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1,518 results ¡ page 27 of 64
Natkamon Tovanich, RĂŠmy Cazabet
No abstract is available for this record.
Matteo Cavallaro, Alban Mathieu
No abstract is available for this record.
Yeray Mezquita, DĂŠvika PĂŠrez, Alfonso GonzĂĄlezâBriones, Javier Prieto
No abstract is available for this record.
Lin William Cong, Eswar Prasad, Daniel Rabetti
Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability.Oracles play key roles in decentralized finance and blockchain applications in centralized finance.We find that integration into decentralized oracle networks is positively associated with key measures of economic activity such as Total Value Locked, triggered by positive network effects in adoption and usage.Our study reveals symbiotic gains from enhanced interoperability and network effects across protocols on a given chain and among integrated chains.Oracle integration appears to improve risk-sharing and mitigates contagion, increasing resilience during turbulent periods in crypto markets.Overall, oracles emerge as a crucial component to enable informational and economic integration in decentralized finance ecosystems.
Louis Bertucci
No abstract is available for this record.
Hugo Almeida, Pedro Pinto, Ana FernĂĄndez Vilas
Cryptocurrencies are considered relevant assets and they are currently used as an investment or to carry out transactions. However, specific characteristics commonly associated with the cryptocurrencies such as irreversibility, immutability, decentralized architecture, absence of control authority, mobility, and pseudo-anonymity make them appealing for money laundering activities. Thus, the collection and characterization of current cryptocurrency-based methods used for money laundering are paramount to understanding the circulation flows of physical and digital money and preventing this illegal activity. In this paper, a collection of cryptocurrency transaction methods is presented and distributed through the money laundering life cycle. Each method is analyzed and classified according to the phase of money laundering it corresponds to. The result of this article may in the future help design efficient strategies to prevent illegal money laundering activities.
WeiâTek Tsai, Dong Yang, Zizheng Fan, Feng Zhang ¡ 7 authors
No abstract is available for this record.
Adla Padma, Mangayarkarasi Ramaiah
No abstract is available for this record.
Muhammed Zakir Hossain
No abstract is available for this record.
Imtiaz Sifat, Denise van Donselaar, Syed Ahzam Tariq
No abstract is available for this record.
Woochang Hyun, Jaehong Lee, Bongwon Suh
No abstract is available for this record.
Jinsha Zhao, Jia Miao
Using Russian-Ukraine war as an exogenous event, we investigate whether Bitcoin is used to evade financial sanctions. We follow three avenues to explore this problem. First, we investigate Bitcoin trading volume pre- and post- Russia's invasion. Second, we explored price and return relationships between Bitcoin and other major asset classes during the same period. Lastly, we investigate the associations between Bitcoin trading volume and Russia oil export by sea. Overall, our results suggest that Bitcoin is not used to evade sanctions in large scale.
Andrea Podhorsky
This paper develops a theoretical model of the bitcoin market and demonstrates that the bitcoinâs volatile and explosive price path is a consequence of the Bitcoin protocolâs system of supply management. The model implies that the marginal cost of mining the target supply of bitcoins is the fundamental value of the bitcoin since it corresponds to an equilibrium in the Bitcoin protocol and the rent-seeking tournament among miners. The data provide strong empirical evidence of cointegration between the bitcoinâs price and the marginal cost of mining the target supply of bitcoins, demonstrating the existence of their long-run equilibrium relationship. Current bubble detection techniques indicate that there is no evidence of explosive departures in the price of the bitcoin from its model-implied fundamental value. Since the raw price data exhibit explosive behavior, the apparent bubbles in the price of the bitcoin can be attributed to its nonstationary market fundamentals. ⢠The bitcoinâs price dynamics result from the protocolâs interference in the market. ⢠The Bitcoin protocol works against the self-correcting mechanism of the market. ⢠Adjustments of the difficulty result in volatile and explosive behavior in the price. ⢠There is cointegration between the bitcoinâs price and the marginal cost of mining. ⢠Apparent bubbles in the price can be attributed to nonstationary market fundamentals.
Joshua S. Gans
No abstract is available for this record.
Lin William Cong, Kimberly Grauer, Daniel Rabetti, Henry Updegrave
We provide an overview of crypto-related scams, including investment scams, Ponzi schemes, and more recently, rug pulls that are commonly seen in Decentralized Finance (DeFi) projects. We then discuss data sources for studying Initial Coin Offering (ICO) scams, before examining the case of PlusToken, the largest crypto scam, AnubisDAO, the prototypical rug pull, and Luno's anti-scam initiative, a good prototype for other cryptocurrency exchanges and service entities to follow. User protection and education are crucial in preventing scams, despite the fact that they may require efforts from centralized entities and regulators.
Sandeep Kumar Panda, A. R. Sathya, Sukanta Das
No abstract is available for this record.
Lars Hornuf, Paul P. Momtaz, Rachel J. Nam, Ye Yuan
We examine how cybercrime impacts victimsâ risk-taking and returns. Our difference-indifferences analysis of a sample of victims and matched non-victims is in line with prospect theory and suggests that victims increase their long-term total risk-taking after losing part of their wealth. Victims also earn lower risk-adjusted returns in the post-cybercrime period. Victimsâ long-term total risk-taking increases because they increase diversifiable risk in the long term. The increased diversifiable risk correlates with victimsâ withdrawal from altcoins after cybercrime. At the same time, the reduction in risk-adjusted returns correlates with increased trading activity and churn, due plausibly to managing cybercrime exposure. In the cross-section of Ethereum addresses, we show that the most affluent victims take a systematic approach to restore their pre-cybercrime wealth level, while the least affluent victims turn into gamblers. Finally, a parsimonious forensic model explains a good part of the addressesâ probability of being involved in cybercrime, on both the victim and the cybercriminal side.
Mohammad Khalid Khawrin, Nooman Zadran, Ahmad Helal
The world is on the brink of rewriting business and monitoring history. It is very crucial to mention academically how Afghan crypto-monitory transactions are taking place. even though it is a soft threat to official government organizations via tax evasion, money laundering, and terrorism financing. The qualitative method with content analysis was applied because the data was in textual form. The data was analyzed through Atlis.ti 9. First of all, the interviews were coded, and then themes were created. The result showed that Bitcoin and Binance had the most users, and there were six types of cryptocurrencies in Afghanistan. Furthermore, the advantages and disadvantages were highlighted. Lastly, it was highly suggested that the Afghan government have specific laws for general protection and to gain the benefits of the new world of high technology.
Ruolei Zhang
With the volatile price of Bitcoin having received widespread media and investor attention in recent years, this paper surveys the existing literature on Bitcoin which focuses on the differences between Bitcoin and traditional currencies and the impact on the real economy. In doing so, various literature and scholarly insights are discussed to formally clarify the economic implications of Bitcoin in the absence of a centralised entity. A SWOT approach is then used to analyse its strengths, weaknesses, opportunities and risks, before parsing Bitcoin's price trends and making predictions for the future based on specific events and news. Society remains sceptical and uninformed about this cryptocurrency. The evidence suggests that bitcoin returns are significantly slightly inefficient at this stage, but there is also a great deal of uncertainty about the future and it could be on the move.
Firuze Simay Sezgin, Caner Ăzdurak
This study investigates the impact of terrorist attacks on the price fluctuations of Bitcoin prices and NFT sales. Although the value proposition of cryptocurrencies, Decentralized Finance, and the whole blockchain revolution is a quicker, cheaper, and more transparent kind of finance, various terrorist organizations tend to use cryptocurrency anonymously to finance their terrorist activities around the world by bypassing the banking system of the regulated countries. The analyses reveal that returns of Bitcoin and NFT markets are positively associated with the organization and funding phases of the terrorist attacks but negatively associated with the post-terrorist attack circumstances, meaning that it generates positive abnormal returns (AR) prior to the attack but creates negative AR right after the attack. Furthermore, while the Bitcoin news impact curve (NIC) is nearly symmetric, the NFT NIC is asymmetric, with positive shocks having significantly more impact on future volatility than negative shocks of the same magnitude. Since previous studies claim that terrorist attack news is good news for Bitcoin returns, we will enrich our AR analysis results with NICs results.
Daniela Penela
Bitcoin is a virtual currency that provides a completely decentralized secure alternative to the currencies currently used. Nakamoto, the creator of this cryptocurrency, published an article on an encryption mailing list in 2008 with the title âBitcoin: A Peer-to-Peer Electronic Cash Systemâ, thus giving the creation of this virtual currency. This study aims to analyze the Bitcoin and what factors can influence its price, in the context of a pandemic. This work will focus on the bitcoin price and on five different factors likely to have an influence on his price, such as: Hash Rate, Mining Difficulty, Volatility Index, Google Search and Transaction Cost. The period for this research ranges from 15/03/2020 to 14/11/2021, a total of 96 weeks, to integrate the covid-19 factor into the study. The results show that the variables fsCoinCirculation and fsTransationCost are both necessary conditions for an increase on the bitcoin price, but for low values of bitcoin price there are no necessary conditions. Additionally, findings suggest that Hash Rate influences the price of bitcoin. Finally, fsVix variable was found to be a variable with an important implication in price, namely, in its volatility.
Ayuba Napari, Ä°nci Parlaktuna
Owing to the high penetration of cryptocurrencies in the Turkish Economy, we sought to determine whether cryptocurrencies as represented by Bitcoin has become a global risk for the Turkish Lira. To accomplish this, we model the Turkish Lira exchange rate returns volatility using threshold GARCH-M with Bitcoin as an exogenous covariate. Bitcoin was found to be a contributor to Turkish forex volatility up until January 2018 when the âongoingâ currency crisis started. Bitcoin, however, lost its volatility contributory power from January 2018. This result is robust to the inclusion of CBOE-VIX, iShares MSCI Turkey EFT, and the dollar-lira interest rate differential as control variables.
Destan KÄąrÄąmhan
No abstract is available for this record.
Bui Tong Nha, Nguyáť n ÄĂŹnh Thuân
Smart contracts have become increasingly popular in the development of trustworthy decentralized applications in recent years. These tools compare vulnerable contracts to a set of predefined rules. However, the emergence of new vulnerable types and programming skills to mitigate potential vulnerabilities results in many false positive and false negative tool reports. To address this, this data was analyzed using unsupervised machine learning to determine whether an algorithm can distinguish between shady/illegal owners and clean owners. Clustering algorithms are used in this paper. However, algorithms can cluster objects and detect fraud activity in Bitcoin transactions. Research on bitcoin network anomalies and suspicious transactions seeks to identify anomalous transactions, when all nodes on the bitcoin network are unlabeled. There is no evidence that any transaction is illegal. We are primarily interested in discovering irregularities in the bitcoin transaction network.