Major Cryptocurrencies Research Articles

Proof of Work with Random Selection (PoWR): An Energy Saving Consensus Algorithm with Proof of Work and the Random Selection Function

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.

Revisiting the dynamics of major cryptocurrencies

Purpose – This study aims to reassess the dynamics of major cryptocurrencies sur-rounding recent economic and geopolitical events. By employing wavelet analysis and quantile regression methods, it seeks to understand the behavior of cryptocurrencies before, during, and after the COVID-19 pandemic. Research methodology – This research employs the Least Asymmetric Daubechies (LA8) wavelet function to decompose log-returns of major cryptocurrencies into various frequency scales. Additionally, it utilizes wavelet coherence and quantile-on-quantile regression techniques to analyze daily price data spanning from July 2017 to May 2024. Findings – The findings reveal a strong long-term association among cryptocurrencies, with a decline in medium-term correlations. Bitcoin exhibits synchronization with major cryptocurrencies, excluding Tether, while BTC-ETH and BTC-BNB display a rapid, interconnected behavior alongside their fundamental links. Moreover, empirical evidence indicates Bitcoin’s heterogeneous nexus with other alternatives, showcasing greater sensitivity to positive extremes over negative ones. Research limitations – The study’s scope is delimited by the selected time frame (July 2017 to May 2024) for data analysis, potentially limiting insights into longer-term trends. Additionally, the reliance on specific methodologies like wavelet analysis might introduce constraints in capturing the entirety of cryptocurrency dynamics, leaving room for alternative interpretations or unexplored aspects. Practical implications – Results suggest that understanding the varying correlations among major cryptocurrencies during different market phases could aid investors and policymakers in devising more nuanced strategies. Recognizing the sensitivity of Bitcoin’s connections with alternatives to market trends could inform risk management approaches, particularly in navigating extreme market conditions. Originality/Value – The originality of this study lies in its comprehensive examination of cryptocurrency dynamics across varying time scales, utilizing wavelet analysis and quantile regression techniques. The findings offer valuable insights into the complex interconnections among cryptocurrencies, especially in terms of their sensitivity to different market conditions, providing a nuanced perspective for investors, analysts, and policymakers navigating the crypto landscape.

Ethereum futures and the efficiency of cryptocurrency spot markets

This paper examines the impact of the introduction of Ethereum futures contracts on the market efficiency of major cryptocurrency (Bitcoin, Ethereum, Ripple, Litecoin, and Dogecoin) spot prices. Using a multifractality-based approach and daily data from September 4, 2017 to February 16, 2024, the main results show a slight improvement in market efficiency. Specifically, the degree of multifractality persistence decreases, implying reduced market inefficiencies in major cryptocurrencies. The temporal linear correlation effect and thick tail effect are less pronounced post-launch. The asymmetry of the generalized Hurst exponent increases after the launch of Ethereum futures, with a higher persistence under the downward trend of cryptocurrencies noted. This downward trend emerges after the launch of Ethereum futures, coinciding with the final stage of the COVID-19 pandemic. Additional analysis shows that fat tails and temporal linear correlations are the main sources of multifractality. The results highlight the influence of introducing financial derivatives into the relatively new and volatile cryptocurrency area, which should concern traders, hedgers, investors, and regulators.

Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency

Evaluating the sophisticated digital assets and cryptocurrencies capacities of substituting international currencies in inflationary eras

This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen) exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international currencies act as hedgers against shocks while major cryptocurrencies are only modest generators with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly rely on a few very strong leaders –Gala being by far the strongest- to have an impact, imitating Bitcoin in the early cryptocurrency era.

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.

Cryptocurrencies Price Prediction Using Deep Learning Models (Gated Recurrent Unit And Recurrent Neural Network))

Background: With their volatile prices, cryptocurrencies have become valuable assets in the financial market. Predicting cryptocurrency prices accurately is essential for making well-informed investment decisions. Time series prediction models, like Gated Recurrent Unit (GRU) and Recurrent Neural Networks (RNN), are popular tools for financial data forecasting because they can capture sequential dependencies in data. Aim: This study aims to predict the average monthly closing prices of five major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Litecoin (LTC), and Ripple (XRP)—using GRU and RNN models and evaluate their performance in forecasting these prices. Method: Time series input sequences were produced and historical price data for the chosen cryptocurrencies were preprocessed using Min-Max Scaling. This data was divided into training and test sets, and it was used to train both the GRU and RNN models. The Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) were used to assess the performance of the model. Results: For the majority of cryptocurrencies, the RNN model exhibited better predicted accuracy and consistently outperformed the GRU model. For instance, the RMSE for Ripple was 0.06 for the RNN model and 0.09 for GRU. In a similar vein, the RNN model outperformed the GRU model with a MAPE of 12.97% for Ethereum. These results imply that RNN models are more suitable for financial forecasting in this sector, as they yield more accurate predictions for cryptocurrency values.

Inner Multifractal Dynamics in the Jumps of Cryptocurrency and Forex Markets

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.

Cryptocurrencies as a vehicle for capital exodus: Evidence from the Russian–Ukrainian crisis

Cryptocurrencies provide an escape from the conventional financial system and its regulations and could therefore become increasingly popular in the midst of geopolitical uncertainties. We analyze the linkage of the Russia–Ukraine conflict and the trading volume of 16 major cryptocurrencies via event study methodologies, based on a geopolitical risk index. The results show that the trading volume of most cryptocurrencies is positively affected by the events of the conflict. This is especially true for payment tokens and most utility coins. Interestingly, stablecoins show only fewer trading volumes before the actual event. Among utility tokens, Ripple in particular is positively influenced.

Dynamic evolution of causal relationships among cryptocurrencies: an analysis via Bayesian networks

AbstractUnderstanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Examining cryptocurrency Trends in the Wake of COVID-19: Addressing volatility challenges

This study examines the effect of the COVID-19 on the liquidity and volatility of nine major cryptocurrencies Trends in the Wake of the pandemic, including Cardano, BNB, Bitcoin, BUSD, Dogecoin, Ethereum, Coin, Tether, and XRP. Using daily data from January 22, 2020, to December 14, 2022, the research reveals a significant increase in overall cryptocurrency market volatility during the pandemic. Contrary to the initial vision of cryptocurrencies as efficient transaction mediums, the study suggests that heightened volatility challenges their suitability for rapid settlement. Additionally, the findings highlight cryptocurrencies’ speculative nature, lacking intrinsic ties to tangible assets and proving ineffective as secure havens or hedges against inflation. The dynamic and unpredictable nature of cryptocurrencies poses challenges to their envisioned roles in the financial landscape, prompting a reevaluation of their utility and stability amid global economic uncertainties.

Volatility spillover effect between cryptocurrency and stock market using MGARCH Bekk model

This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.

Application of Event Study Methodology in the Analysis of Cryptocurrency Returns

ABSTRACT This study employs event study methodology to investigate the impact of various types of events on cryptocurrency market returns and volatility. The research focuses on six major cryptocurrencies—ETH, BTC, BNB, XRP, DOGE, and TRX—over the period from December 31, 2017, to October 30, 2023. Six types of events are analyzed: cybersecurity events, block reward adjustment events, political conflict events, public health emergency events, cryptocurrency recognition and support events, and social media sentiment events. The findings reveal that cybersecurity and block reward adjustment events have minimal and short-lived impacts on market returns. Political conflict events cause significant short-term return volatility depending on market expectations. Public health emergency events, such as the COVID-19 pandemic, have significant and lasting negative impacts on market returns. Cryptocurrency recognition and support events have significant and sustained positive impacts on market returns. Social media sentiment events have significant but short-lived impacts on market returns. The robustness of the results was validated through the analysis of abnormal returns during the event period. This study provides valuable insights for investors and policymakers in managing market volatility.

Identification of Expected Growth in Crypto Currency

Identifying the expected growth in cryptocurrency involves analyzing a combination of market trends, technological advancements, regulatory developments, and economic indicators. Historical performance and adoption rates of major cryptocurrencies provide insight into market trends, while innovations in blockchain technology, such as Ethereum 2.0 and Layer 2 solutions, along with the rise of decentralized finance (DeFi) and non-fungible tokens (NFTs), highlight significant technological advancements. Regulatory developments, including supportive legislation and the involvement of institutional investors through financial products like Bitcoin ETFs, play a crucial role in shaping market confidence and investment. Economic indicators, such as inflation, monetary policies, and global events, also influence interest in cryptocurrencies as alternative assets. Investor sentiment, driven by public perception, media coverage, and social media activity, impacts market dynamics. Additionally, research from financial analysts, market research firms, and academic studies, along with corporate partnerships and the integration of crypto solutions with traditional systems, contribute to growth predictions. Monitoring market capitalization and trading volumes further helps gauge market interest and liquidity. By considering these multifaceted factors, a more comprehensive understanding of the potential growth in the cryptocurrency market can be achieved.

Crypto-Mining and Electricity Market Nexus: Assessing the Evidence from the Nordic Electricity Prices

This paper examines the the dynamic relationship between the cryptocurrency market, through two major cryptocurrencies namely Bitcoin and Ethereum, and the Nordic electricity market. Specifically, it examines how the two main cryptocurrencies’ prices volatilities affect the Nordic electricity prices. Our analysis is based on daily spot prices from 08 August 2015 to 16 June 2020, using the dynamic conditional correlation DCC-GARCH model. Our results confirm the existence of a medium conditional correlation between cryptocurrencies and Nordic electricity market. Especially at the end of 2018, this correlation is significantly important. During this period, a peak in Ethereum prices was noticed which impact electricity prices via the high demand of power by miners who are attracted by such low prices, in the Nordic region, to increase the profitability of their activity.

The trend of digital finance: unveiling the multidimensional network of cryptocurrency risk propagation

ABSTRACT This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.

Asymmetric connectedness between conventional and Islamic cryptocurrencies: Evidence from good and bad volatility spillovers

This paper examines the dynamics of the asymmetric volatility spillovers across four major cryptocurrencies comprising nearly 61% of cryptocurrency market capitalization and covering both conventional (Bitcoin and Ethereum) and Islamic (Stellar and Ripple) cryptocurrencies. Using a novel time-varying parameter vector autoregression (TVP-VAR) asymmetric connectedness approach combined with a high frequency (hourly) dataset ranging from 1st June 2018 to 22nd July 2022, we find that (i) good and bad spillovers are time-varying; (ii) bad volatility spillovers are more pronounced than good spillovers; (iii) a strong asymmetry in the volatility spillovers exists in the cryptocurrency market; and (iv) conventional cryptocurrencies dominate Islamic cryptocurrencies. Specifically, Ethereum is the major net transmitter of positive volatility spillovers while Stellar is the main net transmitter of negative volatility spillovers.

Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning

AbstractThis paper introduces a novel statistical testing technique known as segmented detrended multifractal fluctuation analysis (SMF‐DFA) to analyze the structured scaling properties of financial returns and predict the long‐term memory of financial markets. The proposed methodology is applied to assess the efficiency of major cryptocurrencies, expanding upon conventional approaches by incorporating different fluctuation regimes identified through a change‐point detection test. A single‐factor model is employed to characterize the endogenous factors influencing scaling behavior, leading to the development of a self‐explanatory machine learning approach for price forecasting. The proposed method is evaluated using daily data from three major cryptocurrencies spanning from April 2017 to December 2022. The analysis aims to determine whether the digital market has experienced significant changes in recent years and assess whether this has resulted in structured multifractal behavior. The study identifies common periods of local scaling among the three prices, with a noticeable decrease in multifractality observed after 2018. Furthermore, complementary tests on shuffled and surrogate data are conducted to explore the distribution, linear correlation, and nonlinear structure, shedding light on the explanation of structured multifractality to some extent. Additionally, prediction experiments based on neural networks fed with multi‐fractionally differentiated data demonstrate the utility of this new self‐explanatory algorithm for decision‐makers and investors seeking more accurate and interpretable forecasts.

Support vector regression-based heteroscedastic models for cryptocurrency risk forecasting

In this study, we aimed to combine support vector regression (SVR) with an autoregressive (AR) model and a generalized autoregressive conditional heteroscedastic (GARCH) or asymmetric GARCH model. We compared the performances of these combined models with the maximum likelihood estimation (MLE)-based models under a normal, Student’s t, or skewed Student’s t distributional assumption. We employed such models to protect the heteroscedastic risks of four major cryptocurrencies, namely, Bitcoin, Ethereum, Tether, and Binance Coin, before and during COVID-19. We accomplished this task by forecasting their volatility, Value-at-Risk (VaR), and Expected Shortfall (ES). The empirical results revealed that in terms of the root-mean-square error (RMSE) and mean-absolute error (MAE) metrics, the SVR-based models with a polynomial or radial basis kernel function, which is nonlinear, resulted in a more accurate volatility forecast compared to the MLE-based models under any distributional assumption. The superiority of the former models was more apparent when applied to Tether exhibiting the most stable volatility. This supports evidence of nonlinear characteristics in the cryptocurrency return and volatility series. A combination between SVR and an asymmetric GARCH model also confirms the presence of an asymmetrically nonlinear relationship between the returns and volatility. In addition, the correct-VaR and Kupiec’s backtesting results showed that the SVR-based models tended to produce the VaR forecasts for the four cryptocurrencies with better accuracy at the 1%, 5%, and 10% significance levels compared to the MLE-based ones. The use of the correct-ES metric also leads to the superior ES forecasting performance of the former models at any level. However, McNeil and Frey’s backtesting results demonstrated their worse ES forecasting performance at the 1% level. As the COVID-19 pandemic progressed, we also found an increase in the Bitcoin and Binance Coin market risks as well as a decrease in the Ethereum and Tether market risks.

The driving forces behind the prices of major cryptocurrencies: Evidence from the past five years

Purpose: The primary objective of this study is to uncover the complex relationship between cryptocurrency prices and significant global events that transpired within the past five years. These events encompass a wide spectrum, including political and global health crises, the public disclosure of multinational enterprises' investments in cryptocurrencies, and the influence of macroeconomic indicators. Beyond the exclusive focus on Bitcoin, this study adopts a more comprehensive approach encompassing various cryptocurrencies.Design/methodology/approach: This study examines the effects of the disclosure of cryptocurrency adoption plans by major corporations, the Russian-Ukraine war, Covid-19, Inflation rate, and Economic policy uncertainty within the U.S., U.K., and E.U on cryptocurrency prices (Bitcoin, Ethereum, and Binance coin) using a structural equation model over the past five years.Findings: Our findings provided several compelling insights. Most notably, during the study period major corporations’ adoption of cryptocurrencies positively influenced their price. Furthermore, a negative and significant association emerges between cryptocurrency prices and periods marked by economic policy uncertainty and inflation rates in the countries under investigation (U.S., U.K., and E.U). The results are robust under variations in sample composition and changes in sets of variables.Originality/value: The study uncovered the complex relationship between cryptocurrency price fluctuations and significant global events that transpired within the past five years by taking the factors identified in previous literature as a whole and adding new variables that are not well studied, such as the effect of Russian-Ukraine War and multinational corporations revealing cryptocurrency adoption intention on the prices of cryptocurrencies. This study represents a pivotal contribution by bridging a crucial research void and providing theoretical insights into the legal considerations to be undertaken by policymakers, and informed investment practices by traders, and corporate leaders.