Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

This paper examines the presence of a contagion effect between Chinese and G20 stock markets as well as its intensity over a recent period from 1(st) January 2013 to 7 April 2022. The empirical study is conducted using the time-varying copula approach. The obtained results show strong evidence of a contagion effect between China and all countries except United States America, Argentina and Turkey during the COVID-19 period. In particular, the Chinese stock market exhibits the highest level of dependence with the Asian and European stock markets in addition to the greatest variability in dependence. These findings are interesting and have important implications for several financial applications.

Emerging and advanced economies markets behaviour during the COVID‐19 crisis era

This article examines the consequences of the COVID‐19 crisis on the interdependencies between emerging and advanced economies. Using daily market index data from 22 developed and emerging markets, we develop a combination of statistical methods based on Diebold and Yilmaz spillover index and Toda–Yamamoto and Dolado and Lütkepohl causality approach. The results substantiate an increase in the interdependence between emerging and advances economies, which suggests an increase in the transmission of the stress and uncertainty between financial markets during the pandemic period. Our findings show that the emerging countries are affected by the financial markets of advanced economies during the COVID‐19 crisis and, in particular, by European markets, which appear to be the primary driver of contagion and transmission of stress and uncertainty to all other regional markets.

Examining contagion effects between global crude oil prices and the Southeast Asian stock markets during the COVID-19 pandemic

Many previous studies identify the contagion effect among various types of assets, defined as the increase in correlation of these assets during a financial or economic crisis. During the COVID-19 outbreak, a historic fall in global fuel demand and oil prices has been witnessed. Because crude oil has a strategic position among the export products of the Southeast Asian economies, even a tiny global oil price change leads to a plunge in these stock markets. This study addresses the spillovers of the volatility between the West Texas Intermediate crude oil prices and stock indices across six ASEAN emerging economies. Besides, the study examines whether a contagion connecting the global energy prices and these stock markets exists during the coronavirus pandemic. The empirical results are acquired by applying the Bayesian test for equality of means on the dynamic conditional correlations computed from DCC-GARCH models. The findings present positive volatility transmission from crude oil prices toward these emerging equity markets. During the health crisis, co-movements intensify, indicating the occurrence of contagion effects. The empirical results provide valid implications for policymakers and international investors because a precise volatility forecast is vital for managing portfolio risk. © Mien Thi Ngoc Nguyen, 2023.

The bubble contagion effect between crude oil and oil-exporting stock markets: the case of GCC countries

Purpose: This study aims to investigate the existence of bubbles and their contagion effect in crude oil and stock markets of oil-exporting countries Gulf Cooperation Council (GCC) from 2016 to 2021. Design/methodology/approach: The authors use Generalized Sup augmented Dickey–Fuller (GSADF) and Backward Sup augmented Dickey–Fuller (BSADF) to significantly identify multiple bubbles stock and oil markets with precise dates. Furthermore, the authors check the contagion effect of bubbles between crude oil and GCC stock markets based on the time-varying Granger causality test. Findings: First, the authors find empirical evidence of downwards bubbles in crude oil prices and in all GCC stock indexes (except the Saudi stock index) during the corona virus disease 2019 (COVID-19) outbreak. Second, the authors do not detect empirical evidence of bubble transmission between crude oil markets and GCC stock markets (except with the Dubai Financial Market index). Practical implications: The findings of this study would illuminate policymakers not to limit the factors of systematic financial crises in oil-exporting countries to crude oil and to consider factors such as monetary policy and economic diversification measures. This study has also crucial implications for investors. In fact, investors should not ignore the responses of the stock markets to oil price shocks that are heterogeneous across countries when looking for investment opportunities in the GCC region. Originality/value: The study justifies the changing nature of the bubble contagion effect through the novel implementation of the time-varying Granger causality test to detect whether bubble contagion exists between oil and GCC stock markets and if that does, in which direction. © 2023, Emerald Publishing Limited.

Impact of COVID-19 on the contagion effect of risks in the banking industry: based on transfer entropy and social network analysis method

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.

Bubble contagion effect between the main precious metals

Purpose: This study examines the extent to which gold and silver bubbles are correlated and which metal's bubble spills over to the other. In addition, the overlap in bubble-like episodes for the two metals is demonstrated and the influence of crises (global financial crises, European debt crisis and the COVID-19 pandemic) on the development of these episodes is compared. Design/methodology/approach: This study proposes a two-step approach. In the first step, price bubbles are identified based on the backward sup augmented Dickey–Fuller of Phillips et al. (2015a, 2015b) and modified by Phillips and Shi (2018). In the second step, the correlation in the contagion effect of the bubbles between the two precious metal prices is measured using a nonparametric regression with a time-varying coefficient approach developed by Greenaway-McGrevy and Phillips (2016). Findings: The findings suggest that the safe-haven property of gold and silver during financial market turbulence induces excessive price increases beyond their fundamental values. Furthermore, the results indicate that bubbles are contagious among precious metal markets and flow mainly from gold to silver;these findings are associated with the period after 2005, particularly during the global financial crisis. A contagious bubble effect is not found between gold and silver during the coronavirus disease 2020 pandemic. Practical implications: The results suggest that financial market participants should consider portfolio weights in precious markets in light of the bubble correlation between gold and silver, especially during crises. Originality/value: To the best of the authors' knowledge, this is the first study that explores the correlation of bubble-like episodes between gold and silver. © 2022, Emerald Publishing Limited.

Exploring the Contagion Effect of Social Media on Mass Shootings

Social media plays a prominent role in the spread of mass shootings. It brought about a significant contagious effect on future similar incidents. Therefore, we explore Machine Learning (ML) models to forecast the change in the public’s attitudes about mass shootings on social media over time. These ML models include Support Vector Machine (SVM), Logistic Regression (LR), and the optimized Deep Neural Networks based on an Improved Particle Swarm Optimization algorithm (IPSO-DNN). We then propose a self-excited contagion model to predict the number of mass shootings by focusing on the spread of public attitudes on Twitter. Moreover, we also improve the proposed contagion model with the consideration of social distancing and the daily growth rate of COVID-19 cases, to predict and analyze mass shootings under the COVID-19 pandemic. Experimental results demonstrate that the proposed contagion models perform very well in predicting future mass shootings in the United States.

Time-varying pure contagion effect between energy and nonenergy commodity markets

This paper combines the Kalman filtering technique and the time-varying parameter vector autoregression model with stochastic volatility model to explore pure contagion effects between energy and nonenergy (i.e., industrial metals, precious metals, and agricultural) commodity markets. Empirical results show the significant pure contagion effects between energy and industrial metals markets in most periods, while pure contagion effects between energy and precious metals and agricultural markets occur only in a few specific periods. Comparing the level of pure contagion effects between different commodity markets, energy is still the main price transmitter. In addition, with the acceleration of the global commodity market financialization process, the frequency and harm of pure contagion effects are gradually increasing. Notably, the COVID-19 pandemic is emerging as another major crisis after the global financial crisis, exacerbating the pure contagion effects between energy and precious metals and agricultural markets. © 2022 Wiley Periodicals LLC.

Financial contagion effects of major crises in African stock markets

This study examines financial contagion effects in African stock markets during major crises over the period 2005 to 2020. We investigate contagion effects in individual stock markets and from a regional perspective using dynamic conditional correlations during the global financial crisis, European debt crisis, Brexit, and COVID-19. The empirical evidence confirms contagion effects in some individual markets. However, significant evidence of contagion is found only during the global financial crisis from the regional perspective. Our findings suggest that the regional impacts of crises differ due to the nature of those crises. We also find financial contagion increases in the country-level risk, market capitalization and export to GDP and decreases in corruption.

Bubble contagion effect between the main precious metals

Purpose: This study examines the extent to which gold and silver bubbles are correlated and which metal’s bubble spills over to the other. In addition, the overlap in bubble-like episodes for the two metals is demonstrated and the influence of crises (global financial crises, European debt crisis and the COVID-19 pandemic) on the development of these episodes is compared. Design/methodology/approach: This study proposes a two-step approach. In the first step, price bubbles are identified based on the backward sup augmented Dickey–Fuller of Phillips et al. (2015a, 2015b) and modified by Phillips and Shi (2018). In the second step, the correlation in the contagion effect of the bubbles between the two precious metal prices is measured using a nonparametric regression with a time-varying coefficient approach developed by Greenaway-McGrevy and Phillips (2016). Findings: The findings suggest that the safe-haven property of gold and silver during financial market turbulence induces excessive price increases beyond their fundamental values. Furthermore, the results indicate that bubbles are contagious among precious metal markets and flow mainly from gold to silver;these findings are associated with the period after 2005, particularly during the global financial crisis. A contagious bubble effect is not found between gold and silver during the coronavirus disease 2020 pandemic. Practical implications: The results suggest that financial market participants should consider portfolio weights in precious markets in light of the bubble correlation between gold and silver, especially during crises. Originality/value: To the best of the authors’ knowledge, this is the first study that explores the correlation of bubble-like episodes between gold and silver. © 2022, Emerald Publishing Limited.

NetVIX - A network volatility index of financial markets

We construct a network volatility index (NetVIX) via market interconnectedness and volatilities to measure global market risk. The NetVIX multiplicatively decomposes into a network volatility effect and a network contagion effect. It also additively decomposes into volatility contributions of each market. We apply our measure to study the relationship between the interconnectedness among 20 major stock markets and global market risks over the last two decades. We show that the NetVIX has a strong relationship with the VIX index, and therefore able to reliably signal changes in global market volatility. We also show that while the NetVIX tracks to some extent the VIX, it provides much more information about the level of volatility and contagion effects in financial markets. The result shows that during crisis periods, particularly the tech bubble, the global financial crisis, and the Covid-19 pandemic, stock market interconnectedness contributes to global market turmoil by amplifying average market volatility with over 400 percent multiplier. Also during crisis times, the level of risk is relatively higher and more persistent in the US and German markets, which implies market losses for investors with long exposures. The results also reveal that the highest risk-contributing markets are the US, Brazil, Hong Kong, France, and Germany.

Quality-Adjusted Life-Year Losses Averted With Every COVID-19 Infection Prevented in the United States.

OBJECTIVE: To estimate the overall quality-adjusted life-years (QALYs) gained by averting 1 coronavirus disease 2019 (COVID-19) infection over the duration of the pandemic. METHODS: A cohort-based probabilistic simulation model, informed by the latest epidemiological estimates on COVID-19 in the United States provided by the Centers for Disease Control and Prevention and literature review. Heterogeneity of parameter values across age group was accounted for. The main outcome studied was QALYs for the infected patient, patient's family members, and the contagion effect of the infected patient over the duration of the pandemic. RESULTS: Averting a COVID-19 infection in a representative US resident will generate an additional 0.061 (0.016-0.129) QALYs (for the patient: 0.055, 95% confidence interval [CI] 0.014-0.115; for the patient's family members: 0.006, 95% CI 0.002-0.015). Accounting for the contagion effect of this infection, and assuming that an effective vaccine will be available in 3 months, the total QALYs gains from averting 1 single infection is 1.51 (95% CI 0.28-4.37) accrued to patients and their family members affected by the index infection and its sequelae. These results were robust to most parameter values and were most influenced by effective reproduction number, probability of death outside the hospital, the time-varying hazard rates of hospitalization, and death in critical care. CONCLUSION: Our findings suggest that the health benefits of averting 1 COVID-19 infection in the United States are substantial. Efforts to curb infections must weigh the costs against these benefits.

A COVID-19 forecasting system using adaptive neuro-fuzzy inference.

This article proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) to forecast the number of COVID-19 cases in the United Kingdom. With the combination of artificial neural network and fuzzy logic structure, the model is trained based on collected data. The study examines various factors of ANFIS to come up with an effective time series prediction model. The result indicates that Spain and Italy data can strengthen the predictive power of COVID-19 cases in the UK. It is suggested that the policymakers should adopt Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict contagion effect during the COVID-19 pandemic.