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This paper aims to empirically examine the effect of Coronavirus disease 2019 (COVID-19) pandemic on cryptocurrency market returns with particular attention to top five cryptocurrencies and COVID-19 confirmed and death cases.

The study applies the linear Toda and Yamamoto and nonlinear Diks and Panchenko Granger causality test to know the causal relationship of cryptocurrencies with COVID-19 pandemic. The study also uses the Narayan and Popp endogenous two structural break tests to capture the break period of the sample.

The findings of the study confirm the existence of unidirectional causal relation from COVID-19 confirmed and death cases to cryptocurrency price returns. While examining the break periods, the post-break period result indicates the presence of unidirectional linear causality from COVID-19 confirmed cases to Bitcoin and Ethereum price returns. This shows that prior knowledge of COVID-19 pandemic growth helps to predict the return of cryptocurrencies.

The study suggests the investors or crypto lovers to observe the growth of COVID-19 situations during their investment in cryptocurrency markets.

The purpose of this paper is to examine the price–volume relationship in the bitcoin market to validate near-stock properties of bitcoin.

Daily data of bitcoin returns, returns volatility and trading volume (TV) are utilized for the period August 17, 2010–April 16, 2017. Linear and non-linear causality tests are employed to examine price–volume relationship in the bitcoin market.

The linear causality analysis indicates that the bitcoin TV cannot be used to predict return; however, the reverse causality is significant. In contrast, the non-linear causality analysis shows that there are non-linear feedbacks between the bitcoin TV and returns. The bitcoin TV, which represents new information, leads to price changes, and large positive price changes lead to increased trading activity. Similarly, in recent periods (post-break period), the results of the non-linear causality test show a unidirectional causality from TV to the volatility of returns.

This study uses the average index value of major bitcoin exchanges. But further research on this relationship using data from different bitcoin exchanges may provide further insights into the price–volume relationship of bitcoin and its near-stock properties.

These findings from the non-linear causality analysis, therefore, suggest that investors cannot simply base their decisions on the linear dynamics of the bitcoin market. This is because new information in terms of the TV is neither linearly related to the price nor it is a one-to-one kind of relationship as most investors commonly understand it to be. Rather, investors’ decisions should be based on non-linear models, in general, and the best-fitting non-linear model, in particular.

The study examines bitcoin’s near-stock properties in a price–volume relationship framework with the help of both linear and non-linear causality tests, which to the best of the authors’ knowledge remains unexplored.

The purpose of this study is to examine the club convergence of Financial integration (FI) in the case of 60 countries from 1970 to 2015. FI plays a vital role in economic growth through sharing the risk between countries, cross-border capital association, investment and financial information. It also leads to the efficient allocation of capital and capital accumulation, thereby improving the systematic growth and productivity of the economy. Literature on examining the convergence hypothesis of FI is scarce.

This study applies the clustering algorithm to identify club convergence, advanced by the Phillips and Sul test, which enables the identification of multiple steady states or club convergence, unlike beta and sigma convergences.

The findings indicate the non-convergence when all 60 countries were taken together. This highlights that the selected countries' have unique transition paths in terms of FI. Hence, the authors implement the clustering algorithm, and the estimation shows that 56 countries are categorised into three different clubs. However, for the rest of four countries, the results are sort of ambiguous, favouring neither convergence nor divergence.

On the basis of three country clubs, Club 1 presents the model countries such as the Netherlands, Singapore and Switzerland. The Club 2 and Club 3 countries can start making moves towards the model countries by making policy adaptations for trade, finance and business facilitation.

The existing literature provides a plethora of studies investigating the convergence of stock markets, exchange rates and equity markets, but studies on the convergence of FI, particularly across the countries, are scarce. This study contributes by bridging this gap. The study is unique in its type as it takes into account the multiple steady states or club convergence. This study also contributes in policymaking by suggesting Club 1 countries (the Netherlands, Singapore and Switzerland) as the model ones for the FI.

The purpose of this study is to examine whether the electricity consumption patterns across Indian states do converge.

This study considers 18 Indian states spanning over the period 1970-1971 and 2014-2015, using the recently developed Phillips and Sul panel convergence technique that accounts the multiple steady states.

The results provide the following insights. First, the authors find evidence of convergence in electricity consumption among all Indian states. This suggests that electricity consumption patterns for Indian states are converging to a common steady state. Second, to provide broader insights, we further investigate the convergence in electricity consumption among user groups such as agriculture, industry, commercial, domestic and miscellaneous. The results reveal that commercial, domestic and miscellaneous groups are also converging. Third, the non-convergence patterns in agriculture and industry enable us to investigate the possibility of clubs or the multiple common steady states. The results indicate the occurrence of three clubs in case of agriculture and two clubs in case of the industry. Fourth, this study also inspects the relative speed of convergence among the user groups. The results reveal the higher speed of convergence in case of the domestic user group.

The findings enable policymakers to formulate an appropriate energy policy to accommodate the future electricity demand across Indian states and prioritize low electricity consumption states so that they receive a greater share.

This is the first study that examines the convergence in electricity consumption across Indian states at aggregate and user groups using a new panel club convergence technique.

This paper investigates the per-capita output club convergence in case of 120 countries for the period 1995–2015. Further, we disaggregate per-capita output into three broad sectors such as agriculture, industry, and service and investigate the convergence hypothesis.

The paper tests this hypothesis using the Phillips and Sul panel club convergence technique.

Our findings are as follows: (1) our results indicate the evidence of output divergence for the full sample; (2) when countries are divided into different clubs, the results exhibit the sign of per capita output club convergence both for aggregate and three major sectors. Further, this study confirms that industry's per capita output is the main driver for aggregate per-capita output club convergence in case of club 1. For club 2, agriculture's per capita output is a primary source for aggregate per capita output club convergence. Likewise, in the case of clubs 3 and 4, we find the service sector's per capita output is the main component for aggregate per-capita output club convergence; (3) both the service and industry sectors are major drivers for aggregate per-capita output club convergence.

This study suggests to the policymaker that sector-specific policies need to be adopted to boost the per-capita output growth by improving the performance of each of the sectors across the countries.

Notwithstanding, there are many studies that examine the output convergence using a notion of beta and sigma convergence, but studies regarding per capita output club convergence both at the aggregate and sectoral level are scanty.

The current study investigates the impact of the coronavirus disease 2019 (COVID-19) lockdown restrictions on air quality in an industrial town in Himachal Pradesh (HP) (India) and recommends policies and strategies for mitigating air pollution.

The air quality parameters under study are particulate matter₁₀ (PM₁₀), PM_2.5, SO₂ and NO₂. One-way ANOVA with post-hoc analysis and non-parametric Kruskal–Wallis test, and multiple linear regression analysis are used to validate the data analysis results.

The findings indicate that the lockdown and post-lockdown periods affected pollutant levels even after considering the meteorological conditions. Except for SO₂, all other air quality parameters dropped significantly throughout the lockdown period. Further, the industrial and transportation sectors are the primary sources of air pollution in Paonta Sahib.

Future research should include other industrial locations in the state to understand the relationship between regional air pollution levels and climate change. The findings of this study may add to the discussion on the role of adopting clean technologies and also provide directions for future research on improving air quality in the emerging industrial towns in India.

Very few studies have examined how the pandemic-induced lockdowns impacted air pollution levels in emerging industrial towns in India while also considering the confounding meteorological factors.