While stablecoins such as Tether closely track the peg, there is some evidence for recurring spikes in stablecoins’ intraday volatilities rendering stablecoin volatilities unstable (Grobys et al., 2021). Using the Barndorff-Nielsen and Shephard (2006a) methodology, the purpose of our study is to examine whether jumps in Tether have an impact on (subsequent) Bitcoin returns. We retrieve hourly data for Bitcoin and Tether from Bitfinex covering the November 2018 to June 2021 period and encode the binary choice (1 – ‘jump’ and 0 – ‘no jump’) using bi-power variation based on asymptotic distribution theory at 5% significance level for each trading day. Our results show that the joint effect of positive jumps in Tether in association with an 1% increase in Tether returns on the prior day significantly predict negative prices changes in Bitcoin ranging from -3.65% to -8.49% in daily terms. Our results remain robust even after controlling for various other variables.
Bitcoin market's efficiency and liquidity questions are being comprehensively analyzed in scientific literature. This dataset serves academics for deeper analysis of these topics as well as it gives relevant information for spotting and evaluating risks in the market. Moreover, practitioners can benefit from the dataset and use it to identify patterns in the market, discover potential earning capabilities, and create effective arbitrage trading strategies. This is the first publicly available dataset that provides unique arbitrage data about pairs of cryptocurrency exchanges. The raw dataset was received by the Bitlocus LT, UAB. Using dplyr, reshape2, plyr packages in R we transformed dataset to show the amount of arbitrage which could be earned in 13 different cryptocurrency exchanges from 2019-01-01 to 2020-04-01. We used this dataset to create matrices for each day from 2019-01-01 to 2020-04-01 in order to perform network analysis on Bitcoin arbitrage opportunities (Bruzgė and Šapkauskienė [1]). However, this dataset is beneficial for other purposes such as the evaluation of market's seasonality and day of week effects. The dataset provides values in high-frequency intervals but it is possible to convert data to a suitable data format depending on the research question.
Cryptocurrencies often tend to maintain a publically accessible ledger of all transactions. This open nature of the transactional ledger allows us to gain macroeconomic insight into the USD 1 Trillion crypto economy. In this paper, we explore the free market-based economy of eight major cryptocurrencies: Bitcoin, Ethereum, Bitcoin Cash, Dash, Litecoin, ZCash, Dogecoin, and Ethereum Classic. We specifically focus on the aspect of wealth distribution within these cryptocurrencies as understanding wealth concentration allows us to highlight potential information security implications associated with wealth concentration. We also draw a parallel between the crypto economies and real-world economies. To adequately address these two points, we devise a generic econometric analysis schema for cryptocurrencies. Through this schema, we report on two primary econometric measures: Gini value and Nakamoto Index which report on wealth inequality and 51% wealth concentration respectively. Our analysis reports that, despite the heavy emphasis on decentralization in cryptocurrencies, the wealth distribution remains in-line with the real-world economies, with the exception of Dash. We also report that 3 of the observed cryptocurrencies (Dogecoin, ZCash, and Ethereum Classic) violate the honest majority assumption with less than 100 participants controlling over 51% wealth in the ecosystem, potentially indicating a security threat. This suggests that the free-market fundamentalism doctrine may be inadequate in countering wealth inequality within a crypto-economic context: Algorithmically driven free-market implementation of these cryptocurrencies may eventually lead to wealth inequality similar to those observed in real-world economies.
Andrii Bielinskyi, Oleksandr Serdyuk, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов · 6 authors
Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.
We explore the impact of investor attention on idiosyncratic risk in the cryptocurrency markets. Taking the Google Trends Index as the measure of investor attention, we find that investor attention can significantly reduce cryptocurrencies’ idiosyncratic risks by increasing the liquidity. We further study possible cross-sectional variations of the effect of investor attention on idiosyncratic risk. Evidence shows that the investor attention effect is more pronounced for smaller-cap and younger cryptocurrencies. Moreover, a relatively stable external market environment and rising market state are conducive to the further play of the attention effect.
In this paper, we measure the risk interdependence of 12 major cryptocurrencies before and during the COVID-19 pandemic, based on a GARCH-Copula-VaR approach and a dynamic network analysis. We find that cryptocurrencies generally show high levels of volatility, speculation, homogeneity and tail risk contagion. Furthermore, the COVID-19 pandemic has a continuous impact on the cryptocurrency market. When financial institutions are increasingly investing in crypto assets, the hidden risks in the cryptocurrency market remain high. Therefore, this paper calls for attention on the cryptocurrency market from both investors and regulators.
Sampat Kumar U, S P Aanandhi, S P Akhilaa, Vijayakumar Vardarajan · 5 authors
Cryptocurrency is a tangible or digital currency protected with the help of Cryptography, making it almost impossible to counterfeit or double. Many cryptocurrency networks are categorized primarily based on blockchain technology. The present socio-economic situation also creates an environment for people to hold less cash and remain marginalized by the market trends. The objective of the project is to build a profitable Machine Learning prediction model. We begin by collecting the data from Yahoo Finance website using inbuilt python libraries. Our objective was to perform price prediction of various Cryptocurrencies using Machine Learning, and we have implemented the Autoregressive Integrated Moving Average (ARIMA) Model. We have performed feature engineering on various set of lagged values, on previous day, one for 7 days and another looking back for 30 days. We have forecasted the outcome of the model and plotted the outcome in a responsive chart using Plot graph.
This paper investigates the effectiveness of candlestick patterns in cryptocurrency trading. Our data set includes historical daily opening, high, low, and closing prices of the top 23 cryptocurrencies by market capitalization. We examine 68 commonly used candlestick patterns using statistical analysis and find that the studied candlestick patterns are of little use in cryptocurrency trading. On the contrary, there are more patterns with relatively low accuracy. Investors should be cautious with their trading strategies and decisions when these patterns appear, as they may be a false trading signal that could cause losses rather than gains. To the best of our knowledge, this paper is one of the first research studies to investigate the effectiveness of candlestick patterns in cryptocurrency trading. Our findings could serve as a reference for investors when developing cryptocurrency trading strategies.
Jarosław Kwapień, Marcin Wątorek, Stanisław Drożdż
Time series of price returns for 80 of the most liquid cryptocurrencies listed on Binance are investigated for the presence of detrended cross-correlations. A spectral analysis of the detrended correlation matrix and a topological analysis of the minimal spanning trees calculated based on this matrix are applied for different positions of a moving window. The cryptocurrencies become more strongly cross-correlated among themselves than they used to be before. The average cross-correlations increase with time on a specific time scale in a way that resembles the Epps effect amplification when going from past to present. The minimal spanning trees also change their topology and, for the short time scales, they become more centralized with increasing maximum node degrees, while for the long time scales they become more distributed, but also more correlated at the same time. Apart from the inter-market dependencies, the detrended cross-correlations between the cryptocurrency market and some traditional markets, like the stock markets, commodity markets, and Forex, are also analyzed. The cryptocurrency market shows higher levels of cross-correlations with the other markets during the same turbulent periods, in which it is strongly cross-correlated itself.
Meng Qin, Tong Wu, Ran Tao, Chi‐Wei Su · 5 authors
This paper clarifies the association between the Sino-U.S. bilateral relation (BR) and Bitcoin price (BCP) by applying the bootstrap full- and sub-sample Granger causality tests. It reveals that BR has positive and negative effects on BCP. The negative impact points out that Bitcoin is viewed as a tool to avoid uncertainties caused by the deterioration of BR, also proving that the strained relation between China and the U.S. can stimulate the Bitcoin market. However, this opinion is not held under a positive impact, the main explanation is that the burst of bubble weakens its ability to hedge risks. The above conclusion is not consistent with the theoretical model, underlining that the Bitcoin market is boosted by the deterioration of BR. Conversely, there is a negative influence from BCP to BR, meaning that the relationship between China and the U.S. can be reflected by the Bitcoin market. Under the complex and volatile international situation, investors can benefit from this investigation to compensate for the losses and keep their wealth. Also, it helps the related authorities to create a stable investment environment and promote friendly bilateral relations.
Marco Ortu, Stefano Vacca, Giuseppe Destefanis, Claudio Conversano
We analyse, using a mixture of statistical models and natural language process techniques, what happened in social media from June 2019 onwards to understand the relationships between Cryptocurrencies’ prices and social media, focusing on the rise of the Bitcoin and Ethereum prices. In particular, we identify and model the relationship between the cryptocurrencies market price changes, and sentiment and topic discussion occurrences on social media, using Hawkes’ Model. We find that some topics occurrences and rise of sentiment in social media precedes certain types of price movements. Specifically, discussions concerning governments, trading, and Ethereum cryptocurrency as an exchange currency appear to negatively affect Bitcoin and Ethereum prices. Those concerning investments, appear to explain price rises, whilst discussions related to new decentralized realities and technological applications explain price falls. Finally, we validate our model using a real case study: the already famous case of ”Wallstreetbet and GameStop”1 that took place in January 2021.
Youcef Maouchi, Lanouar Charfeddine, Ghassen El Montasser
This paper investigates digital financial bubbles amidst the COVID-19 pandemic. Using a sample of 9 DeFi tokens, 3 NFTs, Bitcoin, and Ethereum, we detect several bubbles overlapping the examined cryptoassets. We also uncover DeFi and NFT-specific bubbles in Summer 2020 suggesting distinct driving factors for this class of assets. We document that DeFi and NFTs bubbles are less recurrent but have higher magnitudes than cryptocurrencies' bubbles. We also find that COVID-19 and trading volume exacerbate bubble occurrences, while Total Value Locked (TVL) is negatively associated with cryptoassets' bubbles. Our results suggest that TVL can be used as a tool for market monitoring.
Chinchu Thomas, Zillah Watson, M Kim, Anushuya Baidya · 8 authors
Unlike typical banking transactions, blockchain-assisted cryptocurrencies are touted as the currency of the future, allowing peer-to-peer transactions without the need for an intermediary [1]. According to investors, the crypto share market has grown significantly in terms of market capacity, increasing by 300 percent in a year to approximately 1.6 trillion dollars [2]. Crypto investments, on the other hand, are thought to be dangerous given the crypto market's extremely volatile, latent, and non-stationary nature [3]. Stakeholders and investors may be able to easily incorporate crypto into their investment strategy if they can accurately predict the temporal change of the market price over time. In order to anticipate future prices, machine learning (ML) and big data analytics are extremely effective in deciphering stochastic and nonlinear patterns within market data [4].
The multi-chain future is upon us. Modular architectures are coming to\nmaturity across the ecosystem to scale bandwidth and throughput of\ncryptocurrency. One example of such is the Ethereum modular architecture, with\nits beacon chain, its execution chain, its Layer 2s, and soon its shards. These\ncan all be thought as separate blockchains, heavily inter-connected with one\nanother, and together forming an ecosystem. In this work, we call each of these\ninterconnected blockchains "domains", and study the manifestation of Maximal\nExtractable Value (MEV, a generalization of "Miner Extractable Value") across\nthem. In other words, we investigate whether there exists extractable value\nthat depends on the ordering of transactions in two or more domains jointly. We\nfirst recall the definitions of Extractable and Maximal Extractable Value,\nbefore introducing a definition of Cross-Domain Maximal Extractable Value. We\nfind that Cross-Domain MEV can be used to measure the incentive for transaction\nsequencers in different domains to collude with one another, and study the\nscenarios in which there exists such an incentive. We end the work with a list\nof negative externalities that might arise from cross-domain MEV extraction and\nlay out several open questions. We note that the formalism in this work is a\nwork in progress, and we hope that it can serve as the basis for formal\nanalysis tools in the style of those presented in Clockwork Finance, as well as\nfor discussion on how to mitigate the upcoming negative externalities of\nsubstantial cross-domain MEV.\n
Compared with fiat currencies, cryptocurrencies are usually more vulnerable to speculation and thus lead to massive price fluctuations, which makes exchanging cryptocurrencies a potentially profitable but risky endeavor. We aim to contribute to the understanding of the arbitrage behavior involving multiple cryptocurrency exchange markets. Specifically, we applied a Bellman-Ford based algorithm to detect possible arbitrage opportunities. By investigating historical data from three cryptocurrency exchange markets, i.e., Gemini, Coinbase, and Kraken, we designed experiments to identify how often arbitrage was possible in the past as well as the factors that contribute to the existence of arbitrage. We believe this may bring insights into strategies to stabilize the cryptocurrency exchange markets.
Cryptocurrencies are gaining popularity day by day, and their analysis is a fascinating and demanding research topic. The average daily trading volume of Bitcoin was ${\$}67$ billion in May 2021. A peculiar feature of cryptocurrencies is that they are not generally issued by a central authority, making them insusceptible to any governmental impedance. Cryptocurrency rates are closely related to news and influenced by tweets. However, no available dataset can analyze the crypto market adequately. We present CrypTop12, a benchmark dataset for Cryptocurrency Price Movement Prediction based on tweets and historical prices. We collect over 576K tweets related to the top 12 cryptocurrencies, spanning over 1255 days and refine them to filter the tweets that are most relevant to price fluctuations. We also demonstrate use-cases by providing adapted baseline methods and a quantitative results analysis on our dataset.
This paper used two frames based on the Multivariate General Autoregressive Conditional Heteroscedasticity (MGARCH) model, namely the Dynamic Conditional Correlation (DCC) and the Baba, Engle, Kraft, and Kroner (BEKK) models. DCC parameters confirmed the significant results to assess the spillover effects for return volatilities of five cryptocurrencies (Bitcoin, Dogecoin, Ethereum, Monero, and Peercoin). It indicated that cryptocurrency market returns would be volatile, connected with the time-varying pattern. Most ARCH and GARCH effects were significant in estimating the three pairs of return-mining profitability, return-Tweet, and mining profitability-Tweet. For the cryptocurrency return and profitability pair, returns depended on future price returns and cross-volatility spillover and were greater than their own volatility spillover effect. Moreover, the BEKK diagonal model was found to be the best model for return-mining profitability. The research community can also gain valuable insights into cryptocurrency investment models, offering wider future areas of research.
Decentralized Finance (DeFi) is a popular topic in the blockchain and cryptocurrency industry in the early 2020s. Still, cryptocurrencies have not yet become Decentralized Payment Systems (DPS) because of the high volatility of bitcoin and many of the altcoins. We investigated a proposed method to form a non-collateralized stablecoin called the Morini's Scheme of Inv&Sav wallets. We figured out two equations for the rebasement for the Inv wallet balances and then compared the results. We found the second rebasement method to be fairer to the agents, but we found the issue of negative balances with both methods. We proposed novel solutions to overcome these issues. One of the proposed solutions was to freeze some money in the Sav wallet if there is a negative balance in the Inv wallet. Another proposed solution was to introduce a two-money economy of money and antimoney to turn the current centralized token distribution model decentralized and make transactions more probable even if agents do not have enough money funds; this could be seen as a decentralized version of credit cards.
Abstract Cryptocurrencies are digital assets that can be stored and transferred electronically. Bitcoin (BTC) is one of the most popular cryptocurrencies that has attracted many attentions. The BTC price is considered as a high volatility time series with non-stationary and non-linear behavior. Therefore, the BTC price forecasting is a new, challenging, and open problem. In this research, we aim the predicting price using machine learning and statistical techniques. We deploy several robust approaches such as the Box-Jenkins, Autoregression (AR), Moving Average (MA), ARIMA, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and Grid Search algorithms to predict BTC price. To evaluate the performance of the proposed model, Forecast Error (FE), Mean Forecast Error (MFE), Mean Absolute Error (MAE), Mean Squared Error (MSE), as well as Root Mean Squared Error (RMSE), are considered in our study.
Boosted by blockchain technology, the sphere of Decentralized Finance (DeFi) and Non-Fungible Tokens (NFTs) is expanding globally. Although valuations of major cryptocurrencies Bitcoin and Ethereum likely will set the future of crypto-assets, the post-2020 expansion of cryptocurrency markets prompted explosive growth of DeFi- and NFT-branded altcoins. This study constructs time-series models to examine DeFi- and NFT-related cryptocurrencies and to clarify how their weekly prices fluctuated over a one-year period. Using Google Trends data, we measure how weekly Internet searches into crypto markets generally and specific branded cryptocurrencies affected price fluctuations for each coin. Results show that Bitcoin prices modeled as an exogenous variable have a positive effect on Ethereum prices. On average, brand-specific and market-level Google searches were estimated to be negative but not statistically convincing for DeFi- and NFT-branded altcoins.