The valuation and prediction of cryptocurrency prices have become increasingly important in the financial market. Therefore, this study aims to focus on the selection and evaluation of machine learning models for cryptocurrency valuation. Thus, two types of machine learning models, gradient boosting trees (Xgboost and LightGBM) and neural networks, are compared to determine their effectiveness in generating features for cryptocurrency valuation. Additionally, correlation tests are conducted to identify the most suitable input variables for the models. The results demonstrate that the generated features have a significant impact on the accuracy of machine learning predictions for cryptocurrency prices. It highlights the potential of machine learning models in accurately predicting and evaluating the value of cryptocurrencies. Overall, the findings of this study contribute to the understanding of the role of machine learning in cryptocurrency valuation and provide valuable insights for investors and researchers. By leveraging machine learning techniques, investors can make informed decisions and develop effective investment strategies in the cryptocurrency market. This study contributes to cryptocurrency valuation research. Leveraging machine learning enables informed decisions and effective investment strategies. Furthermore, the findings inform the development of advanced machine learning models and algorithms for cryptocurrency valuation.
Yuxin Zhang, Rajiv Garg, Linda L. Golden, Patrick L. Brockett Ā· 5 authors
Cryptocurrencies like Bitcoin have received substantial attention from financial exchanges. Unfortunately, arbitrage-based financial market price prediction models are ineffective for cryptocurrencies. In this paper, we utilize standard machine learning models and publicly available transaction data in blocks to predict the direction of Bitcoin price movement. We illustrate our methodology using data we merged from the Bitcoin blockchain and various online sources. This gave us the Bitcoin transaction history (block IDs, block timestamps, transaction IDs, sendersā addresses, receiversā addresses, transaction amounts), as well as the market exchange price, for the period from 13 September 2011 to 5 May 2017. We show that segmenting publicly available transactions based on investor typology helps achieve higher prediction accuracy compared to the existing Bitcoin price movement prediction models in the literature. This transaction segmentation highlights the role of investor types in impacting financial markets. Managerially, the segmentation of financial transactions helps us understand the role of financial and cryptocurrency market participants in asset price movements. These findings provide further implications for risk management, financial regulation, and investment strategies in this new era of digital currencies.
This paper investigates portfolio optimization methodologies and short-term investment strategies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from June 2020 to March 2024. Using hourly data, we apply the Kurtosis Minimization methodology, along with other optimization strategies, to construct and assess portfolios across various rebalancing frequencies. Our empirical analysis reveals significant volatility, skewness, and kurtosis in cryptocurrencies, highlighting the need for sophisticated portfolio management techniques. We discover that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, especially in shorter-term investment horizons, delivering optimal returns to investors. Additionally, our findings emphasize the importance of dynamic portfolio management, stressing the necessity of regular rebalancing in the volatile cryptocurrency market. Overall, this study offers valuable insights into optimizing cryptocurrency portfolios, providing practical guidance for investors and portfolio managers navigating this rapidly evolving market landscape.
In light of recent cryptocurrency value fluctuations, Bitcoin is gradually gaining recognition as an investment vehicle. Given the market's inherent volatility, accurate forecasting becomes crucial for making informed investment decisions. Notably, previous research has utilized machine learning methods to enhance the accuracy of Bitcoin price predictions. However, few studies have explored the potential of employing diverse modeling methods for sampling with varying data formats and dimensional characteristics. This study aims to identify the internal feature subset that yields the highest returns in forecasting Bitcoin's price. Specifically, Bitcoin's internal features were categorized into four groups: currency data, block details, mining information, and network difficulty. Subsequently, a long short-term memory (LSTM) artificial neural network was employed to predict the next day's Bitcoin closing price, utilizing various categorizations of feature subsets. The model underwent training using two and a half years of historical data for each feature. The findings revealed a mean absolute error rate of 6.38% when modeling with the block details category features. This enhanced performance primarily stemmed from the positive relationship between Bitcoin price and this data subset's low ambiguity. Experimental results underscored that, compared to other investigated feature subsets, the categorization of block detail features provided the most accurate Bitcoin price predictions, laying the foundation for future research in this domain.
Purpose This paper aims to investigate technological innovations within the crypto space that have engendered novel financial crime risks and their potential utilization amidst geopolitical conflicts. Design/methodology/approach The theoretical paper uses an analysis of recent geopolitical events, with a key focus on using cryptocurrencies to undertake illicit activities. Findings The study found that cryptocurrencies and the innovations made within the crypto domain are used for both legitimate and illicit purposes, including money laundering, terrorism financing and sanction evasion. Originality/value This research contributes to understanding the critical role cryptocurrencies play amidst geopolitical conflicts and emphasizes the need for regulatory considerations to prevent their misuse. To the best of the authorsā knowledge, this paper is the first scholarly contribution that considers the evolving mechanisms afforded by cryptocurrencies amidst geopolitical conflicts in undertaking illicit activities.
This paper introduces cryptocurrency into a two-country open-economy model. Based on the theoretical model, we employ the TVP-VAR model to study the dynamic interdependence among interest rate spread (a proxy in the monetary market), exchange rate (a proxy in the forex market), and Bitcoin transactions (a proxy in the cryptocurrency market). The key finding is that Bitcoin has an effect of de-fiatization in the global financial market. When there is a higher divergence in monetary policy between the US and China, Bitcoin attracts greater attention with a higher price, posing a competing force against USD. When there are greater fluctuations in the exchange rate of USD/CNY, Bitcoin diverts investors from CNY. The fiat currencies of the two largest economies are both losers while Bitcoin gains. Therefore, cryptocurrency not only decentralizes the role of commercial banks as a medium of payment, but also decentralizes the role of central banks as a monetary policymaker. In face of this challenge, it is suggested that central banks should embrace blockchain technology and develop their own digital currency to restore the trust lost in the global financial crisis. International collaborations in terms of regulation are necessary given its borderless and authority-less feature.
There is increased interest in the dynamic relationships between cryptocurrency and commodity futures. This study examines the hedging performance of four well-known commodity futures against fluctuations in Bitcoin prices. Furthermore, this study used the DCC- and ADCC-MGARCH models to estimate conditional correlations and time-varying optimal hedge ratios between the returns of copper, gas, gold, and crude oil futures, and Bitcoin. We use a rolling window method to calculate one-step-ahead time-varying optimal hedge ratios and evaluate hedging performance. The empirical results show that gas and gold have hedge benefits to Bitcoin. However, crude oil shows poor hedge performance. From the results of one-step-ahead hedge ratios, for copper and oil, we find that hedge ratios increased and hedge effectiveness improved since the COVID-19 outbreak.
Ritesh Patel, Mariya Gubareva, Muhammad Zubair Chishti, Š¢Š°Š¼Š°ŃŠ° Š¢ŠµŠæŠ»Š¾Š²Š°
This paper studies dynamic connectedness between four prominent healthcare cryptocurrencies, namely MediBloc, MediShares, Medicalchain, and Dentacoin, and bond, equity, and commodity markets along with USD and Bitcoin. The daily data span from February 2018 to April 2023. We applied the quantile VAR method and the wavelet quantile correlation approach to measure the connectedness. The quantile VAR method reveals a strengthening interrelation among the assets during COVID-19 and Russia-Ukraine military conflict, while the wavelet quantile correlation highlights the existing diversification opportunities. To test portfolio performance, we resort to minimum variance portfolio, minimum correlation portfolio and the recently developed minimum connectedness portfolio techniques. The minimum variance portfolio is best performing portfolio with highest Sharpe ratio. In addition, considering the minimum connectedness and minimum correlation portfolios, the exposure to healthcare cryptocurrencies also improves portfolio diversification. Based on the hedging effectiveness, we show that the investment in the healthcare cryptocurrencies reduces the volatility for all the selected portfolios though currently in a limited degree. However, the investors are advised to regularly monitor their asset allocation as, with the passage of time, the strength of hedging attributes may change. Our study provides important implications for policymakers and the portfolio managers.
Ewa FederāSempach, Piotr Szczepocki, Joanna BogoÅÄbska
Abstract This article investigates five safe-haven asset responses from 2014 to 2022, including the unprecedented COVID-19 crisis, Russian invasion of Ukraine, and sharp US interest rate increases of 2015 and 2022. We apply the unique approach of the multivariate factor stochastic volatility (MSV) model, which is extremely efficient for financial market analysis and allows us to conduct dynamic factor analysis of safe-haven relationships that cannot be observed directly. The research sample consists of five prospective safe-haven assetsāgold, bitcoin, the euro, the Japanese yen, and the Swiss francāand five primary world stock market indicesāthe S&P 500, Financial Times Stock Exchange (FTSE) 100, DAX, STOXX Europe 600, and Nikkei 225. Our findings are useful for investors searching for the best safe-haven assets among gold, bitcoin, and currencies to hedge against financial turmoil in global stock markets. Our unique findings suggest that safe-haven effects work differently for gold and the yen; that is, the Japanese yen acts as the strongest safe haven across all stock indices. Bitcoin is not a strong safe-haven currency since it has zero days of negative correlations with the considered stock indices, but it is a weak safe-haven during times of financial distress. Consequently, we state that strong and weak safe-haven properties vary across time and place. The novelty of our study lies in the methodological complexity of the MSV model (used for the first time to find the best safe-haven asset properties), dynamic factor analysis, a long-term research sample covering the Russian invasion of Ukraine in 2022, and an international investor perspective focusing on the worldās leading stock markets. We extend earlier studies by analyzing the interrelations of the worldās leading stock market indices with five potential safe-haven assets during the long period of 2014ā2022 and using a unique dynamic factor analysis to show the differentiated behaviors of the Japanese yen and gold. Additionally, the main innovative contribution is a new framework of weak and strong safe-haven asset classifications not previously applied in the literature.
Richard Harris, Murat MazibaÅ, Dooruj Rambaccussing
Cryptocurrencies are characterized by high volatility and low correlations with traditional asset classes, and present an intriguing investment opportunity. However, their inherent risks and regulatory uncertainties make direct investment challenging for many investors. This paper addresses this challenge by proposing a replication framework that employs machine learning to create synthetic portfolios that replicate the risk-adjusted return profile and diversification benefits of Bitcoin, by far the largest cryptocurrency by market share. We show that the synthetic portfolios offer a compelling alternative to direct investment in Bitcoin, delivering superior risk-adjusted returns net of trading costs while mitigating the risks that are associated with holding Bitcoin directly. Furthermore, the synthetic portfolios provide better diversification benefits and lower tail risk.
Bassam A. Ibrahim, Ahmed A. Elamer, Thamir Hamad Alasker, Marwa Ali Mohamed Ā· 5 authors
Abstract The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets, particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic. This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets, focusing on gold and six major stock markets (Japan, USA, UK, China, Germany, and France) using daily data from January 2, 2011, to June 2, 2022, with 2958 daily observations. We employ DCC-GARCH, wavelet coherence, and cascade-correlation network models to analyze the relationship between Bitcoin and those markets. Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty. We also find evidence of long-term contagion between Bitcoin and the six stock markets, with short-term contagion observed in Chinese and Japanese markets during COVID-19. These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets. Hedge funds, mutual funds, and individual and institutional investors can benefit from using our findings in their risk management strategies. Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions. It holds significant implications for stock market investors and policymakers, providing evidence for potentially using cryptocurrencies for hedging, for diversification, or as a safe haven.
This study investigates the influence of structural change on the diversification, hedging, and safe-haven characteristics of Bitcoin and Ethereum against various financial assets such as gold, the US Dollar Index, stock indices, oil, and commodity indices from August 7, 2015, to August 15, 2022, using the DCCāARMAāGARCH models with the CUSUM test. Our results indicate that cryptocurrencies have the same characteristics vis-Ć -vis financial markets during the entire sample period and periods tied to the date of major international events (COVID-19 and the early-2022 RussiaāUkraine War). However, we find that cryptocurrencies play different roles against specific asset markets in different periods separated by structural change models. Our findings suggest that incorporating structural changes into a model accounts for higher volatility and may better describe the real-world capabilities of cryptocurrencies against financial assets.
ErdinƧ Akyıldırım, Ahmet Faruk Aysan, OÄuzhan Ćepni, Ćzge Serbest
This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market.
This study examines the day-of-the-week (DoW) and month-of-the-year (MoY) effects in the cryptocurrency market, with a focus on Bitcoin (BTC) and Ethereum (ETH). Due to the absence of a specific closing time in the cryptocurrency market, the closing time of the daily data is taken as 23:59 UTC. Initially, an appropriate volatility model for the cryptocurrency market is established using the GARCH, EGARCH, and TGARCH models. The most appropriate model for BTC is ARMA(1,0)-EGARCH(1,1) and ARMA(1,0)-GARCH(1,1) for ETH. The results of the analysis indicate a leverage effect in the cryptocurrency market, where negative shocks cause a more significant increase in volatility than positive shocks. Based on this volatility structure, the DoW and MoY are analyzed. For BTC, returns on other days are lower compared to Mondays. However, for ETH, returns on Thursdays are lower than those on Mondays. In terms of volatility, both BTC and ETH show that the highest volatility occurs on Mondays. For the MoY effect, neither BTC nor ETH donāt exhibit a significant effect in the mean equation. Nevertheless, the variance equation indicates that January has higher volatility compared to other months, indicating the presence of a MoY effect in terms of volatility.
Abstract This study investigates the static and dynamic return and volatility spillovers between non-fungible tokens (NFTs) and conventional currencies using the time-varying parameter vector autoregressions approach. We reveal that the total connectedness between these markets is weak, implying that investors may increase the diversification benefits of their multicurrency portfolios by adding NFTs. We also find that NFTs are net transmitters of both return and volatility spillovers; however, in the case of return spillovers, the influence of NFTs on conventional currencies is more pronounced than that of volatility shock transmissions. The dynamic exercise reveals that the returns and volatility spillovers vary over time, largely increasing during the onset of the Covid-19 crisis, which deeply affected the relationship between NFTs and the conventional currencies markets. Our findings are useful for currency traders and NFT investors seeking to build effective cross-currency and cross-asset hedge strategies during systemic crises.
This research aims to empirically investigate the portfolio risk associated with crypto assets. In other words, we want to investigate whether the inclusion of crypto assets in a portfolio can minimize the portfolio risk or not, because it is argued that there is a lower degree of correlation between crypto assets and traditional assets. In order to achieve our research objectives, we employ the Vector Autoregressive Model (VAR) by using five different asset classes. The first two variables are taken from the crypto assets, Bitcoin and Ethereum, and the remaining three variables for Gold, Crude Oil and VIX (Chicago Board Options Exchange's (CBOE) volatility index). Our research strategy will be based on an analysis for unit root, optimal lag selection, coefficient matrix, checking VAR stability, the Granger causality test, and impulse response function (IRF). Our findings suggest that none of the indicators of traditional assets drive and explain Bitcoin. We also found that only Bitcoin is significantly related to Ethereum. while none of the other variables are statistically useful to explain the variation in the Ethereum. Based on these findings it can be recommended that the inclusion of crypto assets into a portfolio reduces risk because none of the indicators of crypto assets are significantly related to the indicators of traditional assets.
Yoochan Kim, Erkan Topal, Apurna Ghosh, Mohammad Waqar Ali Asad
COVID-19 and SARS are epidemics which have influenced the largest global economic crisis in recent years. This research reveals that both SARS and COVID-19 have led to fluctuations in the prices of gold and the US dollar index; however, there is no direct causal relationship be-tween COVID-19 and the price of bitcoin. The USD index saw a significant increase during the SARS outbreak, while gold prices surged during the COVID-19 pandemic. The notion that cryptocurrency will surpass the value of gold or traditional currencies seems improbable, given the lack of evidence linking bitcoin prices to COVID-19. Gold is expected to maintain its value in the long term, offering lower risk compared to other currencies.
In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.
This study presents an innovative approach for predicting cryptocurrency time series, specifically focusing on Bitcoin, Ethereum, and Litecoin. The methodology integrates the use of technical indicators, a Performer neural network, and BiLSTM (Bidirectional Long Short-Term Memory) to capture temporal dynamics and extract significant features from raw cryptocurrency data. The application of technical indicators, such facilitates the extraction of intricate patterns, momentum, volatility, and trends. The Performer neural network, employing Fast Attention Via positive Orthogonal Random features (FAVOR+), has demonstrated superior computational efficiency and scalability compared to the traditional Multi-head attention mechanism in Transformer models. Additionally, the integration of BiLSTM in the feedforward network enhances the model's capacity to capture temporal dynamics in the data, processing it in both forward and backward directions. This is particularly advantageous for time series data where past and future data points can influence the current state. The proposed method has been applied to the hourly and daily timeframes of the major cryptocurrencies and its performance has been benchmarked against other methods documented in the literature. The results underscore the potential of the proposed method to outperform existing models, marking a significant progression in the field of cryptocurrency price prediction.
Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.
Van Le Thi Thuy, Tran Thi Kim Oanh, Nguyen Thi Hong Ha
Using the GJR-GARCH method, this study examines the safe-haven role of gold, US dollar, and Bitcoin over a period including the global financial crisis, the COVID-19 pandemic and the Russia-Ukraine conflict from 3 April 2006 to 19 May 2023. The study supports the hypothesis that the safe-haven role of assets changes over periods of crisis. Specifically, gold loses its role as a safe-haven asset during the COVID-19 pandemic, but this role has been restored in the Dutch, US and German markets during the Russia-Ukraine conflict. Similarly, Bitcoin is not a safe-haven asset during the COVID-19 pandemic but is a strong safe-haven asset for the stock markets of some European countries, and a weak safe-haven asset for China when the Russia-Ukraine conflict occurred. Only the USD acts as a stable safe-haven asset through periods of crisis. However, this role is weakened in Russia. These results partly help investors and portfolio managers choose a safe haven for their assets, especially during volatile market periods.
Tomas PeÄiulis, Nisar Ahmad, Angeliki N. Menegaki, Aqsa Bibi
Abstract This systematic literature review examines cryptocurrency forecasting trends, influential sources, and research themes. Following PRISMA guidelines, 168 articles from Q1 or Aātier journals in the Scopus database were analyzed using bibliometric techniques. The findings reveal a significant increase in cryptocurrency forecasting research output since 2017, particularly in 2021. āFinance Research Lettersā emerges as the most productive journal, whereas āEconomics Lettersā receives the highest number of citations. Elie Bouri is identified as the most prolific author, and China is the top contributor country. Key research themes include bitcoin, cryptocurrency, volatility, forecasting, machine learning, investments, and blockchain. Future research directions involve utilizing internet searchābased measures, timeāvarying mixture models, economic policy uncertainty, expert predictions, machine learning algorithms, and analyzing cryptocurrency risk. This review contributes unique insights into the field's growth, influential sources, and collaborative structures and offers a foundation for advancing methodology and enhancing cryptocurrency forecasting models.
This paper analyzes the connectedness between gold, wheat, and crude oil futures, Bitcoin, carbon emission futures, and international stock markets in the G7, BRICS, and Gulf regions with the outbreak of exogenous and unexpected shocks related to health, banking, and political crises. To this end, we use a wavelet-based method on the returns of different assets during the period 2 January 2019, to 21 April 2023. The empirical findings show that the existence of time-varying linkages between markets is well documented and appears stronger during the COVID-19 pandemic. However, it seems to diminish for some associations with the advent of the Russia-Ukraine War. The empirical results also show that investor risk perceptions measured by the VIX are negatively and substantially linked to stock markets in different regions. Other interesting findings emerge from the connectedness analysis with the outbreak of Silicon Valley bankruptcy. In particular, Bitcoin tends to regain its role as a safe-haven asset against some G7 stock markets during the bank crisis. Such findings can provide valuable insights for investors and policymakers concerning the relationship between different markets during different crises.
Abstract This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the RussiaāUkraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.