The Structure Of Cryptocurrency Returns By Amin Shams :: SSRN

Last week, El Salvador’s government passed a law to accept bitcoin as legal tender alongside the US dollar. "We are committed to assisting El Salvador in several methods, which includes for currency transparency and regulatory processes," a World Bank spokesperson told Reuters. Adding the cryptocurrency to the roster is not a very simple job, although, and the new law gives the nation just 3 months to roll the plan out nationwide. The country receives $6 billion in remittances per year-almost a quarter of its gross domestic solution-and the hope is that bitcoin’s decrease transaction charges could boost that quantity by a few percentage points. To address these issues, El Salvador turned to the World Bank and the International Monetary Fund for assistance the latter is presently considering a $1.3 billion financing request from the country. No nation has ever made use of bitcoin or any other cryptocurrency as legal tender, and challenges abound. The World Bank was much less generous. In other words, bitcoin’s energy demands and its ease of use in dollars laundering, tax evasion, and other illegal schemes makes the cryptocurrency a no-go in the eyes of the World Bank.

Abstract: As COVID-19 has been spreading across the globe given that early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. To facilitate future investigation, we have released all the properly-labelled scams to the analysis community. In this paper, we present the very first measurement study of COVID-19 themed cryptocurrency scams. For every variety of scams, we additional investigated the tricks and social engineering tactics they utilized. If you liked this information and you would such as to get even more facts pertaining to Blockforums.Org kindly go to our website. However, these newly emerging scams are poorly understood by our community. Then, we propose a hybrid method to execute the investigation by: 1) collecting reported scams in the wild and 2) detecting undisclosed ones based on details collected from suspicious entities (e.g., domains, tweets, and so forth). We very first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from on the internet 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 well-known during the pandemic. We then identified over 200 blockchain addresses linked with these scams, which lead to at least 330K US dollars in losses from 6,329 victims.

This paper empirically supplies help for fractional cointegration of high and low cryptocurrency value series, using particularly, Bitcoin, Ethereum, Litecoin and Ripple synchronized at different high time frequencies. The difference of high and low price gives the value variety, and the range-based estimator of volatility is additional efficient than the return-primarily based estimator of realized volatility. A far more basic fractional cointegration technique applied is the Fractional Cointegrating Vector Autoregressive framework. It is consequently rather exciting to note that the fractional cointegration method presents a reduced measure of the persistence for the range compared to the fractional integration approach, and the final results are insensitive to distinctive time frequencies. The key locating in this perform serves as an option volatility estimation technique in cryptocurrency and other assets' cost modelling and forecasting. The outcomes show that higher and low cryptocurrency costs are essentially cointegrated in each stationary and non-stationary levels that is, the variety of high-low price.

Abstract: Recent studies in big data analytics and all-natural language processing develop automatic approaches in analyzing sentiment in the social media facts. Although preceding work has been developed to analyze sentiment in English social media posts, we propose a technique to recognize the sentiment of the Chinese social media posts from the most preferred 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 quick-term memory (LSTM) primarily based recurrent neural network along with the historical cryptocurrency cost movement to predict the price tag trend for future time frames. This study is directed to predicting the volatile price tag movement of cryptocurrency by analyzing the sentiment in social media and obtaining the correlation amongst them. In addition, the increasing user base of social media and the high volume of posts also deliver useful sentiment information and facts to predict the cost fluctuation of the cryptocurrency. The conducted experiments demonstrate the proposed method outperforms the state of the art auto regressive primarily based model by 18.5% in precision and 15.4% in recall.