Search Results (509)

This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α=1.007, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio’s diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure. Full article

Bitcoin, which has been used for 13 years, has a role in transactions and investments as a major cryptocurrency. However, as the number of users increases, Bitcoin faces difficulties, such as scalability for transaction throughput and energy-consumption problems due to the concentration of the mining pool. When Bitcoin first started to come out, it began to develop gradually through the mining of individuals. Nevertheless, as the price of the cryptocurrency gradually climbed, large mining corporation groups entered the mining competition with integrated circuit (IC) chips. Consequently, the substantial increase in power consumption is raising concerns regarding energy expenditure. This paper confirms that the verifiable random selection consensus protocol based on proof of work facilitates a fair and efficient system, enabling the participation of numerous individual miners in the mining competition while counteracting the monopolization of the hash rate by large mining corporations, thereby preserving the decentralization of mining. The protocol demonstrates the potential to mitigate substantial energy consumption. Moreover, it embodies features that create barriers to the adoption of high-energy-consuming application-specific integrated circuit equipment, significantly diminishing the principal factors contributing to extensive power utilization. Full article

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO₂ eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO₂ equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers. Full article

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades. Full article

Popular methods to value Bitcoin include the stock-to-flow model, Metcalfe’s Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoin’s returns. I find that the stock-to-flow model predictions and Metcalfe’s Law help to explain Bitcoin’s returns in-sample but have limited to no ability to predict Bitcoin’s returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoin’s returns in-sample and are poor predictors of Bitcoin’s returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or “market timing” strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or “HODL”ing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin. Full article

Jump dynamics in financial markets exhibit significant complexity, often resulting in increased probabilities of subsequent jumps, akin to earthquake aftershocks. This study aims to understand these complexities within a multifractal framework. To do this, we employed the high-frequency intraday data from six major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dashcoin, EOS, and Ripple) and six major forex markets (Euro, British pound, Canadian dollar, Australian dollar, Swiss franc, and Japanese yen) between 4 August 2019 and 4 October 2023, at 5 min intervals. We began by extracting daily jumps from realized volatility using a MinRV-based approach and then applying Multifractal Detrended Fluctuation Analysis (MFDFA) to those jumps to explore their multifractal characteristics. The results of the MFDFA—especially the fluctuation function, the varying Hurst exponent, and the Renyi exponent—confirm that all of these jump series exhibit significant multifractal properties. However, the range of the Hurst exponent values indicates that Dashcoin has the highest and Litecoin has the lowest multifractal strength. Moreover, all of the jump series show significant persistent behavior and a positive autocorrelation, indicating a higher probability of a positive/negative jump being followed by another positive/negative jump. Additionally, the findings of rolling-window MFDFA with a window length of 250 days reveal persistent behavior most of the time. These findings are useful for market participants, investors, and policymakers in developing portfolio diversification strategies and making important investment decisions, and they could enhance market efficiency and stability. Full article

Cryptocurrency software implements cryptocurrency operations (including the distributed consensus protocol and peer-to-peer networking) and often involves the open-source community. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol by providing the verification capability of the Bitcoin software by peers and preventing any potential peer from establishing a connection with modified Bitcoin software. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from previous research because it is permissionless, distributed, and lightweight for its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight to support the dynamic nature of the peer connections and large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. Furthermore, the Version++ verification overhead is small compared to the version-verack handshaking process; we measure the overhead to be 0.524% in our local networking environment between virtual machines and between 0.057% and 0.282% (depending on the peer location) in our more realistic cloud-based experiments with remote peer machines. Full article

Cryptocurrencies have now become an emerging blockchain-based payment technology; among them, bitcoin is the best known and most widely used. Users on these networks are pseudo-anonymous, meaning that while all transactions from an address are transparent and searchable by anyone, the users’ true identities are not directly revealed; to preserve their privacy, users often use many different addresses. In recent years, some studies have been conducted regarding analyzing clusters of bitcoin addresses that, according to certain heuristics, belong to the same entity. This capability provides law enforcement with valuable information for investigating illegal activities involving cryptocurrencies. Clustering methods that rely on a single heuristic often fail to accurately and comprehensively cluster multiple addresses. This paper proposes Bitcoin Address Clustering based on multiple Heuristics (BACH): a tool that uses three different clustering heuristics to identify clusters of bitcoin addresses, which are displayed through a three-dimensional graph. The results lead to several analyses, including a comparative evaluation of WalletExplorer, which is a similar address clustering tool. BACH introduces the innovative feature of visualizing the internal structure of clusters in a graphical format. The study also shows how the combined use of different heuristics provides better results and more complete clusters than those obtained from their individual use. Full article

The study investigates the nonlinear contagion, tail dependence, and Granger causality relations with TAR-TR-GARCH–copula causality methods for daily Bitcoin, Fintech, energy consumption, and CO₂ emissions in addition to examining these series for entropy, long-range dependence, fractionality, complexity, chaos, and nonlinearity with a dataset spanning from 25 June 2012 to 22 June 2024. Empirical results from Shannon, Rényi, and Tsallis entropy measures; Kolmogorov–Sinai complexity; Hurst–Mandelbrot and Lo’s R/S tests; and Phillips’ and Geweke and Porter-Hudak’s fractionality tests confirm the presence of entropy, complexity, fractionality, and long-range dependence. Further, the largest Lyapunov exponents and Hurst exponents confirm chaos across all series. The BDS test confirms nonlinearity, and ARCH-type heteroskedasticity test results support the basis for the use of novel TAR-TR-GARCH–copula causality. The model estimation results indicate moderate to strong levels of positive and asymmetric tail dependence and contagion under distinct regimes. The novel method captures nonlinear causality dynamics from Bitcoin and Fintech to energy consumption and CO₂ emissions as well as causality from energy consumption to CO₂ emissions and bidirectional feedback between Bitcoin and Fintech. These findings underscore the need to take the chaotic and complex dynamics seriously in policy and decision formulation and the necessity of eco-friendly technologies for Bitcoin and Fintech. Full article

Despite advances in biofuel production and biomass processing technologies, biorefineries still experience commercialization issues. When costs exceed revenues, their long-term economic sustainability is threatened. Although integrated biorefineries have significant global potential due to process integration and product co-generation, it is crucial that they generate a positive net return, thereby incentivizing their continual operation. Nonetheless, research and development into new system designs and process integration are required to address current biorefinery inefficiencies. The integration of Bitcoin mining into biorefineries represents an innovative approach to diversify revenue streams and potentially offset costs, ensuring the economic viability and commercial success of biorefineries. When using bio-H₂, a total of 3904 sats/kg fuel can be obtained as opposed to 537 sats/kg fuel when using syngas. Bitcoin, whether produced onsite or not, is an accretive asset that can offset the sales price of other produced biochemicals and biomaterials, thereby making biorefineries more competitive at offering their products. Collaborations with policy makers and industry stakeholders will be essential to address regulatory challenges and develop supportive frameworks for widespread implementation. Over time, the integration of Bitcoin mining in biorefineries could transform the financial dynamics of the bio-based products market, making them more affordable and accessible whilst pushing towards sustainable development and energy transition. Full article

This study examines the impact of market volatility and cryptocurrency holdings on corporate liquidity, with a particular focus on the differences between cryptocurrency exchanges and other businesses. The analysis is based on 181 firm-year observations from 2017 to 2022, using Bitcoin volatility, VIX, and VKOSPI as indicators of market volatility. Ordinary Least Squares (OLS) and robust regression analyses are employed to assess the relationships between these variables. It is first noted that, albeit insignificant, market volatility has a detrimental influence on company liquidity. The positive correlation for cryptocurrency exchanges, however, suggests that cryptocurrency exchanges could potentially leverage market volatility as a strategic advantage. Additionally, the study shows that cryptocurrency holdings enhance corporate liquidity, with a stronger association observed in cryptocurrency exchanges. The analysis also incorporates lagged variables to capture delayed effects, confirming that cryptocurrency holdings exert both immediate and delayed positive impacts on liquidity, likely due to effective strategic management practices within exchanges. Full article

Using daily returns of Bitcoin, Litecoin, Ripple and Stellar, we introduce a novel risk measure for quantitative-risk management in the cryptomarket that accounts for the significant co-movements between cryptocurrencies. We find that our model has a lower error margin when forecasting the extent of future losses than traditional risk measures, such as Value-at-Risk and Expected Shortfall. Most notably, we observe this in Litecoin’s results, where Expected Shortfall, on average, overestimates the potential fall in the price of Litecoin by 8.61% and underestimates it by 3.92% more than our model. This research shows that traditional risk measures, while not necessarily inappropriate, are imperfect and incomplete representations of risk when it comes to the cryptomarket. Our model provides a suitable alternative for risk managers, who prioritize lower error margins over failure rates, and highlights the value in exploring how risk measures that incorporate the unique characteristics of cryptocurrencies can be used to supplement and complement traditional risk measures. Full article

We analyzed Bitcoin’s cyclical patterns used by the Markov regime-switching model and explored the impacts of inflation and the US Dollar Index on Bitcoin’s cyclicality. The results showed Bitcoin’s cyclical pattern, the effects of the US dollar index and VIX on Bitcoin’s cyclical pattern, and how the US dollar index and VIX affect BTC’s structural changes in Bitcoin. Full article

The gap between software design and implementation often results in a lack of clarity and precision. Formal languages, based on mathematical rules, logic, and symbols, are invaluable for specifying and verifying system designs. Various semi-formal and formal languages, such as JSON, XML, predicate logic, and regular expressions, along with formal models like Turing machines, serve specific domains. This paper introduces a new specification formal language, ObTFL (Object Transaction Formal Language), developed for general-purpose distributed systems, such as specifying the interactions between servers and IoT devices and their security protocols. The paper details the syntax and semantics of ObTFL and presents three real case studies—federated learning, blockchain for crypto and bitcoin networks, and the industrial PCB board with machine synchronization—to demonstrate its versatility and effectiveness in formally specifying the interactions and behaviors of distributed systems. Full article

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. Full article