The field of cryptocurrencies is in existence and dynamically evolving for over 14 years. Each year introduces new cryptocurrencies, with their total number exceeding 8,500. However, to date, there is no exhaustive categorization of cryptocurrencies that could possibly fully describe the landscape of the cryptocurrency market, which underscores the relevance of this research. The objective of this study is to construct a hierarchical categorization (taxonomy) of cryptocurrencies based on their main characteristics and functions. The principal research method is a retrospective analysis of the development of the cryptocurrency field from the creation of Bitcoin to the present day. As the industry evolved, new projects emerged, which significantly differed in their properties from what existed before, thus forming entirely new categories and niches in the cryptocurrency space. Moreover, the emergence of certain types of cryptocurrencies could lead to changes in the existing classification. The outcome of this research is a taxonomy of interchangeable cryptocurrencies/tokens. The proposed taxonomy is accompanied by a detailed examination of the cryptocurrencies associated with each category, as well as a consideration of the largest cryptocurrencies in terms of capitalization through its prism. The scientific novelty of this research lies in the absence of similar studies that look at the issue of categorizing cryptocurrencies through a historical lens.
This study examines the volatility dynamics of Bitcoin (BTC-USD) and MicroStrategy (MSTR) from September 2019 to September 2024 using the GARCH (1,1) model. Volatility is a key measure of risk in the financial market, understanding its patterns is crucial for effective portfolio management, risk management, and corporate financial strategies. Bitcoin, while known to be volatile, is very unpredictable, and given the high holding of that on MicroStrategy's balance sheet, it is closely tagged to the volatility of Bitcoin. Critical periods, such as the COVID-19 and the subsequent crypto market downturn between 2022 and 2023, demonstrate the linkages between traditional equities and digital assets. The findings of such analysis will prove that MicroStrategy's volatility has indeed closely followed the footsteps of Bitcoin, especially during the 2024 rally in that market, including all its shocks and recoveries. These find great importance in understanding volatility due to the growing integration of digital assets into corporate portfolios. This research will offer investors and corporate managers alike extensive insight into risk management and portfolio diversification by accounting for volatility dynamics between cryptocurrencies and stocks.
Net-zero emission targets require transparent and efficient carbon credit trading systems. This paper introduces a blockchain-based data visualization framework to enhance decision-making in the production and logistics sectors by simplifying blockchain transaction records and identifying potential arbitrage activities. The framework integrates real-time decision support tools, enabling production system managers to monitor carbon offset activities, detect fraudulent behaviors, and streamline operations. This research provides actionable insights into supply chain emissions management and operational risk reduction by leveraging advanced visualization techniques. The proposed approach offers innovative solutions to address the complexities of blockchain-based carbon trading, emphasizing transparency and sustainability. Our analysis demonstrates the effectiveness of these techniques in mitigating fraud and supporting compliance with international carbon trading standards. The findings contribute to integrating advanced technologies into sustainable production systems, offering practical implications for achieving global climate change mitigation goals and fostering a more efficient and secure carbon credit market.
For the development of the clean energy industry and the transformation of energy, clean energy metals are essential raw materials. They are also crucial constituents of the commodity market, attracting many cross-market investors. This research uses the asymmetric TVP-VAR framework to investigate the time-varying connectivity between cryptocurrency environmental attention (ICEA) and clean energy metals’ prices. The results show that ICEA received the net spillover from clean energy metals, while clean energy metals exhibit heterogeneity. There is a difference between the spillover of positive and negative returns among series, and the connectedness of negative returns is more prominent. In addition, the series’ total and net connectivity are time-varying and influenced by COVID-19. This study has certain reference values for the academia and market participants.
Abstract This study examines the return connectedness between decentralized finance (DeFi)’s and the Association of Southeast Asian Nations (ASEAN) stock markets using the quantile vector autoregressive framework, which allows us to investigate the connectedness at conditional quantiles. Our sample includes four major DeFi’s and six ASEAN stock markets, spanning from March 2018 to December 2022. The static results indicate a moderate level of return transmission between the system at mean and median quantile. This propagation increases substantially under extreme market conditions, establishing an asymmetric transmission across quantiles. Despite being a relatively new asset class, DeFi dominates the equity market and acts as the primary shock transmitter to the system in most instances. The dynamic analysis reveals that total system connectedness fluctuates over time and quantiles. The total system connectedness peaked during the COVID-19 and the Russia–Ukraine conflict period, indicating the impact of global events on system transmission. The optimal weight and hedge ratio estimated using the DCC-GARCH model indicate that DeFi is beneficial for portfolio construction and risk management. The rising trend in dynamic optimal weight and hedge ratio during the COVID-19 pandemic demonstrates that investors should decrease their investments in DeFi and increase hedging costs. Therefore, portfolio managers and investors should readjust their portfolio allocation in a timely manner according to different market states to build additional effective hedging and diversification strategies to avoid large losses and to reduce portfolio risk exposure.
With the rapid growth of the cryptocurrency market, researchers increasingly study the price fluctuations and market behavior of digital assets. Gold, as a traditional safe-haven asset, often shows an inverse relationship with high-risk financial assets. Recently, scholars have focused on how gold market volatility affects cryptocurrencies, exploring potential co-movement or substitution effects. This study uses Python and econometric tools, including the Vector Autoregression (VAR) model, Granger causality test, impulse response functions, and forecast error variance decomposition, to analyze the impact of gold price changes on Bitcoin and Ethereum. Using weekly closing prices from 2018 to 2024, the results show that Bitcoin’s price is positively influenced by gold futures in the short to medium term, while gold shows a negative feedback response to Bitcoin’s returns with a two-period lag. Ethereum appears more independent and less affected by gold or Bitcoin. Strong interlinkages exist between Bitcoin and Ethereum, with Bitcoin playing a dominant role in influencing Ethereum’s price. This study has improved the understanding of the connections between cryptocurrencies and traditional assets., which also provides investors with insightful information on portfolio management.
This study employed Extreme Value Theory (EVT) to identify high-risk investment opportunities in the volatile crptocuurency market. EVT provides a more accurate risk assessment than traditional methods as it focuses on the tail distribution. The daily outcomes of six major cryptocurrencies were used for the analysis (Bitcoin, Ethereum, Ethereum Classic, Litecoin, Monero and Ripple). The time frame extends from January 2017 to December 2019 and includes major changes. Returns are fitted to the generalized Pareto distribution (GPD) in conjunction with the extreme value distribution. The results show that Bitcoin has a relatively low downside risk compared to other cryptocurrencies. Ethereum and Litecoin have more stable return patterns, suggesting a safer profile, while Ripple and Monero have the highest tail risk. These findings are consistent with other studies looking at the diversification and safe-haven properties of certain cryptocurrencies and highlight the importance of Extreme Value Theory (EVT) in evaluating extreme negative risk. The study is highly relevant for investors, portfolio managers and regulators to minimize volatility and reduce systemic risk in digital asset markets.
We examine how investor emotions and Bitcoin price influence each other using intraday data and textual analysis. We extract emotions from a popular online chatting window at one of the largest cryptocurrency exchanges in Korea. To control for global factors, we analyse relative Bitcoin prices and the differences between the Korean exchange and other global prices. The identified emotions predict the return and volatility of Bitcoin price one hour ahead. The results are economically significant: simple arbitrage trading strategies using the relationship between emotions and Bitcoin prices generate profits. Consequently, investor emotions drive Bitcoin prices, suggesting irrational crypto-markets that rational speculators may exploit.
This thesis examines Bitcoin's potential as a store of value, focusing on devel- opments up to February 2025. It finds weak cointegration and low correlation between Bitcoin and gold, challenging the view of Bitcoin as a reliable store of value. Instead, Bitcoin shows strong similarities to stock market indices in both price and return trends, especially after the COVID-19 pandemic. These findings suggest Bitcoin behaves more like a risky asset than digital gold. The analysis employs Cointegration and DCC-GARCH methods, re- vealing that emerging parallels between gold and Bitcoin are likely anomalies, making state-level adoption as a reserve asset premature. 1
With the increasingly turbulent political situation and the outbreak of public health events without warning, it will not only affect people’s physical health, but also affect the global financial market, causing the market to fall into a huge crisis, thus leading to a continued decline in the worldwide economy. During periods of financial market turmoil, many investors fall into panic and urgently need a “haven” to protect their assets. With the rise of the digital economy, gold no longer seems to be the only safe-haven option. Bitcoin has gradually entered the investors’ field of vision. Some investors believe that Bitcoin can become an emerging safe-haven asset that is as important as or surpasses gold. Based on an analysis of the safe-haven properties of Bitcoin and gold during major political and historical events and public health events, this article will clarify which of the two is more suitable as a reliable contemporary safe-haven asset and provide advice to investors.
Abstract Bitcoin has emerged as a highly attractive and reliable investment asset for financial managers, businesses, and economic firms due to its unique features such as high security, decentralization, and potential for increased income. Consequently, Bitcoin price prediction has become a significant topic of interest among financial and economic analysts and researchers. Forecasting in such contexts often involves uncertain conditions and limited information. Grey systems theory, which specializes in analyzing problems with small samples and insufficient information, offers a promising approach. This study aims to predict the price of Bitcoin using an advanced model of grey systems theory: the fractional multivariable grey model (FGM(1, N )). The FGM(1, N ) model stands out by incorporating external factors into its predictions. Specifically, this research utilizes the FGM(1,3) model, considering the crude oil and gold prices to forecast Bitcoin price. The results demonstrate that the FGM(1,3) model provides more accurate predictions and better performance than the FGM(1,1) model, which does not include external factors like oil and gold prices. This study highlights the significant impact of crude oil and gold price trends on Bitcoin's market and underscores the effectiveness of the multivariable fractional grey model in financial forecasting.