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.

COVID-19 and information flow between cryptocurrencies, and conventional financial assets.

In this paper, we analyze the impact of the ongoing COVID-19 pandemic on the information flow among the main cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin) and those of the fear index (VIX), Gold price, and the US equity market (S&P500). We use the transfer entropy measure to determine the information flow by allowing for nonlinear dynamics and extreme tail values in the series. Our results indicate that information flow and sharing have changed during the COVID-19 pandemic with the following main findings: i) cryptocurrencies show more correlation with VIX, Gold, and the US equity markets during the COVID-19 period; ii) Gold and VIX maintain their position as safe hedging tools against the pandemic; iii) during COVID-19, S&P500 is the dominant flow transmitter to the four cryptocurrencies, and iv) Ripple plays the dominant role of information flow to VIX, Gold, and S&P500.

COVID-19 Effects on the Relationship between Cryptocurrencies: Can It Be Contagion? Insights from Econophysics Approaches.

Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a "black swan", is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak.

Transfer Entropy Granger Causality between News Indices and Stock Markets in U.S. and Latin America during the COVID-19 Pandemic.

The relationship between three different groups of COVID-19 news series and stock market volatility for several Latin American countries and the U.S. are analyzed. To confirm the relationship between these series, a maximal overlap discrete wavelet transform (MODWT) was applied to determine the specific periods wherein each pair of series is significantly correlated. To determine if the news series cause Latin American stock markets' volatility, a one-sided Granger causality test based on transfer entropy (GC-TE) was applied. The results confirm that the U.S. and Latin American stock markets react differently to COVID-19 news. Some of the most statistically significant results were obtained from the reporting case index (RCI), A-COVID index, and uncertainty index, in that order, which are statistically significant for the majority of Latin American stock markets. Altogether, the results suggest these COVID-19 news indices could be used to forecast stock market volatility in the U.S. and Latin America.

Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches

Motivated by the severe impacts of the Covid 19 outbreak on the global trade and capital flows, which can shift the forex market structure, this paper aims to examine the equicorrelation and causal association across major currency markets during Covid 19 pandemic using novel approaches: DECO-GARCH and Transfer Entropy. We find that major exchange rate markets have a positive equicorrelation, and these trends have been more pronounced during the Covid-19 crisis, uncovering the existence of contagion effects. The results also show the causal associations between the currency markets, depicted by three categories: no effect, mono-direction, and bi-direction. Such connections unveil the shock sender and receiver in the examined exchange rate markets, supporting that there is contagion risk across currency markets. Our findings suggest important implications for investors, firms, and policymakers in risk management during crisis periods. © 2022 Elsevier B.V.

Evolution of Intent and Social Influence Networks and Their Significance in Detecting COVID-19 Disinformation Actors on Social Media

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence;(ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure;and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Nonlinear nexus between cryptocurrency returns and COVID-19 COVID-19 news sentiment.

The paper examines how various COVID-19 COVID-19 news sentiments differentially impact the behaviour of cryptocurrency returns. We used a nonlinear technique of transfer entropy to investigate the relationship between the top 30 cryptocurrencies by market capitalisation and COVID-19 COVID-19 news sentiment. Results show that COVID-19 COVID-19 news sentiment influences cryptocurrency returns. The nexus is unidirectional from news sentiment to cryptocurrency returns, in contrast to past findings. These results have practical implications for policymakers and market participants in understanding cryptocurrency market dynamics under extremely stressful market conditions. .

Causality Analysis for COVID-19 among Countries Using Effective Transfer Entropy.

In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the strength of the causality which is obtained by calculating effective transfer entropies. Transfer entropy has some advantages over other causality evaluation methods. Firstly, transfer entropy can quantify the strength of the causality and secondly it can detect nonlinear causal relationships. After the construction of the causality network, it is analyzed with well-known network analysis methods such as eigenvector centrality, PageRank, and community detection. Eigenvector centrality and PageRank metrics reveal the importance and the centrality of each node country in the network. In community detection, node countries in the network are divided into groups such that countries in each group are much more densely connected.

Dynamics of Information Flow between the Chinese A-Share Market and the U.S. Stock Market: From the 2008 Crisis to the COVID-19 Pandemic Period.

The relationship between the Chinese market and the US market is widely concerned by researchers and investors. This paper uses transfer entropy and local random permutation (LRP) surrogates to detect the information flow dynamics between two markets. We provide a detailed analysis of the relationship between the two markets using long-term daily and weekly data. Calculations show that there is an asymmetric information flow between the two markets, in which the US market significantly affects the Chinese market. Dynamic analysis based on weekly data shows that the information flow evolves, and includes three significant periods between 2004 and 2021. We also used daily data to analyze the dynamics of information flow in detail over the three periods and found that changes in the intensity of information flow were accompanied by major events affecting the market, such as the 2008 financial crisis and the COVID-19 pandemic period. In particular, we analyzed the impact of the S&P500 index on different industry indices in the Chinese market and found that the dynamics of information flow exhibit multiple patterns. This study reveals the complex information flow between two markets from the perspective of nonlinear dynamics, thereby helping to analyze the impact of major events and providing quantitative analysis tools for investment practice.

The Causality and Uncertainty of the COVID-19 Pandemic to Bursa Malaysia Financial Services Index's Constituents.

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index's constituents comprise several of the strongest performing financial constituents in Bursa Malaysia's Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent's causal influence or intensity relative to each other's performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent's causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia's sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index's largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.

CAUSALITY, INFORMATION FLOW, AND CO-MOVEMENT ANALYSIS OF MAJOR STOCK INDICES

Stock indices are key indicators of the economy since they indicate the strength of a country's stock market. For this reason, causality, information flow and co-movement analysis of stock indices gain importance in comparing countries' economies. Here, we apply a novel approach by analyzing the results of two different methodologies;in wavelet coherence (WTC) analysis, the co-movement between stock indices provided and coherent areas can be shown, and information flow is indicated for five-year periods, especially on coherent zones by Transfer Entropy (TE), which detects cause-and-effect relations. This paper analyzed the information flow and co-movement among FTSE100 in the United Kingdom, the DAX in Germany and S&P500 Index in the United States stock indices. Three different results are obtained as follows: (1) DAX is on the leading side in general for five-year periods, (2) bidirectional information flows arise for every pair in the coherent periods and (3) TE-guided WTC analysis shows that TE sign change can be explained by phase angle direction obtained with WTC. These results indicate that both the methods yield proper outcomes in coherent time zones and during financial crisis like the COVID period, which we have faced for two years;for this reason, the results were also obtained for the COVID period, and in general, that shows DAX dominated other indices. We published this study to help researchers understand the connectedness between stock indices and investors avoiding risk in their stock portfolios, especially during financial crisis periods. © 2022 World Scientific Publishing Company.

HAS THE COVID-19 PANDEMIC AFFECTED THE FLOW OF INFORMATION AMONG CRYPTOCURRENCIES?

In this paper, we investigated the impact of the COVID-19 pandemic on the cryptocurrency market. The direction of information transfer between the 67 digital cryptocurrency markets was evaluated, in particular Bitcoin, Ethereum, Litecoin, and Ripple, and we determined which of them were the most influential in the markets. The comparison of the first half of 2019 (outside the pandemic of COVID-19) against the first semester of 2020 (during the COVID-19 pandemic) was used to analyze the pandemic influence. We found two distinct behaviors: (i) in 2019, Bitcoin, as the primary capitalization bond, presented a more substantial transfer of information than the other cryptocurrencies toward Bitcoin → Ripple (0.0541), followed by Litecoin → Ripple (0.0522);(ii) in 2020, the most substantial transfers of information occurred from Ethereum to other cryptocurrencies (Litecoin, Bitcoin, and Ripple, in that order). In this period, the weakest transfers happened from Litecoin → Ripple and in the opposite direction, with equal value (0.0104). Our results indicate that there was a change in the direction of the information flow between the investigated cryptocurrencies, where ETH became the dominant cryptocurrency during the period of turbulence caused by the COVID-19 pandemic. [ FROM AUTHOR] Copyright of Fractals is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions

This paper investigates the directional causal relationship and information transmission among the returns of West Texas Intermediate (WTI), Brent, major cryptocurrencies, and stablecoins by drawing on daily data from July 2019 to July 2020. Applying effective transfer entropy, a non-parametric statistic, the results show that the direction of the causal relationship and the nature of information spillovers changed after the COVID-19 pandemic. More precisely, our findings reveal that WTI and Brent are leading the prices of Bitcoin and Bitcoin Cash. Conversely, Bitcoin futures and stablecoins (TrueUSD and USD Coin) are leading WTI and Brent prices. In addition, the stablecoin Tether became a leader against Brent prices after the pandemic, although it is still following WTI prices. Moreover, Ethereum and USD coin preserved their position as leaders against Brent prices. Interestingly, our results also reveal that Ethereum, Litecoin, and Ripple preserved their position as leaders of WTI prices. The change in the nature of directional causality and the spillover effect after the COVID-19 crisis provide valuable information for practitioners, investors, and policymakers on how the ongoing pandemic influences the connection and network correlation among the energy, cryptocurrency, and stablecoin markets. © 2022 Elsevier Inc.

Efficient markets are more connected: An entropy-based analysis of the energy, industrial metal and financial markets

This is the first article to jointly study the efficiency and connectedness of commodity markets, using the novel method combination of fuzzy entropy and multivariate transfer entropy analysis. We examine this issue in the nexus of energy, industrial metals and financial markets. We identify the existence of a relationship between market efficiency and connectedness, which opens a new direction for investigating the sources of market connectedness. The results indicate that the efficient markets process more information and are more connected in the system. Moreover, efficient markets are net transmitters of information to the less efficient markets. Stronger connectedness does not necessarily exist within the same market sector but emerges among the efficient markets irrespective of their sector categorization. The relationship between market efficiency and connectedness is more pronounced during extreme events. During the turbulence of COVID-19, there are stronger connectedness and higher information spillover from the efficient to less efficient markets. Specifically, we find the industrial metals are on average the most efficient sector, followed by the energy and then the financial markets. The relatively more efficient natural gas and industrial metal markets exhibit strong connectedness and transmit information to the less efficient crude oil and financial markets. These findings imply that the market connectedness can be the result of information dissemination and alleviate the concerns that higher connectedness may compromise the price discovery mechanism.

Global financial crisis versus COVID-19: Evidence from sentiment analysis

This study examines the relationship between sentiment and the realized volatility of returns for different asset classes (stocks, bonds, foreign currency, and commodities). Specifically, we aim to answer two key questions: first, how does sentiment relate to volatility during crises (mainly during the global financial crisis [GFC] and the COVID-19 pandemic)? Second, can sentiment be used to forecast volatility during crises? Using two nonparametric methods, mutual information and transfer entropy, we find that information sharing and transfer increased during the pandemic. We also find that sentiment information transfer to the volatility of assets differed between the GFC and the COVID-19 crisis. Since sentiment can reduce uncertainty around the realized variance of assets, we investigate the forecasting ability of sentiment during crises. We find that sentiment has a greater predictive power on realized volatility during crises, with a differential impact on volatility depending on the asset class. Our findings carry important implications for hedging, risk management and building models to predict variance during crises.

Linear and Nonlinear Effects in Connectedness Structure: Comparison between European Stock Markets.

The purpose of this research is to compare the risk transfer structure in Central and Eastern European and Western European stock markets during the 2007-2009 financial crisis and the COVID-19 pandemic. Similar to the global financial crisis (GFC), the spread of coronavirus (COVID-19) created a significant level of risk, causing investors to suffer losses in a very short period of time. We use a variety of methods, including nonstandard like mutual information and transfer entropy. The results that we obtained indicate that there are significant nonlinear correlations in the capital markets that can be practically applied for investment portfolio optimization. From an investor perspective, our findings suggest that in the wake of global crisis and pandemic outbreak, the benefits of diversification will be limited by the transfer of funds between developed and developing country markets. Our study provides an insight into the risk transfer theory in developed and emerging markets as well as a cutting-edge methodology designed for analyzing the connectedness of markets. We contribute to the studies which have examined the different stock markets' response to different turbulences. The study confirms that specific market effects can still play a significant role because of the interconnection of different sectors of the global economy.

Is Bitcoin Still a King? Relationships between Prices, Volatility and Liquidity of Cryptocurrencies during the Pandemic.

We try to establish the commonalities and leadership in the cryptocurrency markets by examining the mutual information and lead-lag relationships between Bitcoin and other cryptocurrencies from January 2019 to June 2021. We examine the transfer entropy between volatility and liquidity of seven highly capitalized cryptocurrencies in order to determine the potential direction of information flow. We find that cryptocurrencies are strongly interrelated in returns and volatility but less in liquidity. We show that smaller and younger cryptocurrencies (such as Ripple's XRP or Litecoin) have started to affect the returns of Bitcoin since the beginning of the pandemic. Regarding liquidity, the results of the dynamic time warping algorithm also suggest that the position of Monero has increased. Those outcomes suggest the gradual increase in the role of privacy-oriented cryptocurrencies.