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Demand for blockchains such as Bitcoin and Ethereum is far larger than supply, necessitating a mechanism that selects a subset of transactions to include "on-chain" from the pool of all pending transactions. This article investigates the problem of designing a blockchain transaction fee mechanism through the lens of mechanism design. We introduce two new forms of incentive compatibility that capture some of the idiosyncrasies of the blockchain setting, one (MMIC) that protects against deviations by profit-maximizing miners and one (OCA-proofness) that protects against off-chain collusion between miners and users. This study is immediately applicable to a major change to Ethereum's transaction fee mechanism, made on August 5, 2021, based on a proposal called "EIP-1559." Originally, Ethereum's transaction fee mechanism was a first-price (pay-as-bid) auction. EIP-1559 suggested making several tightly coupled changes, including the introduction of variable-size blocks, a history-dependent reserve price, and the burning of a significant portion of the transaction fees. We prove that this new mechanism earns an impressive report card: it satisfies the MMIC and OCA-proofness conditions, and is also dominant-strategy incentive compatible (DSIC) except when there is a sudden demand spike. We also introduce an alternative design, the "tipless mechanism," which offers an incomparable slate of incentive-compatibility guarantees—it is MMIC and DSIC, and OCA-proof unless in the midst of a demand spike. [ABSTRACT FROM AUTHOR]

Bitcoin is the first and most popular decentralized cryptocurrency to date. In this work, we extract and analyze the core of the Bitcoin protocol, which we term the Bitcoin backbone, and prove three of its fundamental properties which we call Common Prefix, Chain Quality, and Chain Growth in the static setting where the number of players remains fixed. Our proofs hinge on appropriate and novel assumptions on the "hashing power" of the protocol participants and their interplay with the protocol parameters and the time needed for reliable message passing between honest parties in terms of computational steps. A takeaway from our analysis is that, all else being equal, the protocol's provable tolerance in terms of the number of adversarial parties (or, equivalently, their "hashing power" in our model) decreases as the duration of a message passing round increases. Next, we propose and analyze applications that can be built "on top" of the backbone protocol, specifically focusing on Byzantine agreement (BA) and on the notion of a public transaction ledger. Regarding BA, we observe that a proposal due to Nakamoto falls short of solving it, and present a simple alternative which works assuming that the adversary's hashing power is bounded by 1/3. The public transaction ledger captures the essence of Bitcoin's operation as a cryptocurrency, in the sense that it guarantees the liveness and persistence of committed transactions. Based on this notion, we describe and analyze the Bitcoin system as well as a more elaborate BA protocol and we prove them secure assuming the adversary's hashing power is strictly less than 1/2. Instrumental to this latter result is a technique we call 2-for-1 proof-of-work (PoW) that has proven to be useful in the design of other PoW-based protocols. [ABSTRACT FROM AUTHOR]

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.

The rise of blockchain technology and the emergence of cryptocurrencies, most notably Bitcoin, have transformed the landscape of finance and technology. This article fulfills three main roles: The first is to show the rapid rate that these technologies are affecting the global landscape, emphasizing the compelling need to integrate these topics into educational curricula, as enabling students to grasp the intricacies of decentralized systems and digital currencies will soon be necessary for their full economic participation in society. Second, this article introduces the concepts of blockchain and Bitcoin, presenting their foundational principles followed by their relationship. Both are viewed in terms of their historical development while emphasizing their decentralized and immutable nature, cryptographic security, and blockchain's potential applications beyond cryptocurrencies. Third, this article presents several perspectives from which blockchain, Bitcoin, and other cryptocurrencies might be taught, including historical, technological, legal, social, economic, political, and environmental contexts. [ABSTRACT FROM AUTHOR]

Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour. [ABSTRACT FROM AUTHOR]

Blockchain Technology has grown exponentially in recent years due to its decentralized, immutable, transparent data storage, transaction sharing, and processing capabilities. With the emergence of different blockchain platforms, it is important to analyze and evaluate the performance of these platforms in various scenarios. The popularity of the public blockchain, i.e., Bitcoin and Ethereum, has increased manifold. But in distinction to a public blockchain, there is a permissioned and private blockchain that allows restricted involvement of users in the network. To make a well-informed decision regarding the selection of an appropriate platform for utilization, it is crucial to evaluate diverse performance metrics among the numerous available blockchain platforms. In this study, we assessed the performance of the Hyperledger Fabric blockchain (HLF), taking into account various metrics such as resource consumption, throughput, success rate, and latency. We have incorporated parameters such as the ordering service, programming language to write chaincode/smart contracts, number of transactions, transactions per second, and organizations to evaluate the system's performance. Along with our analysis, we also suggested potential areas of research for future development of blockchain technology. [ABSTRACT FROM AUTHOR]

The introduction of regulated CME futures contracts on Bitcoin in 2017 raised an expectation that cryptocurrencies would become part of mainstream financial markets. This also heightened links between traditional markets and Bitcoin, implying that the cryptocurrency would be subject to systematic spillovers. This paper uses high‐frequency data to examine whether Bitcoin basis risk is linked to investor sentiment from established financial markets. Our findings indicate that extreme investor sentiment, as reflected by the tail risk in various volatility indices, including the VIX, consistently correlates with a negative Bitcoin basis, where Bitcoin futures prices are lower than spot prices. Fluctuations significantly influence this relationship in the trading volume of Bitcoin futures and are more pronounced during periods of substantial unexpected inflation and deflation. These results underline the complex dynamics between market sentiment and cryptocurrency pricing, offering insights with substantial implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

Recently, there is a surge in ransomware activities that encrypt users' sensitive data and demand bitcoins for ransom payments to conceal the criminal's identity. It is crucial for regulatory agencies to identify as many ransomware addresses as possible to accurately estimate the impact of these ransomware activities. However, existing methods for detecting ransomware addresses rely primarily on time-consuming data collection and clustering heuristics, and they face two major issues: (1) The features of an address itself are insufficient to accurately represent its activity characteristics, and (2) the number of disclosed ransomware addresses is extremely less than the number of unlabeled addresses. These issues lead to a significant number of ransomware addresses being undetected, resulting in a substantial underestimation of the impact of ransomware activities. To solve the above two issues, we propose an optimized ransomware address detection method based on Bitcoin transaction relationships, named XRAD, to detect more ransomware addresses with high performance. To address the first one, we present a cascade feature extraction method for Bitcoin transactions to aggregate features of related addresses after exploring transaction relationships. To address the second one, we build a classification model based on Positive-unlabeled learning to detect ransomware addresses with high performance. Extensive experiments demonstrate that XRAD significantly improves average accuracy, recall, and F1 score by 15.07%, 19.71%, and 34.83%, respectively, compared to state-of-the-art methods. In total, XRAD detects 120,335 ransomware activities from 2009 to 2023, revealing a development trend and average ransom payment per year that aligns with three reports by FinCEN, Chainalysis, and Coveware. [ABSTRACT FROM AUTHOR]

This work considers a combinatorial optimization problem in graphs, the nilcatenation problem, and investigates its potential application for detecting money laundering activities in cryptocurrency networks. The nilcatenation problem consists of finding a set of arcs that can be removed from an arc‐weighted directed graph without changing the balance of any vertex. The balance of a vertex is defined as the difference between the sum of the weights of outgoing and incoming arcs. We propose a 0/1 integer linear programming formulation and a local branching algorithm. The approaches are computationally evaluated and compared using three sets of test instances, two of them generated from Bitcoin's testnet and mainnet networks. An experiment on the testnet showed that it is possible to retrieve a nilcatenation artificially introduced with fake bitcoin transactions. Experiments on the mainnet showed that it is possible to find large nilcatenations, possibly indicating money laundering activities. [ABSTRACT FROM AUTHOR]

The blockchain‐enabled healthcare system assists in tackling the inherent issues of centralized healthcare systems, such as data stewardship, single point of failure, and data integrity. However, existing blockchain‐enabled healthcare systems suffer from high‐energy consumption, low transaction throughput, and poor scalability. This work proposes the healthcare framework ParallelChain to tackle the low scalability and high‐energy consumption issue. ParallelChain divides the network into clusters, enabling linear scalability of nodes. The parallel execution of transactions in each cluster improves the throughput. The leader selection process of ParallelChain does not require solving computer‐intensive puzzles, making it more energy efficient than proof‐of‐work. The performance of ParallelChain is validated using metrics such as data transfer, processing time, consensus delay, block congestion, and message exchange. It shows that the amount of data transmitted is 4.6 times more in Bitcoin than ParallelChain with varying nodes. The number of messages exchanged is reduced by 48%, and the processing time is reduced by 2.17 and 1.7 times for varying nodes and block sizes, respectively. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse. Design/methodology/approach: The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results' robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities. Findings: The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run. Originality/value: This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets. [ABSTRACT FROM AUTHOR]

Since Bitcoin was first proposed in late 2008 and went live in 2009, hundreds of research papers have been published trying to understand the behaviour of cryptocurrencies and their impact on financial markets. Their size and importance to the financial sector has increased substantially also has the number of challenges they face and the negative externalities they have caused. This article reviews the related cryptocurrency literature and introduces articles included in this special issue on this theme which were presented at the 2020 Cryptocurrency Research Conference. We conclude by offering possible future research directions. [ABSTRACT FROM AUTHOR]

As the popularity of Bitcoin among finance communities continues, research on the relationship between Bitcoin and conventional commodities is becoming increasingly important. However, to date the literature has not yet found clear evidence of the dynamics of informational linkages between Bitcoin and the crude oil markets. This paper considers both static and time-varying information transmission of volatility, skewness and kurtosis, between two popular Bitcoin markets, Bitstamp and ItBit, and two major crude oil markets, WTI and Brent crude oil. A two-state regime-switching model is employed to estimate higher order moments and Legendre polynomials are applied to specify the time variations of spillovers. In addition, the latent impacts of the recent US–China trade war on spillovers are also explored. We conclude that crude oil is an information transmitter while Bitcoin is an information receiver in terms of the static and time-varying between-market transmission of three types of risk. The effect is affected by the occurrence of the trade war such that the information content of Bitcoin is enhanced once it begins. Moreover, Bitcoin is a diversifier for oil risk and this effect becomes more pronounced in the post-trade war period. [ABSTRACT FROM AUTHOR]

Using daily data over the period August 5, 2013 – September 27, 2019, this study investigates the dynamic spillovers between international monetary policies across four major economies (i.e. Eurozone, Japan, UK and US) and three key cryptocurrencies (i.e. Bitcoin, Litecoin and Ripple). In doing so, we apply a Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model, a dynamic connectedness approach and network analysis. The empirical results indicate that cryptocurrency returns and monetary policy spillovers were particularly large when shadow policy rates became negative, moderated during the Fed's 'tapering process', and sharpened again more recently as cryptocurrency buoyancy returned. Gross directional spillovers suggest that shadow policy rates have more 'to give than to receive', while those from and to cryptocurrency returns are naturally volatile. There is also strong interconnectedness between monetary policy in either the US or the Eurozone and the UK, and between Bitcoin and Litecoin. However, the spillovers across monetary policy and cryptocurrencies tend to be muted. Finally, spillovers were only slightly larger during the Fed's 'unconventional' policy compared to the 'standard' era, but their composition qualitatively changed over time. [ABSTRACT FROM AUTHOR]

This paper proposes a novel asymmetric jump model for modeling interactions in discontinuous movements in asset prices. Given the jump behavior and high volatility levels in cryptocurrency markets, we apply our model to cryptocurrencies to study the impact of various types of jumps occurring in one cryptocurrency's price process on the discontinuity component of the realized volatility of other cryptocurrencies. Our model also allows us to assess the impact of co-jumps. Using high-frequency data to compute the daily realized volatility, we show that downside, upside, and small jumps observed in cryptocurrencies negatively affect the jump component of other cryptocurrencies' realized volatility, while large jumps have the opposite effect. We further find significant asymmetric effects between small and large as well as between downside and upside jumps for several cryptocurrencies. Moreover, we find evidence of co-jumping behavior, which can trigger future jumps. The practical implications of our findings are also discussed. Finally, we extend our analysis to study the effects of jumps in mainstream financial assets on cryptocurrencies' jump behavior and find that upside and downside jumps observed in the S&P 500 index negatively impact cryptocurrency jumps. [ABSTRACT FROM AUTHOR]

Do behavioral factors mediate the relationship between industry returns and Bitcoin returns? We use four industry indices in technology, energy, clean energy, and banking, and the Sentiment index from Thomson Reuters Marketpsych Indices as a behavioral factor to investigate this question. We show that the sensitivities of technology and clean energy industry indices to Sentiment, positively and significantly, strengthen the relationship between sentiment and Bitcoin returns. By showing that behavioral factors mediate the association between the returns of industry indices and Bitcoin returns, we provide evidence that investors' Sentiment captures the association between Bitcoin and sectors related to cryptocurrencies. Our results, however, do not support prior studies' findings of a direct relationship between the industry indices and Bitcoin returns. [ABSTRACT FROM AUTHOR]

We empirically examine whether feedback traders are active in the Bitcoin and the extent to which their presence is affected by a series of noise-related factors (sentiment; volume; liquidity) at three different frequencies (hourly; daily; weekly) for the April 2013–July 2019 period based on Bitstamp data. Our findings suggest that positive feedback trading grows stronger for higher (hourly; daily) frequencies, with its presence manifesting itself mainly during periods of high/improving sentiment and high/rising volume/liquidity. Additional tests reveal that the significance of hourly feedback trading is identified during hours corresponding to the trading hours of major European/North American markets. Overall, our results confirm extant literature evidence on the prevalence of noise trading in cryptocurrencies, while further showcasing that the factors motivating feedback trading in other asset classes (equities; ETFs; futures) exhibit similar effects over the presence of feedback traders in the cryptocurrency market. [ABSTRACT FROM AUTHOR]

This paper analyzes the stability of stablecoins and proposes a framework to test for absolute and relative stability of stablecoins. Based on high-frequency data, we find strong evidence of excess price variations. While Bitcoin is a likely source of this excess volatility because stablecoin returns, volatility and volumes are highly correlated with corresponding Bitcoin time-series, we also demonstrate through a quasi-natural experiment that stablecoins increase the trading volume of Bitcoin. The findings suggest stablecoins play a key role in cryptocurrency markets. [ABSTRACT FROM AUTHOR]

We study the statistical properties of the Bitcoin return series and provide a thorough forecasting exercise. Also, we calibrate state‐of‐the‐art machine learning techniques and compare the results with econometric time series models. The empirical assessment provides evidence that the application of machine learning techniques outperforms econometric benchmarks in terms of forecasting precision for both in‐ and out‐of‐sample forecasts. We find that both deep learning architectures as well as complex layers, such as LSTM, do not increase the precision of daily forecasts. Specifically, a simple recurrent neural network describes a sensible choice for forecasting daily return series. [ABSTRACT FROM AUTHOR]

This paper considers the design of the Bitcoin protocol from a monetary perspective. To this end, it relies heavily on the publications of the creator(s) of Bitcoin, Satoshi Nakamoto, on different mailing lists and forums. It first expounds the political and economic motives which led Nakamoto to build an electronic system of payment based on ‘proof’ rather than on ‘trust’. Then, it considers the predetermined nature of the issue of bitcoins and support the view that one consequence of this issuing rule is that the bitcoin has no ‘monetary standard’ in any of the senses that have been attributed to this term in the literature. Finally, it deals with the concept of ‘synthetic commodity money’ and the widespread comparison of bitcoin to the Iraqi Swiss dinar. The conception of the monetary standard as a measure of the appreciation or depreciation of money is then used to distinguish the bitcoin from the Iraqi Swiss dinar. [ABSTRACT FROM AUTHOR]

The cryptocurrency market has undergone significant turbulence, characterized by enormous volatility shifts, as recently experienced during the COVID-19 shock. Although there is an abundant literature dealing with various aspects of cryptocurrencies, little attention has been devoted to understanding the interconnectedness among different cryptocurrencies, particularly the role of abrupt changes. This paper aims to fill this gap by conducting an intraday analysis to assess the contagion hypothesis within the cryptocurrency markets, with particular focus on the aprubt changes and whether it is a driver of co-aprubt changes in other markets. Specfically, we investigate four major cryptocurrencies (Bitcoin, Ethereum, Ethereum Classic, and Ripple) both prior to and during the COVID-19 shock, April 2019 to September 2020. Using the Diebold and Yilmaz methodology, we decompose the realized volatility into continuous and jump components, and examine how these spillovers evolve across cryptocurrency markets before and during the COVID-19 crisis. Our findings reveal that while the volatility and returns spillovers across the cryptocurrency market escalate during the crisis, there is a notable decrease in the jumps and co-jumps between cryptocurrencies. This suggests that the heightened interdependency observed is not rooted in fundamental factors. Morever, our findings show that spillover is especially prominent in the continuous part of the realised volatility dynamic. Notably, XRP emerges as the predominant transmitter in the context of continuous part of the realized volatility. Our study contributes to the emerging literature on the interconnectedness of price movement/co-mouvement across cryptocurrencies, offers a novel adaptation of the Diebold and Yilmaz methodology to capture the unique features of cryptocurrency prices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cryptography is used by cryptocurrencies to shift money without the intervention of centralized financial institutions. They are decentralized digital assets. On rapidly changing exchanges like those for crypto currencies, it is a tremendously taxing procedure for people to keep track of many simultaneous instantaneous price changes. As a solution to this, computer software that can make fast and objective decisions by constantly observing can replace humans. In this study, the closing price of Bitcoin (BTC), which has the highest volume in the crypto money system, is analyzed. In the study, in which the Gaussian Process Regression (GPR) model and the SMOTE method were used, data belonging to BTC for the period between 25/07/2010 and 05/06/2022 were used as the data set. Opening price, highest-lowest price, volume, dollar index and some indicators used in technical analysis were used as input parameters. The kfold method was followed in the separation of training and test data. The data is divided into 5 subsets with kfold. The mean MAPE value was found to be 1887, and the mean R2 value was found to be 0.99977 in the models with SMOTE. In addition, the GPR model and the GPR model functions that were applied to the SMOTE method were compared by excluding the opening price, which was the price that was highest-lowest, from the data. It was carried out to determine which model performed better. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Accounting & Finance Reviews / Muhasebe ve Finans İncelemeleri Dergisi is the property of Muhasebe & Finans Incelemeleri Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the co-movement between geopolitical risk (GPR), energy price, and bitcoin (BTC) in BRICS countries, namely Brazil, Russia, India, China, and South Africa. Previous studies have focused on the impact of GPR on the volatility and risk premium of BTC investment. However, very limited studies have focused on integrating BTC as an extension of the mix of GPR on the co-movement with energy price. The analysis is based on monthly data of GPR index for BRICS countries, brent oil futures, natural gas futures and BTCs covering the period between March 2012 and Jun 2021. We employ the Bayesian graphical structural vector autoregressive model and time-varying parameter vector autoregressions-based dynamic connectedness to investigate the network-dependence structure. This research project provides useful empirical evidence for assessing the impact of both BTC and GPR on energy prices. Nonetheless, it will also be informative about the likelihood of co-movements occurring at different stages. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose a four-state regime-switching model that pairs low-volatility and high-volatility (HV) states to test eight stock–safe-haven asset portfolios' risk properties. We find the correlations between gold, U.S. T-bond, and the Swiss franc and stock markets are negative or zero in all states, including the HV–HV state, while the correlations between Bitcoin (BTC) and stock markets are positive in the HV–HV state, implying that gold, T-bond, and the Swiss franc are full safe-havens and BTC is a partial safe-haven asset. Moreover, our model is effective in portfolio construction, performing better than conventional time-varying generalized autoregressive conditional heteroskedasticity-based models. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022. Design/methodology/approach: In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term. Findings: The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future. Originality/value: This study has two implications. First, to the best of the authors' knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment. [ABSTRACT FROM AUTHOR]

This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability. [ABSTRACT FROM AUTHOR]

This study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of 12 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7846 technical rules, five log moving average‐based ratios and 59 fundamental factors are used to test predictability and profitability through the Lucky Factors methodology and Superior Predictive Ability test. We observe predictability for a small set of technical and fundamental rules, while only the short‐term log moving average‐based ratio and Hashrate Index demonstrate genuine in‐sample and out‐of‐sample profitability. Our findings question the value of both technical and fundamental analysis on cryptocurrencies. [ABSTRACT FROM AUTHOR]

This article considers a stochastic volatility model featuring an asymmetric stable error distribution and a novel way of accounting for the leverage effect. We adopt simulation‐based methods to address key challenges in parameter estimation, the filtering of time‐varying volatility, and volatility forecasting. More specifically, we make use of the indirect inference method for estimating the static parameters, while the latent volatility is extracted using the extremum Monte Carlo method. Both parameter estimation and volatility extraction are easily adapted to other model specifications, such as those based on other error distributions or on other dynamic processes for volatility. Illustrations are presented for a simulated dataset and for an empirical dataset of daily Bitcoin returns. [ABSTRACT FROM AUTHOR]

As interest in Bitcoin as an investment asset continues to rise, this study investigates how social media influencers' statements impact Bitcoin price through sentiment and time series analysis. The research employs data from Telegram and compares it with X (formerly Twitter) to examine their effects on Bitcoin price and trading volume fluctuations using a Vector Autoregressive model. In the short-term, focusing on the five days following a shock, negative sentiment affects future price changes negatively for the first two days, while positive sentiment has a positive impact; the direction of the shock reverses after the third day. These findings align with the noise trader theory, supporting the interaction between noise traders and rational traders in the Bitcoin market. Further analysis using the NRC Word-Emotion Association Lexicon (EmoLex) to measure the occurrence of eight basic emotion words (joy, trust, fear, surprise, sadness, disgust, anger, anticipation) revealed that messages from Bitcoin channels on Telegram often convey strong trust and anticipation, suggesting a potential stimulation of noise traders' investment activities. In contrast, posts by Bitcoin influencers on X frequently employ emotionally charged words, likely contributing to the spread and amplification of sentiment among noise traders. These results suggest that messages from social media influencers function as a valuable information source for noise traders, potentially impacting their short-term behavior. This study contributes to understanding the dynamics of the Bitcoin market and offers insights into the intersection of social media and Bitcoin market movements, providing valuable information for researchers and investors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims to investigate the interoperability of the Bitcoin blockchain by comparing the US dollar prices of five cryptocurrencies derived from the Bitcoin price with their corresponding market prices. The deviation rate between the derived price and the market price, referred to as the arbitrage return rate, is examined with respect to its adherence to the efficient market hypothesis and martingale theory principles, specifically regarding mean-reversion and serial independence. Hurst exponents are estimated using R/S and DFA methods, and their dynamics are analyzed using a sliding window technique. Our findings demonstrate that the Bitcoin blockchain effectively facilitates transactions among the five cryptocurrencies, though evidence suggests a potential structural change in Bitcoin blockchain interoperability following April 2023. [ABSTRACT FROM AUTHOR]

Cryptocurrencies' popularity is growing despite short-term fluctuations. Peerreviewed research into trust in cryptocurrency payments started in 2014. While the model created then, is based on proven theories from psychology and supported by empirical research, a-lot has changed in the past 10 years. This research finds that the original model is still valid, but it is extended to capture the current situation better. A quantitative methodology is used to validate the updated model proposed. The results from the quantitative survey show that (1) personal innovativeness in technology and (2) finance, influence (3) disposition to trust. Disposition to trust influences six variables from the specific context of the payment. Three variables related to the cryptocurrency itself are (4) stability in the value, (5) transaction fees, and (6) reputation. Institutional trust is influenced by (7) regulation, and (8) payment intermediaries. The last contextual factor is (9) trust in the retailer. The six variables from the context influence (10) trust in the payment which, finally, influences (11) the likelihood of making the cryptocurrency payment. [ABSTRACT FROM AUTHOR]

It is commonly assumed that features of cryptocurrency such as its pseudo-anonymity and transnationality will be attractive to criminals. However, the public nature of the Blockchain questions the usefulness of this technology for crime. The aim of this research is to find out whether there are characteristics that are more attractive for the use of Bitcoin in crime. A qualitative analysis of the content of a Darknet forum was conducted. The term “Bitcoin” was used, and discussions were selected according to a previously conducted coding. Themes of interest were identified, and analysis was carried out using NVivo software. The discussions were categorized into themes: evading detection by authorities, exploiting cryptocurrencies for crime, recommendations, lessons in cybersecurity, regulation, and strategies to prevent victimization. The findings reveal that the critical factor is not the selection of a specific cryptocurrency but rather the establishment of conversion routes to circumvent traceability. Furthermore, the use of mixers, which are generally disapproved of within the community, is not recommended. Lastly, ample information is accessible for individuals to autonomously engage with this technology. In conclusion, while it is presumed to be a useful instrument for criminal activities, there are instances where alternative tools, including cash, are preferred. [ABSTRACT FROM AUTHOR]

Although the 2022 cryptocurrency market crash prompted despair among investors, the rallying cry, "wagmi" (We're all gonna make it.) emerged among cryptocurrency enthusiasts in the aftermath. Did cryptocurrency enthusiasts respond to this crash differently compared to traditional investors? Using natural language processing techniques applied to Twitter data, this study employed a difference-in-differences method to determine whether the cryptocurrency market crash had a differential effect on investor sentiment toward cryptocurrency enthusiasts relative to more traditional investors. The results indicate that the crash affected investor sentiment among cryptocurrency enthusiastic investors differently from traditional investors. In particular, cryptocurrency enthusiasts' tweets became more neutral and, surprisingly, less negative. This result appears to be primarily driven by a deliberate, collectivist effort to promote positivity within the cryptocurrency community ("wagmi"). Considering the more nuanced emotional content of tweets, it appears that cryptocurrency enthusiasts expressed less joy and surprise in the aftermath of the cryptocurrency crash than traditional investors. Moreover, cryptocurrency enthusiasts tweeted more frequently after the cryptocurrency crash, with a relative increase in tweet frequency of approximately one tweet per day. An analysis of the specific textual content of tweets provides evidence of herding behavior among cryptocurrency enthusiasts. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a relatively new technology that poses significant legal challenges. Specifically, cryptocurrency causes substantial risk to consumers when a cryptocurrency company files for Chapter 11 bankruptcy. These risks are unprecedented, and the law fails to provide consumers with protection when a cryptocurrency company files for Chapter 11 bankruptcy. Many of the risks associated with cryptocurrency exchanges and Chapter 11 bankruptcy can be attributed to the lack of federal and state regulation. Additionally, multiple cryptocurrency companies, including Voyager Digital Holdings, FTX, and Celsius, have filed for Chapter 11 bankruptcy, which required judges to interpret various sections of Chapter 11 of the U.S. Bankruptcy Code in an unprecedented way. The federal government, its agencies, and state governments are only starting to attempt to provide a solution by regulating cryptocurrency. President Biden issued an executive statement, and the Government Accountability Office issued a report that directs financial agencies to consider regulating cryptocurrency, but the federal government has failed to take affirmative steps toward its regulation. Further, Congress has not successfully passed a bill to regulate cryptocurrency at the federal level. North Dakota is a leader in Bitcoin mining by promoting cryptocurrency transactions and encouraging cryptocurrency businesses to do business within the state. North Dakota has also issued guidance to credit unions within the state and considered adopting a central bank currency. Similarly, Wyoming and New York have enacted laws regulating cryptocurrency. While neither state's law is perfect, each mitigate the harmful effects that cryptocurrency can have on consumers. Since cryptocurrency is still relatively new and lacks federal regulation, the North Dakota legislature should adopt statutes similar to Wyoming and New York, while keeping federal policy initiatives in mind. Ultimately, North Dakota must regulate cryptocurrency to protect consumers and provide bankruptcy courts with useful guidance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]