Résultats de recherche pour « Cryptocurrencies »

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms of five criteria. Conclusions are drawn on the best fitting models, forecasts and acceptability of value at risk estimates.

We model a cryptocurrency as membership in a decentralized digital platform developed to facilitate transactions between users of certain goods or services.The rigidity induced by the cryptocurrency price having to clear membership demand with supply of token by speculators, especially with strong complementarity in membership demand, can lead to market breakdown.While user optimism mitigates the market fragility by increasing user participation, speculator sentiment exacerbates it by crowding users out.Informational frictions attenuate the risk of breakdown by dampening price volatility and platform performance.Furthermore, the users' anticipation of losses from strategic attacks by miners exacerbates the market fragility.

We have developed and made available a new Cryptocurrency Uncertainty Index (UCRY) based on news coverage. Our UCRY Index captures two types of uncertainty: that of the price of cryptocurrency (UCRY Price) and uncertainty of cryptocurrency policy (UCRY Policy). We show that the constructed index exhibits distinct movements around major events in cryptocurrency space. We suggest that this index captures uncertainty beyond Bitcoin, and can be used for academic, policy, and practice-driven research.

Retrieving a set of 143 cryptocurrencies for a sample spanning 2014–2018, we investigate the popular momentum strategy implemented in the cryptocurrency market. Contrary to earlier studies our findings do not indicate any evidence of significant momentum payoffs, supporting the view that the cryptocurrency market is far more efficient than suggested in earlier studies.

Abstract We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.

We analyze statistical properties of the largest cryptocurrencies (determined by market capitalization), of which Bitcoin is the most prominent example. We characterize their exchange rates versus the U.S. Dollar by fitting parametric distributions to them. It is shown that returns are clearly non-normal, however, no single distribution fits well jointly to all the cryptocurrencies analysed. We find that for the most popular currencies, such as Bitcoin and Litecoin, the generalized hyperbolic distribution gives the best fit, while for the smaller cryptocurrencies the normal inverse Gaussian distribution, generalized t distribution, and Laplace distribution give good fits. The results are important for investment and risk management purposes.

Bitcoin was the first cryptocurrency to use blockchain and has been the market leader since the first bitcoin was mined in 2009. After the birth of Bitcoin with the genesis block, more than 1,000 altcoins and crypto-tokens have been created, with at least 919 trading actively on unregulated or registered exchanges. This entire class of cryptocurrencies and tokens has been classified by some tax authorities as having the same status as commodities. If cryptocurrency is viewed in the same class as commodities, how different is it in terms of its risk and return structure? This article sets out to help readers understand cryptocurrencies and to explore their risk and return characteristics using a portfolio of cryptocurrency represented by the Cryptocurrency Index (CRIX). Substantial discussions are centered on Bitcoin and its close variants. Some questions are raised about the potential of cryptocurrencies as an investment class. Results show that the return correlations between cryptocurrencies and traditional assets are low and that adding CRIX returns to a traditional asset portfolio improves risk–return performance. Sentiment analysis also indicates the CRIX has a relatively high Sharpe ratio. Although we should view the results with care, a new form of financing for cryptocurrency and blockchain start-ups is born. The disruption brought about by Bitcoin may be felt beyond payments through what is known as initial crypto-token offerings or initial token sales. TOPICS:Currency, risk management, performance measurement, mutual funds/passive investing/indexing

The papers in this special issue focus on the emerging phenomenon of cryptocurrencies. Cryptocurrencies are digital financial assets, for which ownership and transfers of ownership are guaranteed by a cryptographic decentralized technology. The rise of cryptocurrencies’ value on the market and the growing popularity around the world open a number of challenges and concerns for business and industrial economics. Using the lenses of both neoclassical and behavioral theories, this introductory article discusses the main trends in the academic research related to cryptocurrencies and highlights the contributions of the selected works to the literature. A particular emphasis is on socio-economic, misconduct and sustainability issues. We posit that cryptocurrencies may perform some useful functions and add economic value, but there are reasons to favor the regulation of the market. While this would go against the original libertarian rationale behind cryptocurrencies, it appears a necessary step to improve social welfare.