scholarly journals Sector-by-sector analysis of dependence dynamics between global large-cap companies and infectious diseases: A time-varying copula approach in EBOV and COVID-19 episodes

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259282
Author(s):  
Mahdi Ghaemi Asl ◽  
Hamid Reza Tavakkoli ◽  
Muhammad Mahdi Rashidi
Keyword(s):  
Stock Market ◽  
Infectious Diseases ◽  
Ebola Virus ◽  
Disease Outbreaks ◽  
Dependence Structure ◽  
Time Varying ◽  
Copula Approach ◽  
Sector Analysis ◽  
Novel Coronavirus ◽  
The Impact

Infectious diseases and widespread outbreaks influence different sectors of the economy, including the stock market. In this article, we investigate the effect of EBOV and COVID-19 outbreaks on stock market indices. We employ time-varying and constant bivariate copula methods to measure the dependence structure between the infectious disease equity market volatility index (IEMV) and the stock market indices of several sectors. The results show that the financial and communication services sectors have the highest and the lowest negative dependency on IEMV during the Ebola virus (EBOV) pandemic, respectively. However, the health care and energy sectors have the highest and lowest negative dependency on IEMV during the COVID-19 outbreak, respectively. Therefore, the results confirm the heterogeneous time-varying dependency between infectious diseases and the stock market indices. The finding of our study contributes to the ongoing literature on the impact of disease outbreaks, especially the novel coronavirus outbreak on global large-cap companies in the stock market.

scholarly journals Dependence Modelling using GARCH, EGARCH, and Copula Models:

2018 ◽  
Vol 2 (2) ◽  
pp. 55-59
Author(s):  
Nurul Hanis Aminuddin Jafry ◽  
Ruzanna Ab Razak ◽  
Noriszura Ismail
Keyword(s):  
Gaussian Copula ◽  
Dependence Structure ◽  
Time Varying ◽  
Strongly Correlated ◽  
Copula Models ◽  
Egarch Model ◽  
Copula Approach ◽  
Model Finding ◽  
Dependence Modelling ◽  
The Impact

Copula become a popular tool to measure the dependency between financial data due to its ability to capture the non-normal distributions. Hence, this paper will inspect the impact of input models towards the parameter estimation of marginal and copula models for KLCI and FBMHS returns series by considering the ARMA-GARCH model and the ARMA-EGARCH model. This study also investigates the dependency of Islamic-conventional pair for Malaysia indices by using static copula and time-varying copula approach. The closing prices of Malaysia indices represented by KLCI (conventional) index and FBMHS (Islamic) index for the period of 21 May 2007 until 28 September 2018 are used as a sample data. The results show that KLCI-FBMHS pair is strongly correlated, different input models (ARMA-GARCH and ARMA-EGARCH) have identical dependence structure but slightly different value of parameter estimated, and the time-varying Gaussian copula is chosen as the best dependence model. Finding suggest that the diversification between Islamic-conventional pair is worthwhile during stable period.  

scholarly journals Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Ding Ding ◽  
Chong Guan ◽  
Calvin M. L. Chan ◽  
Wenting Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Digital Transformation ◽  
Google Trends ◽  
Building Stock ◽  
Market Sentiment ◽  
Price Performance ◽  
Novel Coronavirus ◽  
The Impact

Abstract As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

scholarly journals Threshold Effects of Infectious Disease Outbreaks on Livestock Prices: Cases of African Swine Fever and Avian Influenza in South Korea

2021 ◽  
Vol 11 (11) ◽  
pp. 5114
Author(s):  
Hyung-Chul Rah ◽  
Hyeon-Woong Kim ◽  
Aziz Nasridinov ◽  
Wan-Sup Cho ◽  
Seo-Hwa Choi ◽  
...  
Keyword(s):  
Avian Influenza ◽  
Infectious Diseases ◽  
Disease Outbreaks ◽  
African Swine Fever ◽  
Threshold Effect ◽  
Impact Response ◽  
Threshold Effects ◽  
Infectious Disease Outbreaks ◽  
Retail Prices ◽  
The Impact

In this paper we demonstrate the threshold effects of infectious diseases on livestock prices. Daily retail prices of pork and chicken were used as structured data; news and SNS mentions of African Swine Fever (ASF) and Avian Influenza (AI) were used as unstructured data. Models were tested for the threshold effects of disease-related news and SNS frequencies, specifically those related to ASF and AI, on the retail prices of pork and chicken, respectively. The effects were found to exist, and the values of ASF-related news on pork prices were estimated to be −9 and 8, indicating that the threshold autoregressive (TAR) model can be divided into three regimes. The coefficients of the ASF-related SNS frequencies on pork prices were 1.1666, 0.2663 and −0.1035 for regimes 1, 2 and 3, respectively, suggesting that pork prices increased by 1.1666 Korean won in regime 1 when ASF-related SNS frequencies increased. To promote pork consumption by SNS posts, the required SNS frequencies were estimated to have impacts as great as one standard deviation in the pork price. These values were 247.057, 1309.158 and 2817.266 for regimes 1, 2 and 3, respectively. The impact response periods for pork prices were estimated to last 48, 6, and 8 days for regimes 1, 2 and 3, respectively. When the prediction accuracies of the TAR and autoregressive (AR) models with regard to pork prices were compared for the root mean square error, the prediction accuracy of the TAR model was found to be slightly better than that of the AR. When the threshold effect of AI-related news on chicken prices was tested, a linear relationship appeared without a threshold effect. These findings suggest that when infectious diseases such as ASF occur for the first time, the impact on livestock prices is significant, as indicated by the threshold effect and the long impact response period. Our findings also suggest that the impact on livestock prices is not remarkable when infectious diseases occur multiple times, as in the case of AI. To date, this study is the first to suggest the use of SNS to promote meat consumption.

scholarly journals The Impact of COVID-19 on the Dependence of Chinese Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xianbo Wu ◽  
Xiaofeng Hui
Keyword(s):  
Stock Market ◽  
Strong Dependence ◽  
Sliding Window ◽  
Dependence Structure ◽  
Chinese Stock Market ◽  
Telecommunication Services ◽  
Window Method ◽  
Dependence Relationship ◽  
Stock Indexes ◽  
The Impact

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long.

scholarly journals Efficacy of contact tracing for the containment of the 2019 novel coronavirus (COVID-19)

2020 ◽  
pp. jech-2020-214051 ◽  
Author(s):  
Matt J Keeling ◽  
T Deirdre Hollingsworth ◽  
Jonathan M Read
Keyword(s):  
Close Contact ◽  
Control Measure ◽  
Disease Outbreaks ◽  
Contact Tracing ◽  
Public Health Response ◽  
Central Public Health ◽  
Increased Risk ◽  
Novel Coronavirus ◽  
The Uk ◽  
The Impact

ObjectiveContact tracing is a central public health response to infectious disease outbreaks, especially in the early stages of an outbreak when specific treatments are limited. Importation of novel coronavirus (COVID-19) from China and elsewhere into the UK highlights the need to understand the impact of contact tracing as a control measure.DesignDetailed survey information on social encounters from over 5800 respondents is coupled to predictive models of contact tracing and control. This is used to investigate the likely efficacy of contact tracing and the distribution of secondary cases that may go untraced.ResultsTaking recent estimates for COVID-19 transmission we predict that under effective contact tracing less than 1 in 6 cases will generate any subsequent untraced infections, although this comes at a high logistical burden with an average of 36 individuals traced per case. Changes to the definition of a close contact can reduce this burden, but with increased risk of untraced cases; we find that tracing using a contact definition requiring more than 4 hours of contact is unlikely to control spread.ConclusionsThe current contact tracing strategy within the UK is likely to identify a sufficient proportion of infected individuals such that subsequent spread could be prevented, although the ultimate success will depend on the rapid detection of cases and isolation of contacts. Given the burden of tracing a large number of contacts to find new cases, there is the potential the system could be overwhelmed if imports of infection occur at a rapid rate.

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

2019 ◽  
Vol 20 (4) ◽  
pp. 962-980 ◽  
Author(s):  
Shegorika Rajwani ◽  
Dilip Kumar
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Tail Dependence ◽  
Time Varying ◽  
The Us ◽  
Asian Economies ◽  
Copula Approach

During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

scholarly journals NETWORK ANALYSIS OF PAKISTAN STOCK MARKET DURING THE TURBULENCE OF ECONOMIC CRISIS

2019 ◽  
Vol 17 (2) ◽  
pp. 269-285 ◽  
Author(s):  
Bilal Ahmed Memon ◽  
Hongxing Yao ◽  
Faheem Aslam ◽  
Rabia Tahir
Keyword(s):  
Network Analysis ◽  
Stock Market ◽  
Interest Rates ◽  
Economic Crisis ◽  
Network Structure ◽  
Topological Property ◽  
Minimum Spanning Tree ◽  
Time Varying ◽  
Tree Length ◽  
The Impact

Purpose – the purpose of this study is to analyse the impact of the recent economic crisis on the network topology structure of Pakistan stock market. Since stock market is considered a core financial market for the development of an economy, it is often used as benchmark to measure a country`s progress. Policymakers often forecast tendency of share prices, that is dependent on several foreign and local macroeconomic factors. Therefore, the aim of this study is to investigate how rising inflation, higher interest rates, and trade and budgetary deficits affect the network structure of blue-chip 96 companies listed on the Karachi stock exchange (KSE-100) index of Pakistan stock market. Research methodology – this study follows the methodology proposed by Mantegna and Stanley and uses cross-correlation in the daily closing price of KSE 100 Index companies to compute Minimum spanning tree (MST) structures. Additionally, we also apply time-varying topological property of average tree length to extract dynamic features of the MST networks. Findings – we construct eight monthly MSTs that show the instability of the network structure and significant differences in the topological characteristics due to economic crisis of Pakistan. Furthermore, the time-varying topological property of average tree length reveals contraction of the networks due to tight correlation among stocks. Research limitations – this study focuses on correlation-based network construction of MST. The scope of the study can be widened by constructing partial correlation-based MSTs and comparison of different networks structures accordingly. Practical implications – the network properties and findings of this paper will help policymakers and regulators in setting right policies, regulatory framework, and risk management for the stock market. Originality/Value – no previous studies have performed MST based network analysis examining macroeconomic events. Therefore, we fill the research gap and thoroughly analyse structural change and dynamics of Pakistan stock market during the turbulence of current economic crisis of Pakistan.

scholarly journals IMPACT OF IMF LENDING ANNOUNCEMENTS ON THE PERFORMANCE OF STOCK MARKET: EMPIRICAL EVIDENCE FROM PAKISTAN

2021 ◽  
Vol 9 (3) ◽  
pp. 467-476
Author(s):  
Muhammad Azeem ◽  
Nisar Ahmad ◽  
Sarfraz Hussain ◽  
Muzammil Khurshid ◽  
Safyan Majid
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Empirical Evidence ◽  
Economic Performance ◽  
Economic Conditions ◽  
The Political ◽  
Time Varying ◽  
Negative Effect ◽  
Multiple Challenges ◽  
The Impact

Purpose of the study: Stock markets have demonstrated varying reactions to IMF lending announcements across various economies. Announcements offered by IMF often be perceived negatively by the participants of the stock market, because of stringent conditions accompanied with the loan that may oppose the political and economic agenda of a borrowing nation. Thus, this study intends to investigate the impact of IMF’s announcements about extending loans to Pakistan on the performance of the Stock market in the debt-ridden economy. Methodology: For regular returns from 1997 to 2017, the benchmarking indexes of KSE-100 and 30 were used. Meanwhile, IMF lending arrangements are categorized into three respective dummies (standby, extended credit facility, and extended fund facility). The Generalized Autoregressive Conditional Heteroscedastic (GARCH) model was used to investigate the effect of IMF’s lending news on the regular stock returns. Main findings: The results show a statistically significant effect of the IMF’s News about lending arrangements on the performance of the stock market in Pakistan. Surprisingly, the negative effect of IMF lending announcements on the performance of the stock market in Pakistan implies that the loans extended by IMF are not professed by speculators as good for the economic performance of the economy. Application of this study: The findings of this study imply that simply extending loans is not a panacea for politically unstable and financially ruined nations. Lending strategies of IMF need to be favourable for the political and economic conditions of a borrowing country. Originality/ Novelty: As for as the novelty is concerned, the study has highlighted the time-varying impact of IMF lending announcements on the performance of the stock market in a financially fragile country where a newborn government facing multiple challenges has made its best effort to avoid borrowing from IMF.

scholarly journals Comparative Analysis of Early Dynamic Trends in Novel Coronavirus Outbreak: A Modeling Framework

2020 ◽  
Author(s):  
Huazhen Lin ◽  
Wei Liu ◽  
Hong Gao ◽  
Jinyu Nie ◽  
Qiao Fan
Keyword(s):  
Growth Model ◽  
Control Measures ◽  
Peak Time ◽  
Time Varying ◽  
Modeling Framework ◽  
Epidemic Size ◽  
Significant Public Health ◽  
Novel Coronavirus ◽  
The Uk ◽  
The Impact

AbstractBackgroundThe 2019 coronavirus disease (COVID-19) represents a significant public health threat globally. Here we describe efforts to compare epidemic growth, size and peaking time for countries in Asia, Europe, North America, South America and Australia in the early epidemic phase.MethodsUsing the time series of cases reported from January 20, 2020 to February 13, 2020 and transportation data from December 1, 2019 to January 23, 2020 we have built a novel time-varying growth model to predict the epidemic trend in China. We extended our method, using cases reported from January 26, 2020 - or the date of the earliest case reported, to April 9, 2020 to predict future epidemic trend and size in 41 countries. We estimated the impact of control measures on the epidemic trend.ResultsOur time-varying growth model yielded high concordance in the predicted epidemic size and trend with the observed figures in C hina. Among the other 41 countries, the peak time has been observed in 28 countries before or around April 9, 2020; the peak date and epidemic size were highly consistent with our estimates. We predicted the remaining countries would peak in April or May 2020, except India in July and Pakistan in August. The epidemic trajectory would reach the plateau in May or June for the majority of countries in the current wave. Countries that could emerge to be new epidemic centers are India, Pakistan, Brazil, Mexico, and Russia with a prediction of 105 cases for these countries. The effective reproduction number Rt displayed a downward trend with time across countries, revealing the impact of the intervention remeasures i.e. social distancing. Rt remained the highest in the UK (median 2.62) and the US (median 2.19) in the fourth week after the epidemic onset.ConclusionsNew epidemic centers are expected to continue to emerge across the whole world. Greater challenges such as those in the healthcare system would be faced by developing countries in hotspots. A domestic approach to curb the pandemic must align with joint international efforts to effectively control the spread of COVID-19. Our model promotes a reliable transmissibility characterization and epidemic forecasting using the incidence of cases in the early epidemic phase.

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