J. Alvarez-Ramirez, Luísa Castro, Eduardo Rodríguez
The recent decade has witnessed a surge of cryptocurrency markets as innovative financial systems based strongly on digital emission, interchange and coding. The main characteristic is that cryptocurrencies are not subjected to the regulation of governments and financial institutions (e.g., central banks), such that their dynamics are determined solely by non-centralized mechanisms. Informational efficiency is a key issue for cryptocurrency markets since its fulfillment guarantees that all participants have access to the same information quality and that arbitrage conditions are discarded. This study evaluated the contribution of nonlinearities to the informational efficiency of the Bitcoin market for the period 2014–2022. Singular value decomposition (SVD) entropy together with shuffled and phase-randomized data in a rolling-window framework was used to capture randomness and nonlinear dynamics in Bitcoin returns. It was found that the contribution of nonlinearities to informational efficiency increases with the time scale, with a mean contribution of about 7.25% for long-time scales. This means that the Bitcoin market is only affected by weak nonlinearities, although these effects should be considered for forecasting and valuation.
Dora Almeida, Andreia Dionísio, Isabel Vieira, Paulo Ferreira
Cryptocurrencies are relatively new and innovative financial assets. They are a topic of interest to investors and academics due to their distinctive features. Whether financial or not, extraordinary events are one of the biggest challenges facing financial markets. The onset of the COVID-19 pandemic crisis, considered by some authors a "black swan", is one of these events. In this study, we assess integration and contagion in the cryptocurrency market in the COVID-19 pandemic context, using two entropy-based measures: mutual information and transfer entropy. Both methodologies reveal that cryptocurrencies exhibit mixed levels of integration before and after the onset of the pandemic. Cryptocurrencies displaying higher integration before the event experienced a decline in such link after the world became aware of the first cases of pneumonia in Wuhan city. In what concerns contagion, mutual information provided evidence of its presence solely for the Huobi Token, and the transfer entropy analysis pointed out Tether and Huobi Token as its main source. As both analyses indicate no contagion from the pandemic turmoil to these financial assets, cryptocurrencies may be good investment options in case of real global shocks, such as the one provoked by the COVID-19 outbreak.
Nakamoto’s Bitcoin is the first decentralized digital cash system that utilizes a blockchain to manage transactions in its peer-to-peer network. The newer generation of blockchain systems, including Ethereum, extend their capabilities to support deployment of smart contracts within their peer-to-peer networks. However, smart contracts cannot acquire data from sources outside the blockchain since the blockchain network is isolated from the outside world. To obtain data from external sources, smart contracts must rely on Oracles, which are agents that bring data from the outside world to a blockchain network. However, guaranteeing that the oracle’s off-chain nodes are trustworthy remains a challenge. A centralized oracle that relies on a single off-chain node creates a single point of failure. Therefore, a decentralized mechanism is necessary. One possible design for a decentralized oracle is to use a game mechanism that utilizes the Schelling-point theory to identify the correct data point among various data points reported by the oracle’s off-chain nodes. In this paper, we introduce Spartan Price Oracle (SPO), a decentralized oracle designed to provide accurate price data. SPO utilizes the Schelling-point theory in its game mechanism to ensure the accuracy of the price data it provides. The mechanism design of SPO is based on SchellingCoin but with two significant improvements. Firstly, SPO uses Kernel Density Estimation to estimate the probability density function of data points that are reported by multiple off-chain nodes. This enables SPO to identify the accurate data by determining the mode of the probability density function. Secondly, SPO utilizes a redistributive economic incentive model that incorporates an appeal mechanism to increases the maximum reward for its off-chain nodes. This model has been proven to raises the budget required for compromising off-chain nodes and helps in preventing potential attacks on the oracle.
This paper aims to analyze Bitcoin as an identifiable system of human-machine interdependence. The authors start with a brief historical outline of the Bitcoin project and discuss questions that Bitcoin poses to social sciences, such as whether Bitcoin is money, how the Bitcoin project relates to economic theory, what determines the value of a Bitcoin, and what are the conditions for trust in Bitcoin? Finally, what happens when the Bitcoin project becomes a reality? In what follows, the authors correlate the existence of Bitcoin with the spread of artificial intelligence (AI) technologies as active intermediaries and participants in human interactions. After observing the similarities and differences between AI and the Bitcoin project, the idea of whether Bitcoin can act as “artificial money” for AI is discussed, and the reality of human-machine interdependence is exemplified. In conclusion, the authors define Bitcoin as a particular system of human-machine interdependence initially conceived as an alternative to money; however, in reality, it supplements the existing economic order.
This article quantifies the correlation between Bitcoin and NVIDIA using the DCC-GARCH model during the period of 2020-2023. We analyzed data from investing.com for this research. Bitcoin is a cryptocurrency based on blockchain technology, which involves mining by solving complex cryptographic puzzles. Mining refers to the process of verifying and recording Bitcoin transactions through computation, and acquiring newly generated Bitcoins as a contribution to network security and the distributed consensus mechanism. Therefore, it is important to understand the correlation between Bitcoin and graphics cards, especially with the expansion of the virtual currency market. Determining the correlation between Bitcoin mining and graphics cards can help miners optimize their hardware choices, investors better understand market potential, and manufacturers produce and develop graphics cards according to market demand. Due to the high computational requirements of Bitcoin mining, traditional central processing units (CPUs) are not well-suited for this task. On the other hand, graphics cards (graphics processing units, GPUs) have become the preferred hardware for Bitcoin mining due to their highly parallel computing capabilities. Consequently, we hypothesize the existence of a correlation between Bitcoin and graphics cards, which is further validated in subsequent sections.
The financial software has expanded to include cryptocurrencies, which are seeing rapid adoption and are being positively received by critics. Mining is an essential part of these systems, which use a distributed ledger to store data in a trustworthy manner. The decentralized ledger, known as the blockchain is updated with information on prior transactions when mining is performed. Users are allowed to arrive at a reliable and robust agreement for each transaction. Mining can result in the generation of new wealth in the form of monetary assets, such as currency. Because cryptocurrencies were conceived from the outset to operate as decentralized, peer-to-peer networks, there is no centralized authority that can supervise the monetary transactions that take place using these currencies. Miners are accountable for ensuring that the transactions they are processing are legitimate. For crypto currencies, mining algorithms that are both dependable and strong are a fundamental must. This paper provides a comprehensive summary of crypto coins, specifically Bitcoin, Ethereum, and Litecoin, and an analysis and critique of the previous research on crypto currency trading that has been published. This paper presents a classification system that could be applied to both wellestablished standards and newly developed concepts.
Since Bitcoin came into the world, modelling and analyzing the underlying characteristics of Bitcoin has attracted increasing attention. This paper uses a framework including decomposition, reconstruction and extraction method (DRE) to analyze price fluctuations based on ultra-high-frequency data from Dec.1, 2019, to Nov.30, 2021. First, the ensemble mode decomposition (EMD) is employed to decompose the Bitcoin hourly spot price into 13 intrinsic mode functions (IMF) plus a residual. Second, the IMFs are reconstructed into high-frequency components, low-frequency components and a trend based on fine-to-coarse reconstruction. Furthermore, the intraday volatility analysis based on LM test is applied on 15-minutes frequency data to detect discontinuous jump arrivals and extract jump from realized quadratic variation. Empirical results show that three components of reconstruction can be identified as short term fluctuations process caused by microstructure noise, the shocks affected by major events, and a long-term trend based on inelastic supply and rigid demand. We find that approximately 40% of jumps can be matched with the news from the public news database (Factiva), and the jump sizes are larger than that of stock markets. This finding indicates that the Bitcoin market has more irregularly noise and unforeseen shocks from unscheduled events.
In this research paper, we'll talk about cryptocurrency with the help and development of deep learning, and AI-assisted trading has gained immense popularity. To regulate the splendid engrossment from the part of cryptology we take the assistance of retailing (Deep learning & AI-support). A specific period of data has been stored on daily bases to receive the outcomes in a company of the help of ultra-modern algos. With the references to various papers, I found out the pros and cons of cryptocurrency price prediction. Some simple algorithms & architectures helped to grow the cryptocurrency market. Crypto trading became popular in 2017 and now more than 1500 cryptocurrencies are proactively trading. Crypto currencies can be smoothly created and used for online settlement. Bitcoin is also known as cryptocurrency and its values keep varying every second. Hence for predicting the rate of bitcoin cost I will use the infrastructure of LSTM. This infrastructure will help us in proving that LSTM will provide more accuracy. RNN is a category of ANN and connectivity for this type of network is made through nodes from the direct nodes along with a time-related progressions. LSTM is a RNN infrastructure which is a part of DL which handles the entire data as well as single data points.
ABSTRACT Recent literature explores the profitability of various cryptocurrency momentum trading strategies and proposes cryptocurrency momentum as a pricing factor (Liu et al.). How risky is this factor‐based investment strategy for crypto‐investments? We answer this question by examining the distributional characteristics (hence, riskiness) of six cryptocurrency momentum trading strategies. The empirical evidence suggests that the realised variances of cryptocurrency momentum strategies are governed by power laws. The statistical tests derived from block bootstraps indicate that the population mean and variance of the momentum factor realised variances are statistically not defined. Contrary to the belief that cryptocurrency momentum trading strategies produce generous payoffs, our results imply that, in real life, we might not be able to realise these risk premiums. We conclude that the performance metrics evaluating the profitability of cryptocurrency momentum strategies, using variance as an input, are not informative. We also find cross‐sectional dependence amongst the tail risk of momentum strategies based on different formation periods.
This Bachelor's thesis studies connectedness effects between returns of US-listed cryptocurrency-linked stocks (CLS), the traditional US stock market, and ma- jor cryptocurrencies. We present results of connectedness measures obtained by utilizing the Dynamic Networks framework. Our dataset contains daily returns of 20 CLS, the stock market index S&P 500 and five major cryptocurrencies, with a time span ranging from September 2021 to July 2023. The connected- ness measures indicate a significant total connectedness among variables within the system, across the whole time span. We also present directional connected- ness measures for individual variables and decompose the total connectedness into time horizons. We report the short-term horizon of connectedness effects between 1-5 days to be the most significant. Finally, we build Ordinary Least Squares (OLS) regressions for CLS returns and find connectedness measures to influence returns of CLS with high exposure to the cryptocurrency market most significantly. Keywords Connectedness effects of returns, Cryp- tocurrencies, Bitcoin, Dynamic Networks, Cryptocurrency-linked stocks, Stock market Title Connectedness between Stocks of Cryptocurrency-linked US companies and the Cryptocurrency market.
This dissertation consists of three essays. The first essay provides background on blockchain, cryptocurrency, and venture capital. It will explain the evolution of token distribution models, regulatory concerns, and the industry adoption of the technology. The second essay presents a model of startup financing that reflects regulatory concerns of the first essay. It develops a three-period model that compares token financing with traditional VC equity financing, where the key difference between the two is that tokens can be sold earlier than equity, which allows them to meet the liquidity needs of investors. The third essay combines token financial data and onchain transaction data from the Ethereum blockchain, to study the relationship between token liquidity, returns, and onchain market maker inventory.