ABSTRACT
Based on the daily stock closing price data of 14 A-share listed banks in China from January 2009 to June 2021, this paper makes a comparative analysis of the contagion effect of risks in the banking industry before and after the outbreak of COVID-19. Based on the transfer entropy method, this paper calculates the correlation network matrix of inter-bank risk contagion effect and empirically studies the contagion effect of risks in the banking industry before and after the outbreak by using social network analysis method, depicting the network structure of systemic risk contagion in Chinese banking industry. This study found that the risk of inter-bank system increased significantly after the outbreak and the key nodes of bank risk contagion have also changed before and after the outbreak;state-owned banks are less risky, joint-stock banks and local financial institutions are riskier, and the contagion effect of risks between banks is asymmetric.
ABSTRACT
The paper investigates the volatility spillover across China's carbon emission trading (CET) markets using the connectedness method based on the quantile VAR framework. The non-linear result shows strong volatility spillover effects in upper quantiles, resulting from major economic and political events. This is in accordance with the risk contagion hypothesis that volatility of carbon price returns is affected by the shocks of economic fundamentals and spills over to other pilots. Guangdong and Shanghai are the most significant contributors to volatility transmission because of their high liquidity and active markets. Hubei CET pilot has shifted from transmitter to receiver since the COVID-19 pandemic. Regarding the pairwise directional connectedness, geographical location and similar market attribute also matter in volatility transmission. This provides implications for policymakers and investors to attach importance to risk management given the quantile-based method rather than the average shocks. © 2023 Elsevier B.V.
ABSTRACT
The paper investigates the volatility spillover across China's carbon emission trading (CET) markets using the connectedness method based on the quantile VAR framework. The non-linear result shows strong volatility spillover effects in upper quantiles, resulting from major economic and political events. This is in accordance with the risk contagion hypothesis that volatility of carbon price returns is affected by the shocks of economic fundamentals and spills over to other pilots. Guangdong and Shanghai are the most significant contributors to volatility transmission because of their high liquidity and active markets. Hubei CET pilot has shifted from transmitter to receiver since the COVID-19 pandemic. Regarding the pairwise directional connectedness, geographical location and similar market attribute also matter in volatility transmission. This provides implications for policymakers and investors to attach importance to risk management given the quantile-based method rather than the average shocks.
ABSTRACT
The evolution of an epidemic is conditioned by the health protection behaviors adopted by the population, which depends, in part, on their perception of the risk of contracting the disease. This study on the perception of the risk of COVID-19 infection in a sample of the Spanish population (N = 374) reveals that significant predictors of one’s perception of the risk of infection are personal experience with the disease, trust in those responsible for managing it, and personal attitudes towards the effectiveness of the protective measures established by the administration. The individual factors (personal experience, trust, knowledge…) and prosocial behavior (as opposed to individualism) were found to be the main elements related to the perception of risk and the performance of protection behaviors. © 2022 Centro de Estudos Sociais da Universidade de Coimbra. All rights reserved.
ABSTRACT
The purpose of this paper is to study the spillover effects of financial stress among five important financial markets (bond, stock, foreign exchange, interbank, and real estate markets) in China, and explore the important determinants of financial stress spillover level among the markets and the impact of the Chinese stress spillover situation on the European markets. Our findings are as follows: First, there is a significant stress spillover effect among the five markets, and the total financial stress spillover index (TSSI) is very high during the global financial crisis. Generally, the stock and real estate markets are the major transmitters of stress spillover, and the interbank and bond markets are the major receivers. Second, the most macro factors have significant impacts on the financial stress spillover level among the markets, especially CPI index, the Chinese economic policy uncertainty index and VIX index. And the severity of the COVID-19 epidemic in China and the world has a significant impact on the TSSI, especially from March 2020 to August 2020. Finally, the TSSI can significantly increase the volatility of French stock market, Italian stock market and German government bond market, especially during the Sino-US trade war and the COVID-19 epidemic.
ABSTRACT
As the COVID-19 outbreak has an impact on the global economy, there will be interest in how China's financial markets function during the outbreak. To investigate the path of risk contagion in China's financial sub-markets before and after the COVID-19 outbreak, we divided the 2016-2021 period into two phases. Based on the time of the COVID-19 outbreak, we divided the new stage of economic development into pre-epidemic and post-epidemic stages and employed the DCC-GARCH model to investigate the dynamic correlation coefficients among the financial sub-markets in China. Furthermore, we employed complex network theory and the minimum tree model to describe the risk contagion path between two-stage Chinese financial submarkets. Finally, we provided pertinent recommendations for investors and policymakers and conducted a brief discussion based on the findings of the research.
ABSTRACT
With the significant increase of uncertainties facing the global economy, systemic risks in China and international financial markets occur frequently, seriously restricting the stable development of financial markets and the smooth operation of the economy. In the context of economic globalization and the integration of market economy, the increased correlation between markets leads to the enhancement of risk resonance effect, which leads to the contagion of risks among different markets, among which China is particularly affected by the fluctuation effect of American market risks. In view of this, this paper uses the BEKK-GARCH (1, 1) model to explore the risk contagion effect between China and the United States during the period of January 2, 2001 solstice and March 19, 2021. The results show that, first, in the full sample interval, there is a two-way volatility spillover effect between the Chinese market and the American market and there is a Granger causality. The degree of volatility spillover from American to China is more significant. Second, in the four crisis intervals, the volatility spillover effects of China and the US show some characteristics different from those of the full sample interval. During the European debt crisis, there was a positive volatility spillover effect between the two markets, and the spillover degree of systemic risks in China's market to the US market was more significant. During COVID-19, the US market risks had negative volatility spillover to the Chinese market. Third, both the Chinese and American markets are affected by their own fluctuations in the early stage with relatively large impacts and short duration, while the impact of the US on China lasts for about 7 periods. © 2021 IEEE.
ABSTRACT
Purpose COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network. Design/methodology/approach Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs. Findings The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node;however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed. Originality/value It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.
ABSTRACT
The novel coronavirus disease (COVID-19) is one of the worst pandemics in human history. Our research objective is to assess the contagion effect on Japanese firms and to evaluate the Japanese government's COVID-19 measures during the period from April 7, 2020, to May 25, 2020. We propose a susceptible-infected-recovered-dead model for COVID-19 and derive COVID-19 parameters for Japan. Subsequently, we analyze the effect of COVID-19 on Japanese firms through correlation-based network and credit risk analyses. The main findings are that the Tokyo Stock Price Index moved in the opposite direction of COVID-19 parameters and COVID-19 parameters are almost the only risk factors that impact a firm's credit risk during the period. Finally, we find that the interconnection analysis between the COVID-19 infection network and the financial networks contribute to the existing pandemic risk management knowledge.
ABSTRACT
This paper examines the risk contagion among international stock markets during the COVID-19 pandemic by using the realized volatility information from sixteen major stock markets in the world. The empirical evidence based on the connectedness methods of Diebold and Yilmaz (2012) and Baruník and Krehlík (2018) shows that the COVID-19 epidemic significantly increases the risk contagion effects in international stock markets. Besides, the risk spillovers from stock markets in European and American regions increase rapidly but those in Asian markets decrease obviously after the outbreak of COVID-19 pandemic. Finally, the risk contagion among international stock markets caused by the pandemic can last for about 6 to 8 months. These results provide important implications regarding to financial risk management and macroprudential design.
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We use the cutting-edge causal forest algorithm to analyze the heterogeneous treatment effects of the COVID-19 outbreak on China's industry indexes. The variable importance index is used with the causal forest and complex network methods to analyze the characteristics of industrial relations and the types of industry risk contagion before and after the COVID-19 outbreak. The results show that the heterogeneity of industries was significantly weakened during the COVID-19 outbreak. In addition, the COVID-19 outbreak changed the original structure of the industry-related network, which shifted to a star network structure with leisure services at the core. It also changed the type of risk contagion between industries, from the original middleman risk type to the input risk type.