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1.
Industrial Management and Data Systems ; 123(1):64-78, 2023.
Article in English | Scopus | ID: covidwho-2246517

ABSTRACT

Purpose: The aim of this paper is to explore the changes in the ICT and global value chains (GVCs) after the COVID-19 pandemic. Design/methodology/approach: This study compared the difference between Korea' domestic ICT industries, ICT imports and ICT exports before and after the COVID-19 outbreak by using trade data of ICT products and national economic indicators, and presents growth strategy for the ICT industry in the post-COVID 19 era. For this purpose, this study determined the causalities between Korea's imports/exports of ICT products and composite Indexes before and after COVID-19, and derived implications in the ICT industry environment after the COVID-19 pandemic. Findings: Analysis results showed the following changes in Korea's ICT industry in the post-COVID-19 world. (1) Non-face-to-face and contact-free technologies related sectors in the ICT industry, such as the semiconductor sector, have grown exponentially;(2) as the USA has grown as the new key player, the causal relationship with China, a key player of the GVC in the pre-COVID-19 era, disappeared;and (3) the GVC of the ICT industry is not a rigid one-way vertical structure, but is changing to a flexible structure influenced by cooperation and competition between countries. Originality/value: The results indicate that it is essential to constantly develop new ICT sectors that make use of non-face-to-face and contact-free technologies in the post-COVID-19 era, and the main strategies in response to the changed GVC would be taking the initiative by securing source technologies and expanding through cooperation with other GVCs and resource sharing. © 2022, Emerald Publishing Limited.

2.
Energy Economics ; 117, 2023.
Article in English | Scopus | ID: covidwho-2239326

ABSTRACT

This study examines the relationship between crude oil, a proxy for brown energy, and several renewable energy stock sector indices (e.g., solar energy, wind energy, bioenergy, and geothermal energy) over various investment horizons. Using daily data from October 15, 2010, to February 23, 2022, we apply a combination of methods involving co-integration, wavelet coherency, and wavelet-based Granger causality. The results show that the relationship between crude oil and renewable energy indices is non-linear and somewhat multifaceted. Firstly, there are sectorial differences in the intensity of the relationships. Notably, the relationship intensity between the wind and crude oil is lower than that involving geothermal energy or bioenergy. Secondly, the relationship evolves with time. For example, the COVID-19 outbreak seems to have increased the relationship between crude oil and renewable energy markets, notably for solar, bioenergy, and geothermal. Thirdly, the relationship varies across scales. When controlling for the VIX (volatility index), a proxy of the sentiment of market participants, and EPU (economic policy uncertainty index), the relationship seems strong in the long term but weak in the short term. This result is confirmed using a Granger causality test on the wavelet-decomposed series. These findings have important implications for long-term investors, short-term speculators, and policymakers regarding the co-movement between brown and renewable energy markets. © 2022 Elsevier B.V.

3.
Entropy (Basel) ; 25(2)2023 Feb 17.
Article in English | MEDLINE | ID: covidwho-2240764

ABSTRACT

Emerging or diminishing nonlinear interactions in the evolution of a complex system may signal a possible structural change in its underlying mechanism. This type of structural break may exist in many applications, such as in climate and finance, and standard methods for change-point detection may not be sensitive to it. In this article, we present a novel scheme for detecting structural breaks through the occurrence or vanishing of nonlinear causal relationships in a complex system. A significance resampling test was developed for the null hypothesis (H0) of no nonlinear causal relationships using (a) an appropriate Gaussian instantaneous transform and vector autoregressive (VAR) process to generate the resampled multivariate time series consistent with H0; (b) the modelfree Granger causality measure of partial mutual information from mixed embedding (PMIME) to estimate all causal relationships; and (c) a characteristic of the network formed by PMIME as test statistic. The significance test was applied to sliding windows on the observed multivariate time series, and the change from rejection to no-rejection of H0, or the opposite, signaled a non-trivial change of the underlying dynamics of the observed complex system. Different network indices that capture different characteristics of the PMIME networks were used as test statistics. The test was evaluated on multiple synthetic complex and chaotic systems, as well as on linear and nonlinear stochastic systems, demonstrating that the proposed methodology is capable of detecting nonlinear causality. Furthermore, the scheme was applied to different records of financial indices regarding the global financial crisis of 2008, the two commodity crises of 2014 and 2020, the Brexit referendum of 2016, and the outbreak of COVID-19, accurately identifying the structural breaks at the identified times.

4.
Entropy (Basel) ; 25(1)2022 Dec 30.
Article in English | MEDLINE | ID: covidwho-2229744

ABSTRACT

Stock-market-crash predictability is of particular interest in the field of financial time-series analysis. Famous examples of major stock-market crashes are the real-estate bubble in 2008 and COVID-19 in 2020. Several studies have studied the prediction process without taking into consideration which markets might be falling into a crisis. To this end, a combination analysis is utilized in this manuscript. Firstly, the auto-regressive estimation (ARE) algorithm is successfully applied to electroencephalography (EEG) brain data for detecting diseases. The ARE algorithm is employed based on state-space modelling, which applies the expectation-maximization algorithm and Kalman filter. This manuscript introduces its application, for the first time, to stock-market data. For this purpose, a time-evolving interaction surface is constructed to observe the change in the surface topology. This enables tracking of the stock market's behavior over time and differentiates between different states. This provides a deep understanding of the underlying system behavior before, during, and after a crisis. Different patterns of the stock-market movements are recognized, providing novel information regarding detecting an early-warning sign. Secondly, a Granger-causality time-domain technique, called directed partial correlation, is employed to infer the underlying interconnectivity structure among markets. This information is crucial for investors and market players, enabling them to differentiate between those markets which will fall in a catastrophic loss, and those which will not. Consequently, they can make successful decisions towards selecting less risky portfolios, which guarantees lower losses. The results showed the effectiveness of the use of this methodology in the framework of the process of early-warning detection.

5.
Entropy (Basel) ; 25(2)2023 Feb 08.
Article in English | MEDLINE | ID: covidwho-2228658

ABSTRACT

The Global Fear Index (GFI) is a measure of fear/panic based on the number of people infected and deaths due to COVID-19. This paper aims to examine the interconnection or interdependencies between the GFI and a set of global indexes related to the financial and economic activities associated with natural resources, raw materials, agribusiness, energy, metals, and mining, such as: the S&P Global Resource Index, the S&P Global Agribusiness Equity Index, the S&P Global Metals and Mining Index, and the S&P Global 1200 Energy Index. To this end, we first apply several common tests: Wald exponential, Wald mean, Nyblom, and Quandt Likelihood Ratio. Subsequently, we apply Granger causality using a DCC-GARCH model. Data for the global indices are daily from 3 February 2020 to 29 October 2021. The empirical results obtained show that the volatility of the GFI Granger causes the volatility of the other global indices, except for the Global Resource Index. Moreover, by considering heteroskedasticity and idiosyncratic shocks, we show that the GFI can be used to predict the co-movement of the time series of all the global indices. Additionally, we quantify the causal interdependencies between the GFI and each of the S&P global indices using Shannon and Rényi transfer entropy flow, which is comparable to Granger causality, to confirm directionality more robustly The main conclusion of this research is that financial and economic activity related to natural resources, raw materials, agribusiness, energy, metals, and mining were affected by the fear/panic caused by COVID-19 cases and deaths.

6.
International Journal of Housing Markets and Analysis ; 2023.
Article in English | Scopus | ID: covidwho-2213065
7.
Journal of Statistical and Econometric Methods ; 11(4), 2022.
Article in English | ProQuest Central | ID: covidwho-2207922
8.
Journal of Pharmaceutical Negative Results ; 13:3412-3433, 2022.
Article in English | EMBASE | ID: covidwho-2206738
9.
Atmosfera ; 36(2):343-354, 2023.
Article in English | Scopus | ID: covidwho-2204802
10.
13th International Conference on E-Business, Management and Economics, ICEME 2022 ; : 392-398, 2022.
Article in English | Scopus | ID: covidwho-2194089
11.
Studies in Economics and Finance ; 2022.
Article in English | Web of Science | ID: covidwho-2191652
12.
Digital Challenges and Strategies in a Post-Pandemic World ; : 213-228, 2022.
Article in English | Scopus | ID: covidwho-2157085
13.
Journal of Pharmaceutical Negative Results ; 13:1475-1481, 2022.
Article in English | EMBASE | ID: covidwho-2156337
14.
Int J Environ Res Public Health ; 19(24)2022 12 07.
Article in English | MEDLINE | ID: covidwho-2155072

ABSTRACT

Understanding the interplay between discrete emotions and COVID-19 prevention behaviors will help healthcare professionals and providers to implement effective risk communication and effective risk decision making. This study analyzes data related to COVID-19 posted by the American public on Twitter and identifies three discrete negative emotions (anger, anxiety, and sadness) of the public from massive text data. Next, econometric analyses (i.e., the Granger causality test and impulse response functions) are performed to evaluate the interplay between discrete emotions and preventive behavior based on emotional time series and Google Shopping Trends time series, representing public preventive behavior. Based on the textual analysis of tweets from the United States, the following conclusions are drawn: Anger is a Granger cause of preventive behavior and has a slightly negative effect on the public's preventive behavior. Anxiety is a Granger cause of preventive behavior and has a positive effect on preventive behavior. Furthermore, preventive behavior is a Granger cause of anxiety and has a negative and lagging effect on anxiety. Exploring how discrete emotions, such as anger and anxiety, affect preventive behaviors will effectively demonstrate how discrete emotions play qualitatively different roles in promoting preventive behaviors. Moreover, understanding the impact of preventive behaviors on discrete emotions is useful for better risk communication.


Subject(s)
COVID-19 , Social Media , Humans , COVID-19/prevention & control , Emotions/physiology , Anxiety , Anger , Anxiety Disorders
15.
Journal of Statistical and Econometric Methods ; 12(1), 2023.
Article in English | ProQuest Central | ID: covidwho-2125790
16.
Journal of Pharmaceutical Negative Results ; 13:1800-1806, 2022.
Article in English | Web of Science | ID: covidwho-2124261
17.
Indian Journal of Finance ; 16(10):24-42, 2022.
Article in English | Scopus | ID: covidwho-2120745
18.
Econ Anal Policy ; 76: 1075-1097, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2086124

ABSTRACT

This paper explores the relationship between the stock markets of emerging and developed economies and the fear triggered by the COVID-19 pandemic crisis in a period that spans from mid-January 2020 to mid-February 2022. The potential relations are analyzed in terms of Granger causality and dynamic correlation, both from the view of raw undecomposed returns and different time-frequency decompositions derived from a previous wavelet transform screening approach. Overall, our Granger and dynamic correlation results suggest that changes in panic indexes resulting from the COVID-19 pandemic do not have a significant relation with the raw stock market returns, but the reverse occurs in terms of time-frequency decompositions. Correlation analysis also indicates that all countries have a quite similar pattern of phase transitions, with certain stages preceded by a hump and others by a valley, i.e., they exhibit both positive and negative correlations. Despite a gradual reduction in media coverage, both causal relationships and correlations between financial markets and panic indexes held in 2021 and early 2022.

19.
Frontiers in Environmental Science ; 10, 2022.
Article in English | Web of Science | ID: covidwho-2082502
20.
Tourism Economics ; 2022.
Article in English | Web of Science | ID: covidwho-2070682
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