We provide a novel perspective on the Bitcoin market, investigating determinants of investor positions and their response to public information proxied by sentiment indicators. We distinguish between investors by size and observe their respective behaviour concerning incoming information. We find that price dynamics and media coverage lead to different decisions depending on the Bitcoin portfolio size. Retail investors react strongly to incoming public information and media narratives, with their decisions strongly influenced by sentiment and media attention. Conversely, the response of large-scale investors to such information is much weaker because they arguably have different, non-public information and divergent investment objectives.
Bitcoin has been gaining increasing attention in academia and industry.This article investigates Bitcoin's research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection.The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market.The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research.The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012-2016), cryptocurrency and market efficiency (2017)(2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019-2022).The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy.The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.
This paper analyses the extreme tail behaviour of Bitcoin returns by fitting a Generalised Pareto Distribution (GPD). The GPD is used to model the extreme daily Bitcoin returns over the period 2008 to 2023. The returns above the chosen thresholds, for both Bitcoin gains and losses, are selected. The GPD is then fitted to the selected excess returns. The Anderson Darling (AD) and Kolmogorov Smirnov (K-S) goodness-of-fit tests reveal that the GPD captures the distribution of the Bitcoin excess returns. The Value at Risk (VaR) and Expected Shortfall (ES) under the GPD are used to measure the extreme tail risk of the Bitcoin returns. The upside risk (gains) is found to outweigh downside risk (losses), and this gives insight to investors interested in Bitcoin.
Purpose: This paper pioneers exploring the relationship between cryptocurrencies, considering the case of non-fungible tokens (NFTs) and traditional cryptocurrencies Design/Methodology/Approach: The analysis is performed through an innovative TVP-VAR frequency connectedness approach, revealing a substantial level of dynamic integration and return transmission among cryptocurrencies systems. Findings: Our findings are multifaceted. Firstly, that there is higher total connectedness in the bearish and bullish market conditions compared to normal conditions. Secondly, the degree of connectedness is even stronger during tranquil and turbulent times such as the Covid-19 pandemic and the Russian-Ukrainian war. Thirdly, the network's net transmission behavior is predominantly by the short-term dynamics for NFT and by the long-term dynamics for Conventional cryptocurrencies, and assets' roles as net-transmitter and net-receiver can change over time. Practical Implications: These findings inform investors, traders, and portfolio managers to prioritize risk management during high-risk periods, such as COVID-19 and the Russian-Ukrainian conflict, as crises involve non-diversifiable systematic risks, demanding careful risk mitigation. Originality/Value: One of the main challenges of cryptocurrencies is determining the nature of the dynamics of their connectivity. The originality and the value of this research is to investigate whether cryptocurrencies evolve in a similar manner to each other.
The advent of cryptocurrencies and blockchain technology has sparked a revolutionary shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist.
Nezir Köse, Hakan YILDIRIM, Emre Ünal, Boqiang Lin
Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.
This article investigates the Ethereum Merge, which occurred on 15 September 2022, and we employ the time-series difference in differences (DiD) model and vector autoregression (VAR) models and analyse how the protocol change from proof-of-work to proof-of-stake (PoS) affects the dynamic relationship between cryptocurrency returns and network factors. The results show that the Merge caused a structural change between Ethereum and Bitcoin networks. The network factors of Ethereum show a significant increase compared to Bitcoin, the cointegration has been strengthened and the lag length is shortened after the Merge. The spillover effect on the Bitcoin network can be seen from both DiD and VAR, indicating the increasing impact of the Ethereum network on Bitcoin. The concern of losing the number of participants due to the implantation of PoS on cryptocurrency is not apparent on Ethereum Merge, and it increases the investors’ attention and involvement.
The significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability.
Cryptocurrencies have become a popular investment option and the Ethereum has become a mainstream cryptocurrency because of the additional functionality that can be accomplished with the backing of the powerful Ethereum network compared to Bitcoin. The high volatility of Ethereum offers both profits and risks, making it crucial to improve the forecasting ability for its price volatility. The results of this study could be useful for investors and policymakers who are interested in understanding and managing the risks associated with investing in Ethereum. Several studies have explored similar topics using heterogeneous autoregressive (HAR) models for cryptocurrencies, but this paper offers a more expansive approach. This paper employs five-minute high-frequency data to construct 4 HAR models to predict the volatility of Ethereum, taking into account the impact of structural breaks, Bitcoin, SP500 and VIX. The model that considers all factors outperforms other models for out-of-sample predictions for the 1-week forecasting. Due to the nature of the Ethereum price, the HAR-RV model has achieved a perfect fit in 1-day and 1-month forecasting. Therefore, other models have a very small improvement in fitness and prediction accuracy. This paper contributes to the understanding of Ethereum’s volatility and its impact on the cryptocurrency market.
Tonuchi E. Joseph, Atif Jahanger, Joshua Chukwuma Onwe, Daniel Balsalobre‐Lorente
Abstract This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.
This research examines the causal relationships between cryptocurrency uncertainty, the price of crude oil, and the price of gold using weekly data from 30 December 2013, to 21 February 2021, applying Granger-causality analysis on each quantile. Under this approach, we are able to distinguish between median and tail relationships for conditional quantiles. We find a bidirectional causal relationship between cryptocurrency uncertainty and crude oil prices, implying that crude oil price volatility is one source of cryptocurrency uncertainty, whereas a causal relationship from cryptocurrency uncertainty or crude oil prices to gold suggests that gold hedges cryptocurrency uncertainty and crude oil price shocks. Our research calls on governments to maintain cryptocurrency market stability to reduce market volatility in crude oil prices. Investors and fund managers can consider adding gold assets to portfolios that contain cryptocurrencies or crude oil in order to hedge against the risks of cryptocurrency uncertainty and crude oil price volatility.
Abstract Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.