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1.
Journal of Decision Systems ; JOUR: 1-19,
Article in English | Web of Science | ID: covidwho-2082830

ABSTRACT

Given the broad scope of Ethereum and the wide range of its decentralized applications, this paper investigates its hedging and safe haven capabilities against main fiat currencies, stock and bond indices in the US and Europe, and crude oil and gold markets. We use daily data from January 2016 until February 2021 and apply percentile regressions and crisis event interaction analysis by selecting four worldwide events including US presidential elections, the Brexit referendum, and COVID-19. We reveal that Ethereum does not act as a hedge or a safe haven against fiat currencies, stock and bond indices, and gold. However, it does act as a strong safe haven against crude oil in calm and turbulent periods and against European bonds during market turbulence. The study provides insights to regulators and investors into the potential role of Ethereum in investment decision-making and protecting financial market participants in the US and EU.

2.
International Review of Financial Analysis ; JOUR: 102417,
Article in English | ScienceDirect | ID: covidwho-2082765

ABSTRACT

This paper explores a fresh topic about the tail connectedness between decentralized- lending/borrowing tokens and centralized-commercial bank stocks, regarded as substitutes. Using the methodological approach proposed by Ando et al. (2022), we compare connectedness results at extreme (lower and upper) quantile levels. DeFis and traditional bank stocks may show positive but low spillovers, thus DeFi lending tokens would constitute a new commercial banking asset class. In addition, the tails of the distribution would show excess return (static and dynamic) spillover compared to the mean and median, indicating an increased sensitivity in the extreme market conditions (such as the COVID-19 pandemic), especially in the left tail. The dynamic net spillovers may vary over time for all markets and increase during periods of uncertainty, in line with very recent studies. Also, RTD (Relative Tail Dependence) rejects quasy-symmetry due to its time-variation, ranging between positive and negative values. Therefore, traders and portfolio managers would need to adjust their positions depending on the time-varying net spillovers.

3.
Sustainability ; 14(19):12356, 2022.
Article in English | ProQuest Central | ID: covidwho-2066403

ABSTRACT

This article investigates the connection between US logistics companies’ commitment to environmental, social and fair governance (ESG) strategy and their performance on the US stock market during the 2007–2022 period. The research considers historical data analysis, CAPM and a comparison of optimised portfolios. According to the results of the analyses, ‘green’ logistics stocks are less volatile, and hence less risky, and more profitable compared to ‘non-green’ logistics stocks. The Great Recession (2007–2009) and the COVID-19 pandemic (2020) had the greatest impact on stock volatility, in terms of the US stock market. Optimised during the time of the Ukrainian crisis, green logistics portfolios were shown to have higher returns, but also risks and Sharpe ratios, than ‘non-green’ ones. The results confirm there to be a connection between companies’ commitment to ESG strategy and enhanced stock performance, which contributes to the importance of the ESG agenda.

4.
Resources Policy ; : 103048, 2022.
Article in English | ScienceDirect | ID: covidwho-2061818

ABSTRACT

Previous studies have neither examined the volatility co-movements across stock and commodity markets in terms of both time and frequency nor differentiated between bad and good volatility and the potential asymmetric effect. To address this gap, we computed 5-min price data, the positive and negative semivariances on five leading Exchange Traded Funds (ETFs) covering the US equity market, crude oil, natural gas, gold, and silver markets from January 2, 2019 to May 29, 2020, and then draw on the wavelet coherency methodology and the time-varying wavelet coherence measure. The results showed that the negative realized volatility co-movements are stronger during the COVID-19 outbreak, especially at short and medium frequencies. The US stock market leads energy and precious metals in the short-run frequency. However, over the long-run, the lead-lag pattern mostly alternates over time for all cases. Notably, the realized volatilities of US equities and precious metals are shaped by the COVID-19 outbreak, reflecting the quest of investors for protection from the market volatilities by investing in gold and silver. This latest finding is confirmed by the wavelet coherence measure. Further results showed asymmetric co-movements emanating especially from realized negative semivariance of equities and energy markets around the pandemic outbreak across short time horizons. We also noticed that the COVID-19 outbreak increased the procyclical movement of all ETFs in the short term. The effect is more pronounced among the US equity and precious metals markets, whereas no significant countercyclical connectedness is observed among these markets. Our findings supported previous evidence that gold and silver can serve as safe-haven assets due to their low coherence with other assets.

5.
Energy Economics ; : 106348, 2022.
Article in English | ScienceDirect | ID: covidwho-2061099

ABSTRACT

The surmounted environmental and energy challenges have motivated this study to explore the connectedness nexus between oil/renewable energy and stock markets for oil-exporting (importing) countries. We utilize the dynamic conditional correlation (DCC-GARCH) connectedness framework to compare the connectedness of oil/renewable energy with stock markets. Our results showcase higher total connectedness between renewable energy and stock markets. We find increased connectedness during three major pandemics (Swine Flu, EBOLA, and COVID-19). We performed a regression analysis that highlighted the impact of economic and financial uncertainties on connectedness as an additional analysis. The addition of dummy variables for three major pandemics indicates that COVID-19 significantly impacted the connectedness between oil/renewable energy and stock markets. For the robustness of our results, we employed time-varying vector autoregressions (TVP-VAR) connectedness framework to showcase that our results remain qualitatively similar and robust to different specifications. We draw useful implications for oil exporting and oil importing countries in particular, and we draft ramifications for investors, portfolio managers, policymakers, and macroprudential bodies in general.

6.
Brazilian Business Review ; 19(5):1-2, 2022.
Article in English | ProQuest Central | ID: covidwho-2045102
7.
International Journal of Development Issues ; 21(3):347-366, 2022.
Article in English | ProQuest Central | ID: covidwho-2037669

ABSTRACT

Purpose>This study aims to explore the sustainability of Jamaica’s public debt over a highly volatile period of time.Design/methodology/approach>The authors use a suite of econometric tools, including, unit root testing, cointegration testing and estimating a fiscal reaction function. The authors control for structural breaks in the regression analysis.Findings>The authors find that whilst reschedulings might be indicative of cash-flow problems in Jamaica, fiscal policy has responded effectively to increase the public debt, thereby making the debt sustainable. Notwithstanding the political economy and social demands of the population prior to the impact of the pandemic, the implications of higher debt stocks (higher debt-servicing and lower social expenditures) might make this approach to fiscal policy and debt management infeasible. As a result, the authors recommend that the government will need to take an active approach in managing its debt position to facilitate responses to shocks and provide conditions within which maintaining fiscal discipline is feasible.Originality/value>To the best of the authors’ knowledge, this is the first study to explore fiscal sustainability in Jamaica over this time period whilst taking into consideration structural breaks caused by the global financial crisis and debt restructurings. The authors also take into consideration variables such as exchange rates and the occurrence of elections, which have not been included in previous studies.

8.
2022 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2022 ; 12330, 2022.
Article in English | Scopus | ID: covidwho-2029453

ABSTRACT

The research is conducted during the rage of COVID-19 throughout the world. The world meets new challenges from COVID-19 from every dimension, especially the economical world. In the economic world, the most related part for the influence that springs from COVID-19 is the stocks belonging to the healthcare sector. Aiming at doing the return prediction for healthcare sector stocks, the study chooses Long Short-Term Memory (LSTM) Algorithm to introduce machines to adapt the pattern and make predictions. The study selects 6 less volatile while keeping high average trading volume stocks from the healthcare sector. Using the LSTM learning model to learn the past 5 years’ data and make the prediction to the future 5 days. The data consist of 65% of the company's data from five years ago as the training set, and the last 35% of the data as the test set. The study compares the actual data to the predicted data and sees the error by calculating root mean square error (RMSE). The result draws the conclusion that the model will perform more precise prediction when the picked stock has a clear price trend and less fluctuation. The application for this study is to provide a short-term trading strategy and manage the risk for short-term stock investment by using the LSTM model. © 2022 SPIE.

9.
Operations Management Research ; 15(1-2):503-527, 2022.
Article in English | ProQuest Central | ID: covidwho-2027683

ABSTRACT

This paper, for the first time, presents a production scheduling model for a production line considering physical distancing between the machines' workforces. The production environment is an unrelated parallel-machine, in which for producing each part, different machines with different production rates and the required number of workers are available. We propose a three-objective mixed-integer linear programming mathematical model that aims to maximize the manufacturer's total benefit, parts' safety stock (SS) index, and the workforce's physical distance over a finite horizon (one year) by determining the optimal scheduling of the parts on the machines. Since a large production scheduling problem belongs to the Np-Hard category of problems, a non-dominated sorting genetic algorithm, and a non-dominated ranked GA algorithm are developed to solve the presented model in two stages using the empirical data from a Canadian plastic injection mold company. In the first stage, the LP-metrics approach is utilized for validating the meta-heuristics on a reduced-size problem. In the second stage, the validated meta-heuristics are utilized to optimize the company's yearly production schedule. The results indicate both metaheuristics are performing well in determining the optimal solution. Moreover, implementing physical distancing in the company reduces the company's monthly net benefit by around 9.56% compared to the normal operational conditions (without considering physical distancing).

10.
Finance: Theory and Practice ; 26(3):19-32, 2022.
Article in English | Scopus | ID: covidwho-2026388

ABSTRACT

The subject of the research is the segments of the financial system of the Russian Federation: the budget system, the banking sector, the stock and insurance markets, and the currency policy of the state. The purpose of the study is to determine the trends and factors in the development of the main elements of the financial system at the present stage. The relevance of scientific research is due to the fact that the financial system is a key element of the strategy of socio-economic development of any state, providing economic processes with financial resources and capital. The author uses the following methods: analysis, synthesis, generalization, and the logical method. The study highlights promising directions, ways and mechanisms for the development of the Russian financial system that are relevant in the 2020s. The key factors influencing their trends and threats that create barriers are analyzed. The main directions, ways and mechanisms for stimulating the further development of the elements of the financial system are described. The author concludes that due to the spread of the coronavirus pandemic and economic sanctions imposed on Russia, the stability of the Russian financial system has been violated, which requires the adoption of state regulation mechanisms to improve the activities of financial institutions. The prospect of further research on this topic may be related to the development of areas for improving individual elements of the Russian financial system. © Ismoilov g.N., 2022.

11.
Axioms ; 11(8):375, 2022.
Article in English | ProQuest Central | ID: covidwho-2023120

ABSTRACT

This paper introduces methodologies in forecasting oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. We also apply Bayesian variable selection and nonlinear principal component analysis (NLPCA) for data dimension reduction. With a reduced number of important covariates, we also forecast oil prices (Brent and WTI) with multivariate time series of major S&P 500 stock prices using Gaussian process modeling, deep learning, and vine copula regression. To apply real data to the proposed methods, we select monthly log returns of 2 oil prices and 74 large-cap, major S&P 500 stock prices across the period of February 2001–October 2019. We conclude that vine copula regression with NLPCA is superior overall to other proposed methods in terms of the measures of prediction errors.

12.
Digital Policy, Regulation and Governance ; 24(4):398-399, 2022.
Article in English | ProQuest Central | ID: covidwho-2018450

ABSTRACT

[...]Musk – that is to say, Elon Musk, the boss of car-maker Tesla. In 2020, Musk tweeted that the “Tesla stock price too high” which promptly wiped $14bn from the market value of Tesla. The share price had peaked at $77 one year earlier but had subsequently fallen by roughly one-third – much in line with other tech stocks and widely attributed to the emergence from COVID-related restrictions that had resulted in a deluge of social media communication by people stuck at home with nothing better to do.

13.
Tourism Economics ; 2022.
Article in English | Scopus | ID: covidwho-2020991

ABSTRACT

The outbreak of the COVID-19 pandemic and the steps taken to contain its spread resulted in a decline in tourism sector stock prices. Using linear and quantile regressions, we examine the impact of Twitter-based investor sentiment for COVID-19 and Twitter-based sentiment towards uncertainty on the performance of tourism stocks. The findings indicate a heterogenous effect of tweets and Twitter economic uncertainty on tourism sector equity returns with a major impact on the lower quantiles. © The Author(s) 2022.

14.
Asian-European Journal of Mathematics ; 2022.
Article in English | Web of Science | ID: covidwho-2020369

ABSTRACT

In the aftermath of the COVID-19 pandemic, global financial markets have seen growing uncertainty and volatility and as a consequence, precise prediction of stock price trend has emerged to be extremely challenging. In this background, we propose two time frameworks wherein the Hybrid genetic algorithm (RIGA) is used to set up an optimal portfolio included ten stocks traded in Bulgarian stock market during pre and post COVID-19 periods. The fitness function values of constructed HG A during pre- and post-COVID-19 periods were -7.194e(-04) and -7.014e(-04), respectively. The estimated nonzero portfolio weights during pre-COVID-19 period were ALCM (0.025), HNVK (0.253), HVAR (0.378), MSH (0.204), NEOH (0.038), and SFT (0.102) while during post-COVID-19 period were AGH (0.003), ALCM (0.015), HNVK (0.272), HVAR (0.460), MSH (0.142), NEOH (0.057), SFT (0.031), and SPH (0.021). The corresponding expected portfolio return and portfolio risk during pre-COVID-19 period were 9.825e(-03) and 7.163e(-04) while during past-COVID-19 period were 9.656e(-03) and 6.895e(-04), respectively.

15.
Journal of Commodity Markets ; 2022.
Article in English | ScienceDirect | ID: covidwho-2007821

ABSTRACT

The paper examines the frequency-based interlinkages between stock indices and precious metals at extreme and median quantiles. It employs the quantile cross-spectral approach (Baruník and Kley, 2019) and the novel frequency quantile connectedness analysis (Chatziantoniou et al., 2021) to a sample of stocks and precious metals returns. The results show that the interdependence between equity indices and precious metals markets is contingent on the state of the market (bear, bull, or normal) and the horizon of frequency domains. Of all precious metals, the diversification benefits from gold, followed by silver, are consistently the highest for SP500 and STOXX50 and the least with palladium in most cases. The same holds when we investigate the diversification potential of precious metals for industrial sectors in the US and UK. A quantile frequency connectedness approach reveals that the diversification potential of precious metals diminishes in the long frequency horizon as coherence with stock indices becomes highly positive. The connectedness between stock indices and precious metals is high during market extremities but dampens as the market attains stability. At the same time, connectedness increases during periods of financial turmoil across all frequencies. We also document a change in the diversification role of precious metals during COVID-19.

16.
Fulbright Review of Economics and Policy ; 2(1):20-34, 2022.
Article in English | ProQuest Central | ID: covidwho-2001554

ABSTRACT

Purpose>The paper examines the impact of COVID-19 on bank stock returns over various time scales and frequencies for 36 countries. Moreover, the authors look at the governments' responses to the corona crisis and examine its impact on bank stock returns.Design/methodology/approach>The paper applies continuous wavelet transformation to obtain robust estimates of the co-movement (coherency) between confirmed cases and bank stock returns over time and at different time scales. Furthermore, the authors apply fixed effects panel regression to examine the response of bank stocks to domestic COVID-19 policies.Findings>The results indicate that the number of confirmed COVID-19 cases negatively impacts bank stock returns during different waves of the pandemic in the medium-run. However, there is only little dependence in the very short-run. Moreover, bank stock returns positively react to domestic COVID-19 polices. This demonstrates that governmental interventions not only reduce the spread of COVID-19 but are also able to thereby calm financial markets.Originality/value>The application of wavelet methods to the field of economics and finance is relatively recent and allows the distinction between short-term and long-term effects. Standard econometric methods, in contrast, only operate within the time domain. This paper combines wavelet methods with conventional econometrics to answer the research question.

17.
Frontiers in Marine Science ; 9, 2022.
Article in English | Web of Science | ID: covidwho-1997451

ABSTRACT

The ocean is facing multiple pressures from human activities, including the effects of climate change. Science has a prominent role in identifying problems and communicating these to society. However, scientists are also increasingly taking an active role in developing solutions, including strategies for adapting to and mitigating climate change, increasing food security, and reducing pollution. Transmitting these solutions to society changes our narrative about the ocean and motivates actions. The United Nations triple initiatives for this decade-the Sustainable Development Goals, the Decade on Ocean Science for Sustainable Development, and the Decade of Ecosystem Restoration-provide the momentum for this change in narrative and focus. Here, we reflect on the search for solutions and the need for better ways of communicating science in a positive way. We synthesize insights from a summer school held during the COVID-19 pandemic and present some examples of successes and failures and the lessons learned from these.

18.
International Journal of Managerial Finance ; 2022.
Article in English | Web of Science | ID: covidwho-1997107

ABSTRACT

Purpose - This study seeks to investigate role of the coronavirus disease 2019 (COVID-19) pandemic on clean energy stocks for the United States for the period 21 January 2020-16 August 2021. Design/methodology/approach - At the empirical stage, the Fourier-augmented vector autoregression approach has been used. Findings - According to the empirical results, the response of the clean energy stocks to the feverish sentiment, lockdown stringency, oil volatility, dirty assets, and monetary policy dies out within a short period of time. In addition, the authors find that there is a unidirectional causality from the feverish sentiment index and the lockdown stringency index to the clean energy stock returns;and from the monetary policy to the clean energy stocks. At the same time, there is a bidirectional causality between the lockdown stringency index and the feverish sentiment index. The empirical findings can be helpful to both practitioners and policy-makers. Originality/value - Among the COVID-19 variables used in this study is a new feverish sentiment index, which has been constructed using principal component analysis. The importance of the feverish sentiment index is that it allows us to examine the impact of the aggregate level of fear in the economy on clean energy stocks.

19.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-341991

ABSTRACT

Motivated by the incessant demand for portfolio diversification, this study examines the connectedness between value and diverse types of stocks (growth, momentum, ESG, high beta, classic S&P 500, volatility). The applied methodology encompasses the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (2012) framework for the period from 03/31/2011 to 03/31/2021. Results show moderate volatility transmissions among the sampled assets, which tend to escalate during periods of turmoil, such as the European Sovereign Debt Crisis, the plunge in oil prices and the COVID-19 outbreak. Growth and ESG stocks play an indispensable part in the transmission mechanism. Moreover, we investigate the hedging ability of value stocks within a portfolio containing other stocks, by estimating hedge ratios and optimal weights with the usage of conditional variance estimates (DCC-GARCH). The empirical findings reveal that value stocks can adequately hedge against the risk deriving from the volatility of the remaining investment instruments, especially in the case of high beta and volatility stocks. Thus, this analysis provides portfolio managers and investors with valuable insights in order for them to hedge their stock portfolios effectively.

20.
Resources Policy ; 78:102920, 2022.
Article in English | ScienceDirect | ID: covidwho-1983875

ABSTRACT

Recent literature extensively studies the safe-haven properties of different asset classes in crisis periods. The magnitude of the economic policy uncertainty index (EPU) and the geopolitical risk (GPR) increases significantly during extreme crisis periods such as covid crisis, but the earlier literature ignores how both risk measures impact on different asset classes during severe economic downturns. In this paper, we contribute by examining the hedging and safe-haven properties of gold, oil, equities, and foreign exchange rates against the United States (US) EPU and GPR by utilizing OLS regression, quantile regression and the quantile connectedness approach for pre-covid (October 1, 2013–March 10, 2020) and post-covid data (March 11, 2020–August 27, 2021). OLS results suggest that only the stock market has positive risk premium for both uncertainty measures. With quantile regression analysis for the pre-covid period, we find that asset returns provide no hedge (hedge) across bearish (bullish) market conditions. Importantly, safe-haven properties suggest that gold is a safe-haven asset at the extreme stress condition (at higher level of USEPU shocks). Other assets also exhibit safe-haven characteristics during extreme uncertain periods with heterogeneity in safe-haven effectiveness across bearish to bullish markets. With the post-covid data, we show that S&P500 stocks and EURO hedge EPU and GPR in bullish market condition, while Oil, S&P500, Great Britain Pound, EURO, Japanese Yen display safe-haven properties at the 99% quantile of USEPU. Specifically, gold lost its safe-haven features during covid. Interestingly, results from quantile connectedness suggest that selected asset returns have the potential to diversify against uncertainty measures considering low volatility transmissions between them across the lower and higher quantiles. Our findings are important for investors and asset managers who aim to hedge EPU and GPR during the stress period.

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