The Structure Of Cryptocurrency Returns By Amin Shams :: SSRN

Fra Geowiki
Spring til navigation Spring til søgning


Last week, El Salvador’s government passed a law to accept bitcoin as legal tender alongside the US dollar. "We are committed to helping El Salvador in various ways, such as for currency transparency and regulatory processes," a World Bank spokesperson told Reuters. Adding the cryptocurrency to the roster isn’t a straightforward task, even though, and the new law provides the nation just 3 months to roll the plan out nationwide. The nation receives $6 billion in remittances per year-almost a quarter of its gross domestic solution-and the hope is that bitcoin’s reduce transaction charges could enhance that quantity by a few percentage points. To address those concerns, El Salvador turned to the World Bank and the International Monetary Fund for help the latter is at the moment contemplating a $1.3 billion financing request from the nation. No country has ever employed bitcoin or any other cryptocurrency as legal tender, and challenges abound. The World Bank was less generous. In other words, bitcoin’s energy demands and its ease of use in income laundering, tax evasion, and other illegal schemes tends to make the cryptocurrency a no-go in the eyes of the World Bank.

Abstract: As COVID-19 has been spreading across the planet since early 2020, a growing number of malicious campaigns are capitalizing the subject of COVID-19. To facilitate future research, we have released all the effectively-labelled scams to the analysis neighborhood. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. For every single type of scams, we additional investigated the tricks and social engineering procedures they employed. However, these newly emerging scams are poorly understood by our community. If you adored this article so you would like to get more info about click the up coming document please visit our own page. Then, we propose a hybrid strategy to perform the investigation by: 1) collecting reported scams in the wild and 2) detecting undisclosed ones primarily based on information collected from suspicious entities (e.g., domains, tweets, etc). We 1st develop a complete taxonomy of COVID-19 scams by manually analyzing the current scams reported by users from on the net resources. We have collected 195 confirmed COVID-19 cryptocurrency scams in total, like 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. COVID-19 themed cryptocurrency scams are increasingly preferred during the pandemic. We then identified over 200 blockchain addresses connected with these scams, which lead to at least 330K US dollars in losses from 6,329 victims.

This paper empirically offers assistance for fractional cointegration of high and low cryptocurrency cost series, utilizing particularly, Bitcoin, Ethereum, Litecoin and Ripple synchronized at diverse high time frequencies. The distinction of high and low price provides the price tag variety, and the range-primarily based estimator of volatility is more effective than the return-based estimator of realized volatility. A a lot more common fractional cointegration method applied is the Fractional Cointegrating Vector Autoregressive framework. It is therefore rather exciting to note that the fractional cointegration strategy presents a decrease measure of the persistence for the range compared to the fractional integration strategy, and the final results are insensitive to various time frequencies. The most important finding in this function serves as an alternative volatility estimation system in cryptocurrency and other assets' value modelling and forecasting. The final results show that higher and low cryptocurrency rates are actually cointegrated in both stationary and non-stationary levels that is, the variety of high-low price.

Abstract: Current studies in significant information analytics and natural language processing develop automatic approaches in analyzing sentiment in the social media facts. While earlier function has been created to analyze sentiment in English social media posts, we propose a system to determine the sentiment of the Chinese social media posts from the most common Chinese social media platform Sina-Weibo. We develop the pipeline to capture Weibo posts, describe the creation of the crypto-certain sentiment dictionary, and propose a lengthy short-term memory (LSTM) based recurrent neural network along with the historical cryptocurrency value movement to predict the price trend for future time frames. This analysis is directed to predicting the volatile value movement of cryptocurrency by analyzing the sentiment in social media and finding the correlation between them. In addition, the expanding user base of social media and the higher volume of posts also give useful sentiment details to predict the price tag fluctuation of the cryptocurrency. The conducted experiments demonstrate the proposed approach outperforms the state of the art auto regressive based model by 18.5% in precision and 15.4% in recall.