Since the launch of Bitcoin, there has been a lot of controversy surrounding what asset class it is. Several authors recognize the potential of cryptocurrencies but also certain deviations with respect to the functions of a conventional currency. Instead, Bitcoin’s diversifying factor and its high return potential have generated the attention of portfolio managers. In this context, understanding how its volatility is explained is a critical element of investor decision-making. By modeling the volatility of classic assets, nonlinear models such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) offer suitable results. Therefore, taking GARCH(1,1) as a reference point, the main aim of this study is to model and assess the relationship between the Bitcoin volatility and key financial environment variables through a Conditional Correlation (CC) Multivariate GARCH (MGARCH) approach. For this, several commodities, exchange rates, stock market indices, and company stocks linked to cryptocurrencies have been tested. The results obtained show certain heterogeneity in the fit of the different variables, highlighting the uncorrelation with respect to traditional safe haven assets such as gold and oil. Focusing on the CC-MGARCH model, a better behavior of the dynamic conditional correlation is found compared to the constant.
This article analyzes the influence of Spanish citizens’ computer knowledge and level of education on their tolerance of, or aversion to, risk in the online purchase of goods and services. To this end, annual survey data from Spain’s National Statistics Institute were used to know the incidence in e-commerce of the following variables: “computer knowledge,” “electronic commerce,” and “socioeconomic characteristics.” A method based on the design of a computer knowledge indicator was used, followed by a bivariate logistic regression to determine computer knowledge and level of education. The results show that the higher the level of computer knowledge, the greater the impulse to buy online and the higher the tolerance of risk. There was also a positive relation between level of education and propensity to buy; the higher the level of education, the greater the propensity.
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