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Motivated by the ongoing debate on the existence and magnitude of herding in financial markets, the purpose of this paper is to examine Eurozone stock markets for herding behavior. In the context of the present study, the authors seek for herding behavior of stock markets as a whole as opposed to previous studies that examine herding on stock level.

To this end, the authors employ data on benchmark stock market indices for a long sample starting from 2000 through 2016. The testing procedure entails the standard Capital Asset Pricing Model-based procedure along with an advanced econometric method allowing the coefficients of the model to vary over time.

Results provide evidence in favor of negative herding behavior (anti-herding) for the Eurozone as a whole with noteworthy transitions. Further analysis reveals that stock markets of the periphery exhibit scarce evidence of herding, whereas continental countries are mainly characterized by negative herding behavior.

The present study’s main contribution is twofold. First, herding is examined not in sector or stock level as previous studies but at market level. Second, the testing methodology entails a pure time-varying regression model with stochastic volatility proposed by Nakajima (2011) that has not been previously employed in stock market herding. The results entail significant implications for investors seeking for diversification across Eurozone stock markets.

The purpose of this paper is to examine the value-at-risk and related measures for the Bitcoin and to compare the findings with Standard and Poor’s SP500 Index, and the gold spot price time series.

A GJR-GARCH model has been implemented, in which the residuals follow the standardized Pearson type-IV distribution. A large variety of value-at-risk measures and backtesting criteria are implemented.

Bitcoin is a highly volatile currency violating the value-at-risk measures more than the other assets. With respect to the Basel Committee on Banking Supervision Accords, a Bitcoin investor is subjected to higher capital requirements and capital allocation ratio.

The risk of an investor holding Bitcoins is measured and quantified via the regulatory framework practices.

This paper is the first comprehensive approach to the risk properties of Bitcoin.

The COVID-19 crisis has brought to the forefront the importance of rural health enterprises (RHEs), the peculiarity, in these terms, of rural areas, and the impact of rurality on health entrepreneurial activities. This paper aims to undertake a literature review regarding RHEs in the EU, identify research gaps and set future research directions.

A systematic literature review was conducted and the key aspects coded across four thematic areas – after examining 68 papers.

The findings reveal that more intense research should be conducted across four area which emerged; rural health providers vs urban health providers; RHEs and rural development; RHEs and quality of life; and social RHEs.

Future research avenues were identified and suggestions for further research on RHEs were provided.

The paper provides insights into how rural areas can attract health enterprises and how health enterprises can operate in rural areas.

This research expands on the limited existing knowledge of RHEs and sets the foundations for further research.

Our study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market.

We used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc.

Applying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market.

Our results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors.

Our study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.