospective observational study in a population of healthcare professionals

Background and aimTo contain the spread of COVID-19, many countries imposed several restrictive measures, leading to radical changes in daily life behaviors. Healthcare workers experienced additional stress due to the increased risk of contagion, possibly causing an increase in unhealthy habits. We investigated changes in cardiovascular (CV) risk assessed by the SCORE-2 in a healthy population of healthcare workers during the COVID-19 pandemic; an analysis by subgroups was also conducted (sportspeople vs sedentary subjects). Methods and resultsWe compared medical examination and blood tests in a population of 264 workers aged over 40, performed yearly before (T0) and during the pandemic (T1, T2). We found a significant increase in the average CV risk, according to SCORE-2, during the follow-up in our healthy population, with a shift from a mean low-moderate risk profile at T0 (2.35%) to a mean high-risk profile at T2 (2.80%). Furthermore, in sedentary subjects was observed a greater and early increase in SCORE-2 compared to sportspeople. ConclusionsSince 2019, we observed an increase in CV risk profile in a healthy population of healthcare workers, particularly in sedentary subjects, highlighting the need to reassess SCORE-2 every year to promptly treat high-risk subjects, according to the latest Guidelines.