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 helping El Salvador in quite a few techniques, like for currency transparency and regulatory processes,” a World Bank spokesperson told Reuters. Adding the cryptocurrency to the roster isn’t a simple task, though, and the new law provides the nation just three months to roll the strategy out nationwide. The country receives $6 billion in remittances per year-almost a quarter of its gross domestic product-and the hope is that bitcoin’s decrease transaction expenses could enhance that amount by a couple of percentage points. To address these issues, El Salvador turned to the World Bank and the International Monetary Fund for help the latter is currently taking into consideration a $1.3 billion financing request from the nation. No country has ever utilised bitcoin or any other cryptocurrency as legal tender, and challenges abound. The World Bank was much less generous. In other words, bitcoin’s power 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 due to the fact early 2020, a expanding number of malicious campaigns are capitalizing the topic of COVID-19. To facilitate future analysis, we have released all the well-labelled scams to the research community. In this paper, we present the initial measurement study of COVID-19 themed cryptocurrency scams. For each form of scams, we additional investigated the tricks and social engineering procedures they applied. However, these newly emerging scams are poorly understood by our neighborhood. Then, we propose a hybrid approach to execute the investigation by: 1) collecting reported scams in the wild and 2) detecting undisclosed ones based on facts collected from suspicious entities (e.g., domains, tweets, etc). We 1st create a extensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by customers from on the net sources. We have collected 195 confirmed COVID-19 cryptocurrency scams in total, such as 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 throughout the pandemic. We then identified over 200 blockchain addresses related with these scams, which lead to at least 330K US dollars in losses from 6,329 victims.

This paper empirically delivers support for fractional cointegration of high and low cryptocurrency value series, employing especially, Bitcoin, Ethereum, Litecoin and Ripple synchronized at distinctive higher time frequencies. The difference of high and low price tag provides the price variety, and the range-primarily based estimator of volatility is more efficient than the return-primarily based estimator of realized volatility. A much more basic fractional cointegration approach applied is the Fractional Cointegrating Vector Autoregressive framework. It is as a result quite fascinating to note that the fractional cointegration approach presents a reduce 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 obtaining in this perform serves as an alternative volatility estimation method in cryptocurrency and other assets’ value modelling and forecasting. The benefits show that higher and low cryptocurrency rates are in fact cointegrated in both stationary and non-stationary levels that is, the range of higher-low cost.

Abstract: Current studies in major information analytics and natural language processing create automatic procedures in analyzing sentiment in the social media facts. Although prior work 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 well-liked 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 cost trend for future time frames. This analysis is directed to predicting the volatile price movement of cryptocurrency by analyzing the sentiment in social media and acquiring the correlation between them. In addition, the expanding user base of social media and the higher volume of posts also give precious sentiment data to predict the price tag fluctuation of the cryptocurrency. The performed 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.

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