Search Results (12)

The aim of this study is to thoroughly assess the informational content of the CBOE Volatility Index^® (VIX^® Index) in the context of various turbulent periods. The VIX Index is especially important from an investor perspective. It is often referred to as the “investor fear gauge”, because its level tends to spike during periods of market turmoil and other extreme events. Therefore, this index significantly differs from other market indices and financial instruments. Information theory and normalized Shannon entropy, combined with a rolling-window dynamic approach, are used to explore the evolution of the VIX Index over time. The research hypothesis states that the informational content of the VIX Index varies substantially across periods affected by crucial events. To verify this hypothesis, three important periods of the twenty-first century are analyzed: (1) the Global Financial Crisis, (2) the COVID-19 pandemic outbreak, and (3) the period covering the sub-periods before and after the Donald Trump’s Presidential Inauguration. The results provide no reason to reject the research hypothesis. The empirical findings show that the entropy values appear to be quite sensitive to the choice of discretizaton procedure. However, this evidence is consistent with the existing literature. Full article

The present study utilized two prominent emotion theories to investigate intention and behavior involved in propagating threatening social media messages. Participants were randomly assigned to different blocks of tweets/Xs with the same word count but different topics/sentiments. The topics in Study 1 (N = 619) were neutral and illegal border crossing, whereas the topics in Study 2 (N = 577) were the virulent risk of COVID-19 and the potential risks of newly developed vaccines. Dissemination intention was gauged by the number of tweets that participants wanted to share. Participants were also asked to summarize the messages to observe their behavioral engagement with the information, specifically through time spent on the task and the number of words written. An intention–behavior disjoint was found under all threatening topics and on both sides of the political divide. Fearful participants showed engaging intentions (wanted to share more tweets) but disengaging behaviors (wrote fewer words and submitted their summaries sooner). The necessary and sufficient conditions for the intention–behavior disjoint seemed to be the presence of threatening contents and subjective fear. Communicating risks can spark interest, but it is important not to burden the audience with too much fear, or they may stop spreading the word. Full article

The six short-sales constraints (SSCs) regime changes from 2002 to 2009 in the Taiwan stock market provide “natural social” experiments to examine how different SSC intensities affect price adjustment efficiency and market stability. There are three main findings. Firstly, we derive the theoretical price with put–call parity from seven series index options. Using a “threshold error correction model” (TECM), we find a more efficient price adjustment to new equilibria for upward adjustments than for downward adjustments. The SSCs impede the price adjustment downward, especially during the financial crisis of 2008. Therefore, relaxing the short-sales constraints essentially improves price efficiency. Secondly, our findings also refute the claim that tighter SSCs can help stabilize the market since the tightening of the short-sales restriction leads to increases in both market volatility and downside risk even after controlling the investor fear gauge of the Taiwan volatility index (TVIX). These results hold even when market conditions and liquidity are controlled. Finally, the evidence from our counterfactual policy analysis suggests that tighter constraints help restore market confidence even though prices may fall more sharply without short-sales bans. As a result, policymakers may practically optimize to strike a balance between the benefits of restored emerging market order and the cost of elevated market volatility. Full article

Each year, the Department of Entomology at Virginia Tech hosts an entomology-themed outreach event known as Hokie BugFest. This on-campus, festival-sized experience aims to educate the public about insects and other arthropods through hands-on activities, games, displays, and live arthropods. In 2021, due to the COVID-19 pandemic, Hokie BugFest and similar large public events were cancelled. In response, the department launched Hokie BugFest on the Go, which offered smaller-scale, in-person learning opportunities during these closures. Instead of hosting the community on campus, Virginia Tech’s Department of Entomology brought live arthropods, university experts, and the exciting science of entomology directly into schools, fostering small-group, hands-on learning experiences. In 2022, a playful assessment was added to the traveling outreach program to measure changes in student attitudes and perceptions of insects and other arthropods before and after the program. The assessment also gauged students’ favorite arthropods after seeing, and in some cases handling, them live during the program. Assessment results revealed valuable insights into how hands-on, applied learning experiences can shift children’s attitudes toward arthropods. Results showed that even after expressing trepidation and fears, students’ knowledge and comfort levels with insects and other arthropods increased as they interacted and learned throughout the program. These findings underscore the value of using engaging, hands-on, small-group approaches when designing entomology-themed outreach events for young audiences and offer guidance for future programs. Full article

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors’ fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets. Full article

Fear-responses to bat-originating coronavirus pandemics with respect to quarantine imposition are gathered and interpreted from large datasets, identified and disseminated by media. Responses are effectively gauged using postmodern thought with a continuum ranging from people’s resilience to define their own perspectives to public views being socially conditioned from media persistence in maintaining fear. Public responses to the 2003 SARS pandemic generally presumed and supported resilience of citizens’ perspectives. In contrast, from late 2019 to mid-2022, public responses to the COVID-19 pandemic were media-determined, promoting fear. In this regard, reactions to the COVID-19 quarantines are contrasted to the hospital isolations of SARS. The primary source of the difference was the major polarizing influence by social media of the WHO policy makers’ pronouncements and of healthcare providers’ statements directing media spotlight in their guidance of public response to COVID-19 throughout the pandemic, unlike during SARS. An investigation of cognitive bias regarding the psychological and societal implications related to this migration from resilience to fear regarding public responses to novel bat-originating coronavirus pandemics elicits recommendations concerning future quarantine dictates. These recommendations are dependent on appropriate encouragement of hopeful resilience through evidence based practice with respect to one extreme of the postmodern thought continuum. Full article

This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible. Full article

The COVID-19 pandemic forced countries all over the world to take unprecedented measures, like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between the 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on—(i) the Italian official campaign for self-quarantine (#iorestoacasa), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data. Full article

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities. Full article

The oil price time series data can be affected by major global political and economic events, which would result in structural changes that could lead to biased estimations. By adopting the Bai and Perron model this paper found that there were six structural breaks in the Brent oil price due to major global events and that ARDL-ECM cointegration exists only between oil price and stock market volatility index (VIX) throughout the sampling period. However, cointegration relations were found between oil price and Crude Oil Volatility Index (OVX) in the second and fourth sub-periods, and also between oil price and VIX in the second, third, fourth, sixth, and seventh sub-periods. Furthermore, the cointegration relation coupled with correlation analysis indicates a long-term equilibrium positive (negative) relation between the two variables. Such relations existed between the price and the two fear gauges, respectively, only for some specific sub-periods, implying that OVX seemed to be better than VIX in predicting oil price changes. We suggest that the investors in the global oil market must pay attention to not only the impacts of major global political and economic events on oil price, but also the positive or negative correlations between oil price and fear gauge. Full article

This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between oil prices and OVX (VIX) using the linear autoregressive distributed lag (ARDL)-bounds test. Likewise, while using the nonlinear autoregressive distributed lag (NARDL)-bounds test, not only does a long-run equilibrium relationship exist, but also the rising OVX (VIX) has a greater negative influence on oil prices than the declining OVX (VIX), thus indicating that a long-run, asymmetric cointegration exists between the variables. Furthermore, OVX (VIX) oil prices have a linear Granger causality, while for the nonlinear Granger causality test, oil prices have a bidirectional relation with OVX (VIX). In addition, we find that once major international political and economic events occur, structural changes in oil prices change the behavior of oil prices, and thus panic indices, thereby switching from a linear relationship to a nonlinear one. The empirical results of this study provide market participants with more valuable information. Full article

The role of automation in sustainable development is not in doubt. Computerization in particular has permeated every facet of human endeavour, enhancing the provision of information for decision-making that reduces cost of operation, promotes productivity and socioeconomic prosperity and cohesion. Hence, a new field called information and communication technology for development (ICT4D) has emerged. Nonetheless, the need to ensure environmentally friendly computing has led to this research study with particular focus on green computing in Africa. This is against the backdrop that the continent is feared to suffer most from the vulnerability of climate change and the impact of environmental risk. Using Nigeria as a test case, this paper gauges the green computing awareness level of Africans via sample survey. It also attempts to institutionalize green computing maturity model with a view to optimizing the level of citizens awareness amid inherent uncertainties like low bandwidth, poor network and erratic power in an emerging African market. Consequently, we classified the problem as a stochastic optimization problem and applied metaheuristic search algorithm to determine the best sensitization strategy. Although there are alternative ways of promoting green computing education, the metaheuristic search we conducted indicated that an online real-time solution that not only drives but preserves timely conversations on electronic waste (e-waste) management and energy saving techniques among the citizenry is cutting edge. The authors therefore reviewed literature, gathered requirements, modelled the proposed solution using Universal Modelling Language (UML) and developed a prototype. The proposed solution is a web-based multi-tier e-Green computing system that educates computer users on innovative techniques of managing computers and accessories in an environmentally friendly way. We found out that such a real-time web-based interactive forum does not only stimulate the interest of the common man in environment-related issues, but also raises awareness about the impact his computer-related activities have on mother earth. This way, he willingly becomes part of the solution to environment degradation in his circle of influence. Full article