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The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility.

The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t-distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021).

Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC.

Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.

One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.

Considering the unique data of the gold investor sentiment index in Thailand, the purpose of this paper is to investigate the bivariate dynamic relationship between the gold investor sentiment index and stock market return, as well as that between the gold investor sentiment index and stock market volatility, using the panel vector autoregression (PVAR) methodology. The author presents and discusses the findings both for the full sample and at the industry level. The results support prior literature that stocks in different industries do not react similarly to investor sentiment.

The PVAR methodology with the GMM estimation is found to be superior to other static panel methodologies due to considering both unobservable time-invariant and time-variant factors, as well as being suitable for relatively short time periods. The panel data approach improves the statistical power of the tests and ensures more reliable results.

In general, a negative and unidirectional association from gold investor sentiment to stock returns is observed. However, the gold sentiment-stock realized volatility relationship is negative and bidirectional, and there exists a greater impact of a stock’s realized volatility on gold investor sentiment. Importantly, evidence at the industry level is stronger than that at the aggregate level in both return and volatility cases, confirming the role of gold investor sentiment in the Thai stock market. The capital flow effect and the contagion effect explain the gold sentiment-stock return relationship and the gold sentiment-stock volatility relationship, respectively.

The gold price sentiment index can be used as a factor for stock return predictability and stock realized volatility predictability in the Thai equity market.

Practitioners and traders can employ the gold price sentiment index to make a profit in the stock market in Thailand.

This is the first paper to use panel data to investigate the relationships between the gold investor sentiment and stock returns and between the gold investor sentiment and stocks’ realized volatility, respectively.

The level of trust in a service provider is an important decision factor when buying industrial services. Especially, the outcome uncertainties of services, its individuality, and asymmetric information between buyer and seller are some reasons that the evaluation of trust is a key component in service trading. Consequently, searching of potential new suppliers involves examining providers’ trustworthiness. This paper focuses on the study of online trust signals used by buyers, to assess provider’s trustworthiness in the context of industrial services. The main research objective is to propose the basis for a digital tool, which helps buyers to assess provider’s trustworthiness by providing a “standardized trustworthiness signal description” and “trust functionalities.” A particular approach is used, wherein different methods are mixed such as a case study, expert interviews, and a quantitative method following the guideline of the design science paradigm. The aim is to propose a useful tool for trustworthiness assessment to enhance e-markets for industrial services.

The study of the diffusion of innovations into libraries has become a cottage industry of sorts, as libraries have always provided a fascinating test-bed of nonprofit institutions attempting improvement through the use of new policies, practices, and assorted apparatus (Malinconico, 1997). For example, Paul Sturges (1996) has focused on the evolution of public library services over the course of 70 years across England, while Verna Pungitore (1995) presented the development of standardization of library planning policies in contemporary America. For the past several decades, however, the study of diffusion in libraries has tended to focus on the implementation of information technologies (e.g., Clayton, 1997; Tran, 2005; White, 2001) and their associated competencies (e.g., Marshall, 1990; Wildemuth, 1992), the improvements in performance associated with their use (e.g., Damanpour, 1985, 1988; Damanpour & Evan, 1984), and ways to manage resistance to technological changes within the library environment (e.g., Weiner, 2003).