Purpose This study investigates the time-varying volatility spillover connectedness among seven major cryptocurrencies before and during the COVID-19 pandemic. It aims to understand contagion risk and its implications for diversification and financial stability, especially during periods of extreme price volatility. Design/methodology/approach Using the frequency-domain spillover index, the study analyzes the interconnectedness of cryptocurrency markets with daily data from 10 August 2015 to 10 December 2021. This method allows for examining volatility spillovers across different time frequencies. Findings The study finds that cryptocurrencies are highly interconnected at higher frequencies, indicating significant contagion risk and limited short-term diversification opportunities. The spillover effects are frequency-dependent, varying across different time horizons. Practical implications The findings suggest the need for targeted regulatory policies focused on short-term cryptocurrency behavior to maintain financial stability. Investors should exercise caution when using cryptocurrencies for portfolio diversification, given the high interconnectedness and contagion risk. Originality/value This study uniquely contributes to the literature by applying a frequency-domain approach to analyze volatility spillovers across multiple cryptocurrencies, particularly in the context of the COVID-19 pandemic. It provides novel insights into the frequency-dependent nature of spillover effects, offering a deeper understanding of the contagion risk in cryptocurrency markets.
Hanen Ben Ameur, Fouad Jamaani, Mohammed N. Abu-Alfoul
This paper examines gold and cryptocurrencies' hedge and safe-haven capabilities against various downturns, including the COVID-19 pandemic and Geopolitical Risks (GPR), across different market conditions. The study covers a sample period from 2013 to 2021 at a daily frequency, employing the GARCH model and quantile regression with binary variables. The empirical results indicate that neither gold nor cryptocurrencies can act as strong hedges against infectious disease pandemics. However, gold, Bitcoin, and Ethereum exhibit weak safe-haven abilities during geopolitical risks. Using regression quantiles, the study finds that gold demonstrates a strong safe-haven against low and high Infectious Disease Epidemic Market Volatility (IDEMV) during extremely bearish and bullish markets. In contrast, Bitcoin and Ethereum act as strong safe havens only against low IDEMV during extreme bearish markets. Gold also shows a strong hedge propriety against extreme geopolitical events, while cryptocurrencies provide a weak hedge. Overall, gold exhibits strong safe-haven properties against low and high Geopolitical tensions, while cryptocurrencies' hedging and safe-haven abilities vary across markets. These findings convey insights for investors and guidance to supervisors on the evolution of gold, Bitcoin, and Ethereum as safe-haven and hedge instruments during both bearish and bullish markets.
This paper examines the price impact of large block trades in cryptocurrency markets by using a natural experiment in Bitcoin provided by the Gemini exchange. The exchange introduced a block trading facility in 2018, but in December 2019, it changed the minimum size threshold that allows market participants to trade a block and report it with a delay. Consistent with theoretical predictions and earlier empirical findings, we largely confirm that the information content of large trades is significantly lower in the upstairs market than in the downstairs. In contrast with prior research in traditional markets, we find that delaying the reporting of a block traded away from the continuous book discourages informed trading and potentially decreases the informativeness of trading and, therefore, information efficiency. Further, we find that the newly implemented size requirement for upstairs trades increases the total market impact, thereby not working as the intended introduction of a block trading facility.
Understanding the interconnectedness of cryptocurrencies based on their underlying technology is crucial for effective portfolio management and risk assessment. To establish the tail dependence structure and risk spillover between cryptocurrencies, this paper used the daily closing prices of the top eight proof-of-stake-based cryptocurrencies and the top ten proof-of-work-based cryptocurrencies from September 22, 2020 to April 7, 2023. This study applied the C-vine copulas and CoVaR measures. The outcome of the copula findings for the proof-of-stake cryptocurrencies illustrates that Ethereum exhibits strong resilience during market downturns, acting as a buffer for other proof-of-stake cryptocurrencies with pairwise tail dependence coefficients ranging from 0.45 to 0.67. Bitcoin Cash emerges as a portfolio diversifier within the proof-of-work ecosystem, absorbing 45% to 75% of volatility spillovers. However, from the proof-of-stake CoVaR analysis, ETH, DOT, and MATIC rank highest in systematic importance before April 2022, signifying their significant risk transmission role, and for the proof-of-work CoVaR analysis, Bitcoin (BTC) is the primary risk transmitter in the cryptocurrency portfolio, having a positive CoVaR of 0.15. Ethereum and Bitcoin are identified as the dominant risk transmitters within their respective groups, highlighting their potential to amplify systemic risk. This study provides valuable insights for investors and policymakers navigating the increasingly complex cryptocurrency landscape.
The objective of this study is to analyse the correlation between Bitcoin and altcoins in the post-covid world and take advantage of this possible relationship to design investment strategies on Bitcoin based on the evolution of altcoins using Artificial Intelligence (AI) models. The sample of daily observations covers from January 2020 to February 2023, and the regressions performed between altcoins and Bitcoin are positive and 99 % significant, except for Dogecoin, which has a correlation with Bitcoin. If we add a lag, the estimated parameters are still 95 % significant, except for Dogecoin, so we can assume that the return of altcoins anticipates the evolution of Bitcoin. We train an artificial intelligence model in which the predictors are the observed daily return in altcoins and the target to predict is next day trend of Bitcoin (up or down). We use decision tree algorithms (J48), random forest and naive bayes, but in a retrospective cross-sectional validation with 10 sample partitions we obtain a poor predictive capacity of only a 51 % success rate in the best of cases. Therefore, despite the evident correlation between predictors and the objective variable, we should not implement this investment strategy.
This paper studies the Bitcoin volatility forecasting performance between popular traditional econometric models and machine learning techniques. We compare the 1-day to 2-month ahead forecasting performance of the Long Short-Term Memory (LSTM) and a hybrid Convolutional Neural Network-LSTM (CNN-LSTM) model to the traditional models. We find that neural networks outperform Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models for all forecasting horizons. Furthermore, the LSTM model outperforms the Heterogeneous Autoregressive (HAR) model and by integrating the Markov Transition Field (MTF) into the CNN-LSTM model, we achieve superior forecasting results in the short-term, particularly for the 7-day forecasts. • We forecast Bitcoin volatility using intraday data with machine learning models. • High-frequency Bitcoin data benefits Bitcoin volatility predictions. • We convert time series to images to improve Bitcoin volatility prediction. • Our approach outperforms HAR and GARCH, especially in short-term forecasts. • Image transformation can capture non-linear features such as clustering effect.
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.
Krzysztof Gogol, Manvir Schneider, Tessone, Claudio, Livshits, Benjamin
Layer-2 (L2) blockchains inherit Ethereums security guarantees while reducing gas fees. As a result, they are gaining traction among traders at Automated Market Makers (AMMs), sparking debate over whether they contribute to liquidity fragmentation of Ethereum. Our research suggests that such fragmentation is not currently occurring. However, it could emerge in the future, particularly if Liquidity Providers (LPs) recognize the higher returns available on L2s. Using Lagrangian optimization, we develop a model for optimal liquidity allocation across AMMs on Ethereum and its L2s, using staking as a benchmark. We show that, in equilibrium, AMM liquidity provision returns converge to this reference rate. Additionally, we measure the elasticity of trading volume with respect to Total Value Locked (TVL) in AMMs and find that, on well-established blockchains, an increase in TVL does not necessarily lead to higher trading volume. Finally, our empirical findings reveal that Ethereums liquidity pools are oversubscribed compared to those on L2s and often yield lower returns than staking Ether. LPs could maximize their rewards by reallocating more than two-thirds of their liquidity to L2s and staking.
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.
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
Carlos Esparcia, Tarek Fakhfakh, Francisco Jareño, Achraf Ghorbel
Abstract This study examines the link between stocks and decentralized finance (DeFi) in terms of returns and volatility. Major G7 exchange-traded funds (ETFs) and various highly traded DeFi assets are considered to ensure the robustness of the empirical experiment. Specifically, this study applies the vector autoregression generalized autoregressive conditional heteroskedasticity (VAR-GARCH) model to examine the information transmission of these two markets on a two-way basis and the dynamic conditional correlation (DCC)-GARCH model to assess the bivariate correlation structure between each DeFi and ETF pair. The volatility spillover analysis proves a contagion effect occurred between different geographic markets, and even between markets of different natures and typologies, during the most turbulent moments of the COVID-19 crisis and the war in the Ukraine. Our results also reveal a weak positive correlation between most DeFi and ETF pairs and positive hedge ratios that approach unity during turbulent times. In addition, DeFi assets, except for the Bazaar (BZR) Protocol, can offer diversification gains when included in financial investment portfolios. These results are particularly relevant for portfolio managers and policy-makers when designing investment strategies, especially during periods of financial crisis.
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.
January 2024 which covered recent catastrophic events such as Bitcoin Price Crash-2018, COVID-19, Global plummet in Oil Demand-2020, and Russia-Ukraine War. The findings reveal that NFTs and DeFi assets possess weak connectedness with housing market in normal market state, however, connectedness become robust in extreme bearish and bullish market states. Moreover, NFTs and DeFi assets are net transmitters and housing market acts as net receiver of shock in all market states. Investors, portfolio managers, and policymakers should carefully analyze both digital financial assets and housing market especially during extreme events to build well diversified profit-gaining portfolios and to formulate policies.
Richard Umeokwobi, Edmund Tamuke, Obumneke Ezie, Marvelous Aigbedion · 5 authors
The rapid adoption and growing prominence of Bitcoin and other cryptocurrencies have sparked significant interest and debate among economists, policymakers, and financial analysts. In Sub-Saharan Africa, where traditional financial systems often face challenges such as limited access to banking services, high transaction costs, and volatile currencies, Bitcoin presents both opportunities and risks. Understanding the interplay between Bitcoin and key monetary indicators such as monetary aggregates, exchange rates, and interest rates can provide valuable insights for policymakers and stakeholders in these economies. This study therefore seeks to investigate the nexus between monetary indicators and Bitcoin in selected Sub-Saharan African countries using a Panel ARDL (Autoregressive Distributed Lag) approach. The analysis focuses on understanding the dynamic relationship between key monetary variables, such as monetary aggregates, exchange rates, interest rates, and Bitcoin prices, from 2010 quarter three to 2022 quarter four. The findings reveal several significant relationships between monetary indicators and Bitcoin across the selected Sub-Saharan African countries. In the short run of the Panel Ardl monetary aggregates exhibit a positive relationship with Bitcoin prices, indicating that changes in the money supply may influence the demand for cryptocurrencies. Conversely, both exchange rates and interest rates show a negative relationship with Bitcoin prices in the short run, suggesting that currency depreciation and higher borrowing costs may reduce demand for Bitcoin. In the long run, the relationship between monetary aggregates and Bitcoin remains positive, emphasizing the potential influence of money supply on cryptocurrency markets over time. However, the significance of exchange rates diminishes, indicating a less pronounced impact in the longer term. Interestingly, interest rates continue to exhibit a significant negative relationship with Bitcoin prices in the long run, highlighting the persistent effect of borrowing costs on cryptocurrency demand. These results have important implications for policymakers, investors, and researchers interested in the intersection of monetary policy and cryptocurrency markets in Sub-Saharan Africa. Policymakers may consider the impact of monetary policy decisions on cryptocurrency adoption and market dynamics, while investors can use these insights to inform their investment strategies.
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.
Christian Kreuzer, Ralf Laschinger, Christopher Priberny, Sven Benninghoff
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. • We examine how the Russia–Ukraine conflict affects the trading volume of 16 major cryptocurrencies. • Most cryptocurrencies see temporary increased trading volumes on events of the conflict. • Payment tokens and many utility coins, in particular, experience higher trading volumes. • Stablecoins only have lower trading volumes before the event.
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.