Abstract Cryptocurrencies, particularly Bitcoin have attracted a lot of attention in the last decades of humanity. Analyzing cryptocurrencies algorithmic differences, chaotic behavior and self-similarity in cryptocurrency metrics might give significant insights for identifying risks and opportunities. Determining the degree of chaos in crypto metrics is critical for understanding complexity, improving prediction capabilities, and supporting decision-making. This study focuses on the analysis of chaos and self-similarity in Bitcoin dynamics for predictability perspective. Return, rate of return and volume quantities in different scales are analyzed with using rescaled range method to reveal the degree of self-similarity. Hurst parameter extracts a comprehensive summary providing information on how current values depend on previous ones to reveal any persistence in Bitcoin metrics. Daily rate of return and return give Hurst degree around 0.64 while they are in between 0.52–0.55 for minutely and hourly based prices. However, an increasing persistence is observed with the increasing time window. Although the largest Lyapunov exponents stay in the positive region for prices and returns of Bitcoin, they are approximately zero for inspected statistics. Periodic characteristics of Bitcoin are also investigated to reveal any dependencies on halving mechanism of Bitcoin. Detailed self-similarity analysis on specific periods shows that bull and bear market seasons don’t make any significant effect on the degree of Hurst parameter. Due to nonlinear and unpredictable characteristics of Bitcoin metrics, distribution fittings are applied to characterize BTC return and rate of return. While Wakeby distribution gives best fitting for daily return, Cauchy distribution gives best for hourly returns.
Osman Gülseven, Bashar Yaser Almansour, Jesús Cuauhtémoc Téllez Gaytán
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.
Abstract Decentralized finance has gained significance in recent years, as have concerns about the financial system’s stability. Exchange mechanisms, such as those utilized on cryptocurrency platforms, enhance volatility, and transmit risk contagion to other financial actors globally, which may increase financial calamity. We propose a Susceptible-Infected-Recovered model with a time delay to examine the mechanism of risk contagion in the cryptocurrency markets during the last decade. The governance token prices of the main cryptocurrency exchange platforms, as well as their spillover effects, crash risks and indicators of people’s attention, are assessed, and the obtained parameters are used in the Susceptible-Infected-Recovered model to replicate the dynamics of risk contagion in the examined crypto markets. Findings suggest high interconnection among crypto markets in short-run and the fear spread among people play an important contribution to financial risks. Under the new decentralized finance paradigm, predictive modeling of the temporal distribution of risk among cryptocurrencies may provide useful insights for policy and financial system stability, as well as for contagion risk.
Vincenzo Pacelli, Caterina Di Tommaso, Matteo Foglia, Stefania Ingannamorte
Abstract This research delves into the intricate relationship between cryptocurrencies and systemic risk within the framework of global financial markets. Utilizing a comprehensive dataset that amalgamates relevant indices from the cryptocurrency market along with global equity indexes from Europe, the United States, and China, the study employs a VAR for VaR model. This approach allows for the computation of spillover effects at different risk quantiles, offering insights into both downside and upside risk scenarios. The analysis underscores the notable spillover between cryptocurrency and traditional financial markets, revealing a complex interplay of risk factors that are not confined to geographical or asset-class boundaries. Our findings suggest that these interconnections could have far-reaching implications for global financial stability, regulatory policies, and risk management practices. By shedding light on these underexplored dimensions of financial markets, this study contributes to a deeper understanding of the systemic risks introduced by the growing prominence of cryptocurrencies.
Blockchain consensus mechanisms play a critical role in ensuring the security, decentralization, and integrity of distributed networks. As blockchain technology expands beyond cryptocurrencies into broader applications such as supply chain management and healthcare, the importance of efficient and scalable consensus algorithms has grown significantly. This study provides a comprehensive bibliometric analysis of blockchain and consensus mechanism research from 2014 to 2024, using tools such as VOSviewer and R’s Bibliometrix package. The analysis traces the evolution from foundational mechanisms like Proof of ork (PoW) to more advanced models such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT), with particular emphasis on Ethereum’s “The Merge” in 2022, which marked the historic shift from PoW to PoS. Key findings highlight emerging themes, including scalability, security, and the integration of blockchain with state-of-the-art technologies like artificial intelligence (AI), the Internet of Things (IoT), and energy trading. The study also identifies influential authors, institutions, and countries, emphasizing the collaborative and interdisciplinary nature of blockchain research. Through thematic analysis, this review uncovers the challenges and opportunities in decentralized systems, underscoring the need for continued innovation in consensus mechanisms to address efficiency, sustainability, scalability, and privacy concerns. These insights offer a valuable foundation for future research aimed at advancing blockchain technology across various industries.
David Alaminos, M. Belén Salas-Compás, Manuel Á. Fernández-Gámez
In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.
As technology has improved in the last decade, financial institutions have developed new technologies, including quantitative trading and cryptocurrency, to enhance their financial products and services. This paper first provides a brief background of quantitative trading and argues for the transactional efficiency of quantitative trading over traditional trading practices; it characterizes quantitative trading as fast and precise. Meanwhile, the study also accounts for the regulatory concerns–including data leakage and platform security–that quantitative trading firms may encounter. This study then establishes a distinction between cryptocurrency and quantitative trading–the former is money-driven, and the latter is data-driven. This paper then discusses the speculative nature of cryptocurrency and addresses its financial concerns citing the FTX collapse. Overall, this paper establishes the argument that quantitative trading supported by technological experts and facilitators offers more advantages than disadvantages compared to cryptocurrency trading. This research concludes that since quantitative trading and cryptocurrency trading are conducted without consideration for international boundaries, they offer bold financial potential as alternatives to traditional banking practices, as long as specific international financial laws are complied with.
The paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes the maximum likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavily tailed data (Bitcoin and Ethereum returns) and two peaked data (S\&P 500 and SPY ETF returns). For each index, the estimation results show that the six-parameter estimations are statistically significant except for the local parameter, $μ$. The goodness-of-fit was assessed through Kolmogorov-Smirnov, Anderson-Darling, and Pearson's chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p-value; and outperforms the Kobol, Carr-Geman-Madan-Yor, and Bilateral Gamma distributions.
Kostas Giannopoulos, Ramzi Nekhili, Christos Christodoulou-Volos
Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.
Oct 8, 2024·4th International Conference on AI-ML Systems (AIMLSystems 2024), October 08-11, 2024, Baton Rouge, LA, USA. ACM, New York, NY, USA, 8 pages
Francesco Puoti, Fabrizio Pittorino, Manuel Roveri
This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP. On one hand, to assess the complexity and the randomness of these time-series, a comparative analysis has been performed using Brownian and colored noises as a benchmark. The results obtained from the Complexity-Entropy causality plane and power density spectrum analysis reveal that cryptocurrency time-series exhibit characteristics closely resembling those of Brownian noise when analyzed in a univariate context. On the other hand, the application of a wide range of statistical, machine and deep learning models for time-series forecasting demonstrates the low predictability of cryptocurrencies. Notably, our analysis reveals that simpler models such as Naive models consistently outperform the more complex machine and deep learning ones in terms of forecasting accuracy across different forecast horizons and time windows. The combined study of complexity and forecasting accuracies highlights the difficulty of predicting the cryptocurrency market. These findings provide valuable insights into the inherent characteristics of the cryptocurrency data and highlight the need to reassess the challenges associated with predicting cryptocurrency's price movements.
Purpose- The primary purpose of this study is to model Bitcoin price volatility and forecast its future price returns using advanced econometric models such as ARCH and GARCH. The study aims to enhance risk management strategies and support informed investment decisions by addressing the time-varying nature of Bitcoin’s volatility. The research explores the persistence of volatility shocks and the clustering of price movements to provide insights into market dynamics. Methodology- This research examines daily Bitcoin closing prices over the period from January 2020 to October 2024. The data was preprocessed to ensure reliability, including applying logarithmic transformations to standardize the data and eliminate trends. Stationarity tests, such as the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests, were conducted to confirm the series' stationarity. The ARCH-LM test was utilized to detect volatility clustering which is essential for validating the use of ARCH and GARCH models. Following this, ARIMA models were employed to define mean equations and GARCH models were used to estimate conditional variance and capture volatility dynamics. The dataset was split into training and validation subsets with data from July to October 2024 reserved for validation. Findings- The findings demonstrate that Bitcoin’s price movements exhibit significant volatility clustering and persistence of shocks which are key characteristics effectively captured by ARCH and GARCH models. These models provide valuable insights into the volatility patterns of Bitcoin, supporting their application in cryptocurrency analysis. Despite their robustness, the models face limitations in precise return forecasting during highly volatile periods, suggesting the need for further refinement or integration with advanced approaches. Conclusion- The research concludes that ARCH and GARCH models are effective tools for understanding and forecasting Bitcoin’s volatility. The study underscores the importance of acknowledging volatility persistence and clustering effects when analyzing cryptocurrency price behavior. However, it also highlights areas for improvement in econometric modelling by including the exploration of hybrid models and the integration of macroeconomic factors to enhance forecasting accuracy. Keywords: Bitcoin, ARCH models, GARCH Models, forecasting, ARIMA models JEL Codes: C58, G10, G12
Monks, CA, USA, Shafeeq Ur Rahaman, P. Sudheer, Samsung, CA, USA · 5 authors
The rapidly evolving landscape of cryptocurrency markets presents unique challenges and opportunities. The significant daily variations in cryptocurrency exchange rates lead to substantial risks associated with investments in crypto assets. This study aims to forecast the prices of cryptocurrencies using advanced machine learning models. Among seven models that were tested for their prediction and validation efficiency, Neutral Networks performed the best with minimum error. Thus, Long Short-Term Memory (LSTM) neural networks were used for predicting future trends. LSTM model is well-suited for analyzing complex dependencies in financial data. Starting with historical data collection, data preprocessing, feature engineering, normalization and integrative binning, a comprehensive Exploratory Data Analysis (EDA) was conducted on 50 cryptocurrencies. Top performers were identified based on criteria such as trading volume, market capitalization, and price trends. The LSTM model was implemented using Python to predict 90-day price movements data to check intricate patterns and relationships. Model performance was validated by performance metrics such as MAE and RMSE. The findings align with the Adaptive Market Hypothesis (AMH) which suggests that cryptocurrency markets exhibit dynamic efficiency influenced by evolving market conditions and investor behavior. The study shows the potential of machine learning models in financial economics and their role in enhancing risk management strategies and investment decision-making processes.
Antonio Pellicani, Gianvito Pio, Michelangelo Ceci
Cryptocurrencies are virtual currencies that exploit cryptography to perform secure financial transactions. They gained widespread popularity in recent years due to their decentralized nature, (pseudo-)anonymity, and ability to facilitate cross-border transactions without the need for intermediaries. However, their price on the market exhibits a huge volatility, that makes them prone to market anomalies. Therefore, predicting anomalies in cryptocurrency time series can be considered an important task for financial institutions, traders, and investors, to maximize their profit or minimize losses. In this paper, we propose a novel approach for predicting anomalies in cryptocurrency time series by exploiting temporal correlations among different cryptocurrencies. Our approach, called CARROT, is based on the idea that groups of cryptocurrencies exhibit similar trends, possibly due to common influencing factors. CARROT analyzes the temporal correlation between different cryptocurrencies, and identifies clusters showing similar patterns that can be useful for gaining insights into future anomalies. Subsequently, CARROT exploits multiple (i.e., one for each cluster) multi-target LSTM models to predict anomalies. Our experiments, performed on a dataset of 17 cryptocurrencies, proved that CARROT outperforms single-target LSTM models of up to 20%, as well as other approaches based on neural networks, i.e., MLP and CNN, in terms of macro F1-score. Therefore, the proposed approach can be considered as a promising tool for predicting anomalies in cryptocurrency time series data and can potentially be used to improve risk management and trading strategies in the cryptocurrency market. • Analysis of cryptocurrency trends. • Clustering-based multi-target prediction of anomalies in time series. • Consistent improvements achieved over the single-target counterpart.
Jan Šíla, Evžen Kočenda, Ladislav Krištoufek, Jiří Kukačka
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.
Bashar Yaser Almansour, Osman Gülseven, Jesús Cuauhtémoc Téllez Gaytán
Our research re-examines the dynamics of major cryptocurrencies in response to recent economic and geopolitical events, focusing specifically on the Covid-19 pandemic period. Employing wavelet analysis and quantile regression methods, we examine cryptocurrency behavior before, during, and after the pandemic, using the Least Asymmetric Daubechies (LA8) wavelet function to decompose log-returns into frequency scales. Additionally, wavelet coherence and quantile-on-quantile regression techniques analyze daily price data from July 2017 to April 2023. Results uncover a robust long-term relationship among cryptocurrencies with diminishing medium-term correlations. Bitcoin shows synchronization with major cryptocurrencies, excluding Tether, and BTC-ETH and BTC-LTC pairs exhibit interconnected dynamics alongside fundamental links. Empirical evidence highlights Bitcoin"s heterogeneous relationship, being more responsive to positive extremes than negatives. Our study"s timeframe limitation (July 2017 to April 2023) restricts insights into longer-term trends, and methodological choices like wavelet analysis may not fully capture cryptocurrency dynamics, suggesting potential for alternative interpretations. Nonetheless, findings suggest significant implications for investment strategies, emphasizing temporal dynamics within cryptocurrency markets.
Haider Ali, Muhammad Aftab, Faheem Aslam, Paulo Ferreira
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.
Abstract Within the adaptive market hypothesis (AMH) framework, this study explores the dynamic impact of cryptocurrency heists on Bitcoin's market efficiency. By analysing Bitcoin's one‐minute price data, we calculate permutation entropy to assess market disorder and employ the complexity‐entropy causality plane to quantify structural changes in the market. The analysis focuses on the market efficiency changes the day before, the day of, and the day after a heist, revealing that heists significantly disrupt market efficiency. Specifically, on the day of and following a heist, we observe a marked decrease in permutation entropy alongside a significant increase in complexity, indicating a notable decline in market efficiency. Further analysis shows that when a heist targets a specific token, this token draws investor attention, causing a less severe drop in Bitcoin's market efficiency, while the affected token's market efficiency drops more dramatically. These findings suggest that different token markets react differently to heists, and investors should consider adjusting their strategies to respond to these changes. For policymakers, the results highlight the critical need to enhance market stability and security through informed policy measures to mitigate the impact of such disruptive events.
Research background: The research employs the Cross-Sectional Absolute Deviation of returns (CSAD) model, augmented with modifications by Chiang and Zheng (2010) to address asymmetric investor behavior, facilitating the detection of herding behavior. Additionally, the study leverages Quantile Regression (QR), demonstrated by Barnes and Hughes (2002) to effectively capture extreme values in financial data with fat tails or skewed distributions. This approach is particularly relevant in the context of the volatile cryptocurrency market, allowing for the analysis of outliers and the assessment of the magnitude of return impacts using T-stat and Quantile Process Estimates. Purpose of the article: This study primarily centers its empirical analysis on identifying market-wide herding behavior (Henker et al., 2006) within the cryptocurrency market, spanning from January 1, 2016, to February 1, 2019, juxtaposed with the period from January 1, 2019, to January 7, 2022. The selected time frames were chosen to evaluate potential shifts in herding dynamics within this market, particularly during its phases of rapid expansion and subsequent stagnation. Methods: The Cross-Sectional Absolute Deviation (CSAD) methodology, as proposed by Chiang and Zheng (2010), was employed for herding detection, alongside the incorporation of dummy variables to discern the market conditions under which herding occurs. Herding behavior manifests when dispersion diminishes, or its increase is less than proportionate to market returns, indicating an inverse correlation between market returns and dispersion in the presence of herding. Additionally, CSAD estimation was conducted utilizing quantile regression to encompass a broader range of quantiles, facilitating the identification of herding tendencies across various return magnitudes. To delve further into investor behavior, Bitcoin was utilized as an illustrative example, elucidating investor reactions to market bubbles through the application of the Hodrick-Prescott (HP) Filter. Findings & value added: The findings reveal instances of herding behavior during downward market movements and at higher return levels preceding 2019. However, post-2019, herding is observed during upward market movements and at medium to higher return levels. This study presents compelling evidence of herding phenomena coinciding with the bursting of bubbles, particularly concerning Bitcoin. The findings provide a deeper understanding of how herding manifests differently across distinct market conditions and timeframes, offering actionable insights for investors and policymakers navigating the volatile cryptocurrency landscape. Additionally, by highlighting the correlation between herding behavior and market bubbles, particularly in the context of Bitcoin, this study contributes to the broader discourse on cryptocurrency market dynamics.
Purpose The purpose of the study is to examine the impact of uncertainty and return of classical financial assets on herding behaviour in the cryptocurrency market. Also, herding in this market and the impact of the COVID-19 pandemic have been investigated. Design/methodology/approach The study uses quantile regression to estimate the models. Daily data from ten major cryptocurrencies, the CCI30 index and three volatility indices (VIX, EVZ and GVZ), spot gold price, the MSCI and the US dollar indices from January 2018 to December 2023 have been used. Findings The findings show evidence of anti-herding during periods of simultaneous high volatility in stock and currency markets, as well as in the gold and currency markets. However, the results support herding in the whole sample period, which reduces when including the COVID-19 pandemic effect. In addition, the study does not support the relationship between returns of traditional financial assets and herding in the cryptocurrency market. Practical implications The result of the study can be useful for investors, particularly the managers of the novel class of ETFs, to make their investment decisions more consciously, regarding uncertainty in other financial markets. Also, the findings provide some insight to regulators regarding the herding behaviour in the cryptocurrency market and its influences on the financial system’s stability. Originality/value To the best of the authors’ knowledge, for the first time, this study examines the impact of concurrent high uncertainty conditions in classical financial markets on herding behaviour in the cryptocurrency market.