Cryptocurrencies and oil price shocks: A NARDL analysis in the COVID-19 pandemic

The dynamic risk spillover effects among carbon, renewable energy, and electricity markets based on the TVP-VAR-DY model

The linkages among carbon, renewable energy, and electricity markets are gradually strengthening. In order to prevent risk transmission among markets, this paper uses the TVP-VAR-DY (Time-Varying Parameter-Vector Auto Regression-Dynamic) model to analyze the dynamic risk spillover effects and network structure of risk transmission among carbon, renewable energy, and electricity markets. The empirical results show that there are significant asymmetric spillover effects among carbon, renewable energy, and electricity markets. The total spillover index shows that spillover effects among carbon, renewable energy, and electricity markets are time-varying, especially during unexpected events. Besides, the net spillover index indicates that the spillover effects are bidirectional, asymmetric, and time-varying. Finally, under the influence of unexpected events, the network structures of risk transmission among carbon, renewable energy, and electricity markets are heterogeneous. Compared to the Russia-Ukraine conflict, the COVID-19 pandemic has a more significant impact on these markets.

Who are the receivers and transmitters of volatility spillovers: oil, clean energy, and green and non-green cryptocurrency markets

In the context of clean energy and green cryptocurrency development, the relationship between energy and cryptocurrency markets deserves further exploration. This study employs a quantile time-frequency connectedness approach to measure the dynamic connectedness and volatility propagation mechanisms between oil, clean energy, green cryptocurrency (GC), and non-green cryptocurrency (NGC) markets. Our findings suggest that, at median and low volatility levels, the oil and clean energy markets act as net receivers, taking on volatility spillovers from cryptocurrency markets. However, at high volatility levels, oil and clean energy markets transform into net transmitters. Most NGCs are volatility transmitters, while most GCs are volatility receivers in the median and extremely high volatility cases. We also observe that the total connectedness index (TCI) is heterogeneous over time and dependent on economic events. At median and low volatility levels, the short-run TCI makes the primary contribution. On the other hand, for high volatility levels, where short-term TCI does not have an absolute advantage, long-term TCI plays a greater role in many periods. Additionally, there is asymmetry in the TCI (including long-term and short-term TCI) at the quantile level. In the median and extreme scenarios, the COVID-19 has caused different levels of shock on oil, clean energy, GC, and NGC markets connectedness.

Government Bonds and COVID-19. An International Evaluation Under Different Market States

This study evaluates the sensitivity of government bond yields from the countries most affected by the COVID-19 pandemic to variations in some international risk factors during the period between January 2020 and April 2021. This sample period allows us to focus the study on the first, and the subsequent waves of the COVID-19 pandemic. Specifically, we propose an extended risk factor model estimated using the quantile regression approach. In addition, this study compares the COVID-19 pandemic period with a pre-pandemic and a post-vaccination period. Interesting differences among them are observed, remarking that gold is the key risk factor during the pandemic, whereas VIX and crude oil play that role in the pre-pandemic and the post-vaccination periods, respectively, mainly for bearish states. As expected, the explanatory power of the model is better at extreme quantiles, showing relevant differences between sensitivities, because the found effects are quantile-, country- and risk factor-dependent. The results during the pandemic are robust to the inclusion of a country-specific factor and a factor accounting for the mutual influence of the government bonds.

Climate change, the by-product of tourism and energy consumption through a sustainable economic growth: a non-linear ARDL analysis for Turkey

Using a non-linear autoregressive distributed lag time-series analysis, this paper investigates the causal relationship between climate change, the tourism sector, and energy consumption in Turkey. The trade-off between a country's economic growth and the environmental degradation caused by tourism and the energy sector is critical in terms of scientifically addressing the issue and developing economic policies. As a result, climate change is used as the dependent variable and is represented by precipitation and temperature separately; the independent variables are tourist arrivals, energy consumption, and economic growth. Data is gathered by various institutions from 1995 to 2020. According to the test results, while positive and negative shocks contribute to the decrease in precipitation and temperature in renewable energy consumption (REC) in the long-run, they affect the increase in precipitation and temperature in non-renewable energy consumption (NREC). In the long-run relationship between tourism and temperature, a decrease in the number of tourist arrivals causes a decrease in temperature and precipitation. The findings reveal that a decrease in the number of tourist arrivals and an increase in REC may aid in decreasing temperature, while the increase in NREC may cause an increase in temperature. Through a case study of Turkey, decision-makers should consider these scientific findings that are in the frame of non-linear analysis as possible scenarios for mitigating climate change and fostering sustainable economic growth with efficient tourism policies for the world.

Cryptocurrency adoption and continuance intention among Indians: moderating role of perceived government control

Purpose

This study aims to investigate the influence of perceived government control (PGC) on cryptocurrency adoption and continuance intention among Indians through an integrated model of the extended Unified Theory of Acceptance and Use of Technology (UTAUT) with the Information System Success Model (ISSM).

Design/methodology/approach

This study examined the items of cryptocurrency adoption, continuance intention and PGC adopted from the information systems and cryptocurrency literature. The survey was administered to 391 Indians through an online questionnaire. Partial least squares structural equation modeling was used to analyze data.

Findings

Results have shown that social influence, effort expectancy and perceived trust are the major drivers for cryptocurrency adoption. All paths leading to cryptocurrency adoption were found to be significant in the hypothesized directions. The study also found that PGC moderates the relationship between adoption and continuance intention.

Originality/value

This study advances existing literature by empirically verifying the integrated UTAUT and ISSM in the context of cryptocurrency adoption for investment purposes. The findings offer crypto-developers and crypto-exchange insight into how adoption is diffusing in emerging markets. The findings provide policymakers with meaningful insights into the role of government regulations in cryptocurrency continuance intention.

The role of foreign trade and technology innovation on economic recovery in China: The mediating role of natural resources development

The coronavirus disease (COVID-19) has caused the most recent global economic collapse, which severely impacted worldwide economic operations. Natural resource volatility significantly affects global economic recovery. Therefore, the study aims to determine the significance of natural resource volatility, foreign trade, technological innovation, urbanisation, and investment in energy resources on the economic recovery of Chinese provinces. A total of 30 provinces in China were examined between 1995 and 2020. The generalised method of movement (GMM) technique was used to demonstrate that investments in energy sector resources, international commerce, and technological innovation are inconsistent than gross domestic product (GDP) and natural resources. The results demonstrated how trade blocs limit the effect of natural resources on regional economic development in the central provinces. Specifically, 33.4% of energy was saved while 35.2% of emissions were reduced. Although abundant natural resources significantly influence economic growth, studies discovered a negative impact on urbanisation. Nonetheless, the positive effects of trade openness outweighed those of economic recovery. The study proposed stabilising the fluctuating costs of natural resources, encouraging green financing, and increasing investment in energy resources. The findings also provided a novel strategy for achieving high economic growth and recovery.

Can gold and bitcoin hedge against the COVID-19 related news sentiment risk? New evidence from a NARDL approach

The COVID-19 pandemic has led to extensive news coverage, causing investor sentiment to swing, which has further increased financial market price volatility. There is an increasing need to find a hedge against sentiment risk. This paper examines the hedge capabilities of gold and Bitcoin against COVID-19-related news sentiment (CNS) risk under a nonlinear autoregressive distributed lag (NARDL) model. Our empirical results reveal that there is an obvious asymmetric effect from the CNS on gold prices in the short run and that the decrease in the COVID-19-related news index would have a greater impact on gold prices than when it increases. The impact of CNS on Bitcoin prices is asymmetric in the long and short term, especially in the long term. In addition, we conclude that gold is a hedge against CNS risk in the long term, and the hedging effect of Bitcoin is mainly reflected in the short-term.

The role of cryptocurrencies in predicting oil prices pre and during COVID-19 pandemic using machine learning

This study aims to explore the role of cryptocurrencies and the US dollar in predicting oil prices pre and during COVID-19 pandemic. The study uses three neural network models (i.e., Support vector machines, Multilayer Perceptron Neural Networks and Generalized regression neural networks (GRNN)) over the period from January 1, 2018, to July 5, 2021. Our results are threefold. First, our results indicate Bitcoin is the most influential in predicting oil prices during the bear and bull oil market before COVID-19 and during the downtrend during COVID-19. Second, COVID-19 variables became the most influential during the uptrend, especially the number of death cases. Third, our results also suggest that the most accurate model to predict the price of oil under the conditions of uncertainty that prevailed in the world during the bear and bull prices in the wake of COVID-19 is GRNN. Though the best prediction model under normal conditions before COVID-19 during an uptrend is SVM and during a downtrend is GRNN. Our results provide crucial evidence for investors, academics and policymakers, especially during global uncertainties.

Testing oil price volatility during Covid-19: Global economic impact

This paper is occasioned by the current events in the crude oil markets throughout the Covid pandemic time. The study analyzes the evolving nature of crude oil cost unpredictability caused by the variations that influence the crude sector throughout the current contagion. Every day's dataset is within the first month of 2020 and December 30, 2021 were measured by applying VAR and GARCH models. The results corroborate that the current contagion has adverse effects on the crude sector, primarily in two ways. It resulted in the headwinds for demand and cut international demand for crude oil, increasing uncertainty for major advanced and developing nations. Next, it resulted in output headwinds as the pandemic caused hydrocarbons conflicts among the leading crude supplying countries. The two headwinds seem to have caused the more than necessary crude unpredictability. Moreover, it was found that the United States output, total requirements, and crude-leaning demand shocks adversely affect the supply unpredictability of the United States and the extractive sectors. The findings depict that crude price instability responded significantly to the contagion caused by crude headwinds. Specifically, the study recorded the effect of uncertainty because of these headwinds beyond financiers' concerns about crude price instability. This study indicates that spillovers do not have meaningful forecast data, igniting critical debates concerning the relevance of the spillover indicator for predicting at minimal sampling occurrence.

On the dynamic return and volatility connectedness of cryptocurrency, crude oil, clean energy, and stock markets: a time-varying analysis

The high energy consumption of cryptocurrency transactions has raised concerns about the environment and sustainability among green investors and regulatory authorities. The current study examines the connectedness among clean energy, Bitcoin, the stock market, and crude oil empirically. The time-varying parameter vector autoregression (TVP-VAR) is used to estimate the dynamics of connectedness in a daily dataset spanning the period November 11, 2013 to September 30, 2021. We find that the clean energy and traditional stock markets transmit shocks to Bitcoin and oil in terms of return, and they receive shocks in terms of volatility from Bitcoin and oil. Additionally, Bitcoin and other financial markets are only tenuously linked during non-crisis periods. Nonetheless, their connection strengthens substantially during times of crisis, such as the great cryptocurrency crash of 2018 and the COVID-19 pandemic of 2020. We believe that these findings can help explain how clean energy and cryptocurrency markets are linked during times of crisis.

On the relationship between Bitcoin and other assets during the outbreak of coronavirus: Evidence from fractional cointegration analysis

This article tries to investigate the connectedness between Bitcoin and Crude Oil, S&P500 and Natural Gas with the health crisis. That is why one might apply fractional cointegration analysis on daily data over the period 01/09/2019-30/04/2020. Our results indicate the presence of fractional integration in residual series, implying the existence of a fractional cointegration relationship. A short-run joint dynamics between Bitcoin and some other assets (Crude Oil, S&P500 and Natural Gas) is nevertheless well-pronounced. Such analysis of the long and short-term dependencies between different assets could be interesting from a portfolio perspective.

How does COVID-19 influence dynamic spillover connectedness between cryptocurrencies? Evidence from non-parametric causality-in-quantiles techniques

This research examines the impact of the COVID-19 on cryptocurrencies' connectedness by employing two techniques: TVP-VAR-based connectedness and causality in the quantiles. First, the TVP-VAR-based connectedness unveils that cryptocurrencies act as a net receiver and transmitter of shocks, with Bitcoin, Ethereum are the highest transmitters among others. Moreover, the causality-in-quantile test shows that COVID-19 significantly causes spillover connectedness among cryptocurrencies, mainly at the quantiles ranging from 0.1 to 0.8, while an insignificant causal relationship is found in few cases. The study has implications for investors and policymakers.

Cryptocurrency as Epidemiologically Safe Means of Transactions: Diminishing Risk of SARS-CoV-2 Spread

In comparison with other respiratory viruses, the current COVID-19 pandemic’s rapid seizing the world can be attributed to indirect (contact) way of transmission of SARS-CoV-2 virus in addition to the regular airborne way. A significant part of indirect transmission is made through cash bank notes. SARS-CoV-2 remains on cash paper money for period around four times larger than influenza A virus and is absorbed by cash notes two and a half times more effectively than influenza A (our model). During the pandemic, cryptocurrencies have gained attractiveness as an “epidemiologically safe” means of transactions. On the basis of the authors’ gallop polls performed online with social networks users in 44 countries in 2020–2021 (the total number of clear responses after the set repair 32,115), around 14.7% of surveyed participants engaged in cryptocurrency-based transactions during the pandemic. This may be one of the reasons of significant rise of cryptocurrencies rates since mid-March 2020 till the end of 2021. The paper discusses the reasons for cryptocurrency attractiveness during the COVID-19 pandemic. Among them, there are fear of SARS-CoV-2 spread via cash contacts and the ability of the general population to mine cryptocurrencies. The article also provides a breakdown of the polled audience profile to determine the nationalities that have maximal level of trust to saving and transacting money as cryptocurrencies.