Search Results (501)

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

Bitcoin is a payment system that generates a decentralized digital currency without ensuring temporal constraints in its transactions; therefore, it is vulnerable to double-spending attacks. Karame has proposed a formalization for a successful double-spending attack based on meeting three requirements. This focuses on fast payment scenarios where the product is delivered immediately after the payment is announced in the mempool, without waiting for transaction confirmation. This scenario is key in Bitcoin to increase the probability of a successful double-spending attack. Different approaches have been proposed to mitigate these attacks by addressing one or more of Karame’s three requirements. These include the following: flooding every transaction without restrictions, introducing listeners/observers, avoiding isolation by blocking incoming connections, penalizing malicious users by revealing their identity, and using machine learning and bio-inspired techniques. However, to our knowledge, no proposal deterministically avoids double-spending attacks in fast payment scenarios. In this paper, we introduce DiFastBit: a distributed transaction differentiation scheme that shields Bitcoin from double-spending attacks in fast payment scenarios. To achieve this, we modeled Bitcoin from a distributed perspective of events and processes, reformulated Karame’s requirements based on Lamport’s happened-before relation (HBR), and introduced a new theorem that consolidates the reformulated requirements and establishes the necessary conditions for a successful attack on fast Bitcoin payments. Finally, we introduce the specifications for DiFastBit, formally prove its correctness, and analyze DiFastBit’s confirmation time. Full article

Recently, liquidity issues in financial markets and portfolio asset management have attracted much attention among investors and scholars, fuelling a stream of research devoted to exploring the role of liquidity in investment decisions. In this paper, we aim to investigate the effects of introducing liquidity in portfolio optimization problems. For this purpose, first we consider three volume-based liquidity measures proposed in the literature and we build a new one particularly suited to portfolio optimization. Secondly, we formulate an extended version of the Markowitz portfolio selection problem, named mean–variance–liquidity, wherein the goal is to minimize the portfolio variance subject to the usual constraint on the expected portfolio return and an additional constraint on the portfolio liquidity. Thirdly, we consider a sensitivity analysis, with the aim to assess the trade-offs between liquidity and return, on the one hand, and between liquidity and risk, on the other hand. In the second part of the paper, the portfolio optimization framework is applied to a dataset of US ETFs comprising both standard and alternative, often illiquid, investments. The analysis is carried out with all the liquidity measures considered, allowing us to shed light on the relationships among risk, return and liquidity. Finally, we study the effects of the introduction of a Bitcoin ETF, as an asset with an extremely high expected return and risk. Full article

This article examines illicit financial flows (IFFs) perpetuated in financial statements to develop a framework to curb IFFs. IFFs create opacity, impeding economic progress through investment deterrents and financial uncertainty. Through a comprehensive literature review and the synthesis of sets of qualitative propositions, the researchers previously developed a conceptual framework to address IFFs, and the purpose of the present article is to strengthen and validate the framework among stakeholders in the financial and audit sectors. Following a mixed inductive and deductive research approach and a qualitative methodological choice, the researchers conducted interviews among practitioners to enhance the framework, followed by a focus group to validate the framework. IFF challenges that emerged are tax evasion, for example, investments in untraceable offshore accounts, harming the economy, and bitcoins not being subject to regulation everywhere in the world and being used by cryptocurrency criminals to transfer IFFs to nations with lax regulations. Internationally, IFF risks are also determined by geographical position, trade links, and porous borders among countries that emerged as further challenges, calling for entities to execute existing policies, improve tax enforcement methods, apply cross-border coordination, and practice financial reporting transparency aimed at combatting IFF practices. On the strength of these, the industry surveys significantly enhanced the conceptual framework. Full article

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation. Full article

This paper introduces hybrid models designed to analyze daily and weekly bitcoin return spanning the periods from 18 July 2010 to 28 December 2023 for daily data, and from 18 July 2010 to 24 December 2023 for weekly data. Firstly, the fractal and chaotic structure of the selected variables was explored. Asymmetric Cantor set, Boundary of the Dragon curve, Julia set z² −1, Boundary of the Lévy C curve, von Koch curve, and Brownian function (Wiener process) tests were applied. The R/S and Mandelbrot–Wallis tests confirmed long-term dependence and fractionality. The largest Lyapunov test, the Rosenstein, Collins and DeLuca, and Kantz methods of Lyapunov exponents, and the HCT and Shannon entropy tests tracked by the Kolmogorov–Sinai (KS) complexity test determined the evidence of chaos, entropy, and complexity. The BDS test of independence test approved nonlinearity, and the TeraesvirtaNW and WhiteNW tests, the Tsay test for nonlinearity, the LR test for threshold nonlinearity, and White’s test and Engle test confirmed nonlinearity and heteroskedasticity, in addition to fractionality and chaos. In the second stage, the standard ARFIMA method was applied, and its results were compared to the LieNLS and LieOLS methods. The results showed that, under conditions of chaos, entropy, and complexity, the ARFIMA method did not yield successful results. Both baseline models, LieNLS and LieOLS, are enhanced by integrating them with deep learning methods. The models, LieLSTMOLS and LieLSTMNLS, leverage manifold-based approaches, opting for matrix representations over traditional differential operator representations of Lie algebras were employed. The parameters and coefficients obtained from LieNLS and LieOLS, and the LieLSTMOLS and LieLSTMNLS methods were compared. And the forecasting capabilities of these hybrid models, particularly LieLSTMOLS and LieLSTMNLS, were compared with those of the main models. The in-sample and out-of-sample analyses demonstrated that the LieLSTMOLS and LieLSTMNLS methods outperform the others in terms of MAE and RMSE, thereby offering a more reliable means of assessing the selected data. Our study underscores the importance of employing the LieLSTM method for analyzing the dynamics of bitcoin. Our findings have significant implications for investors, traders, and policymakers. Full article

Knowledge of foreign exchange rates and their evolution is fundamental to firms and investors, both for hedging exchange rate risk and for investment and trading. The ARIMA model has been one of the most widely used methodologies for time series forecasting. Nowadays, neural networks have surpassed this methodology in many aspects. For short-term stock price prediction, neural networks in general and recurrent neural networks such as the long short-term memory (LSTM) network in particular perform better than classical econometric models. This study presents a comparative analysis between the LSTM model and BiLSTM models. There is evidence for an improvement in the bidirectional model for predicting foreign exchange rates. In this case, we analyse whether this efficiency is consistent in predicting different currencies as well as the bitcoin futures contract. Full article

Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the world market. A novel approach that explicitly separates the falling markets from the rising ones is utilized for this purpose. The empirical results have important implications for investors and financial institutions. Our approach provides a position-dependent measure of risk for Bitcoin. This is essential since the source of risk for an investor with a long position is the falling prices, while the source of risk for an investor with a short position is the rising prices. Thus, providing a separate risk measure in each case is expected to increase the efficiency of the underlying risk management in both cases compared to the existing methods in the literature. Full article

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption. Full article