We investigate the long-range cross-correlation and cross-multifractality between the âdirtyâ and âcleanâ cryptocurrencies and the major financial assets: the Dow Jones Index (DJI), the EuroâDollar exchange rate (EURUSD), and Gold. The analysis shows a high long-range correlation between most pairs with some exceptions, including the DJIâRipple and GoldâPolygon. When the DJI is paired with clean cryptocurrencies such as Polygon and Cardano, they exhibit multifractal properties. As for the EURUSDâBTC and GoldâBTC, these two pairs demonstrated the highest level of multifractality in their corresponding pairs. All pairs of cryptocurrencies and main financial indices are persistent, with the exceptions of EURUSDâPOLYGON (H = 0 . 4970 Âą 0 . 0048 for q =2), GOLDâBTC (H = 0 . 5039 Âą 0 . 0058 for q =2) and GOLDâLTC (H = 0 . 5044 Âą 0 . 0057 for q =2) that are Brownian, and GOLDâPOLYGON (H = 0 . 4917 Âą 0 . 0055 for q =2) which is anti-persistent. For q =5, all are anti-persistent, except DJI-Eth, XRP, and ADA are Brownian, and EURUSD-XRP is persistent. We also assessed the asymmetric persistence behavior when the market is upward or downward and found that for the pairs involving dirty cryptocurrencies with DJI and EURUSD, there is a higher level of persistence during the downward market. On the other hand, Gold-related pairs were almost symmetric. Thus, we identified the complexity and variability of the cryptocurrency pairs with the traditional financial instruments, which shows their various reactions to the changes in the market and types of assets.
Ever since the emergence of cryptocurrencies, scholars have grappled with the question of whether they are forms of money or not. The most interesting problem, however, is not if these instruments are already money, but whether they could become money. One crucial aspect in this regard is the potential (or lack thereof) of a privately-issued cryptocurrency to become the monetary unit of account. Drawing on Marxâs theory of money and making the hypothesis that cryptocurrencies are digital commodities, the article argues that cryptocurrencies create a unit of account (BTC) to describe a novel monetary instrument (a Bitcoin coin) aspiring to become a new form of world money. So far, they have not been widely used to denominate prices, incomes, or credits/debts except in certain, still limited but growing, areas of the on-chain digital world. Things could change if the use of cryptocurrencies spills over to the off-chain (digital and non-digital) world. Nevertheless, the adoption of cryptocurrencies as units of account would face several challenges in international and national circulation, crucially among them, the action of states to remain in control of the monetary unit.
Eleni Koutrouli, Polychronis Manousopoulos, John Theal, Laura Tresso
As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum.
This study investigates return spillovers among the 15 most capitalized cryptocurrencies during the Russia-Ukraine war and the COVID-19 pandemic. Data were extracted from the Coin Market Cap database to ensure a comprehensive analysis of market behavior, covering a daily series from January 2020 to December 2023. The research employs three autoregressive techniques (TVP-VAR, LASSO VAR, and Ridge VAR) to verify the robustness of findings regarding market fragility influenced by non-economic shocks. The study identifies extensive return spillovers primarily driven by Bitcoin and Ethereum, with considerable influences from Cardano, Litecoin, and Polkadot. The results show Ethereum as a primary spillover transmitter in the cryptocurrency market, taking that position formerly held by Bitcoin. Despite the speculative nature of cryptocurrencies, there is potential for diversification through two stablecoins, Tether and USD Coin, which exhibit limited spillover effects from other cryptocurrencies and negative correlations with one another. As a stablecoin, DAI served as a potential diversifier during the COVID-19 pandemic but not during the Ukraine war. The study offers practical insights for investors on managing crypto portfolios during geopolitical and global health crises and the strategic use of stablecoins. Societally, the study examines the need for enhanced regulatory frameworks to reduce systemic risks in the highly interconnected cryptocurrency market. JEL Classification: G01, G11.
Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to measure the dynamic volatility patterns of Bitcoin, revealing the assetâs substantial fluctuations over time and its sensitivity to market shocks. Additionally, we utilize Monte Carlo simulations to forecast potential future price movements of Bitcoin, highlighting risk scenarios and the probability distribution of price trajectories over a one-year period. The Value-at-Risk (VaR) model is implemented to estimate potential losses within a given confidence interval, providing a robust measure of downside risk. Furthermore, the study examines the integration of cryptocurrency markets with traditional financial instruments by analyzing cross-asset correlations and volatility spillover effects. The findings suggest that while Bitcoin remains a highly volatile asset, its correlation with the broader financial system is increasing, indicating a potential shift towards mainstream financial adoption. The results contribute to the ongoing debate on whether cryptocurrencies serve primarily as speculative instruments or as viable components of diversified investment portfolios. These insights are valuable for institutional investors, risk managers, and policymakers in designing more effective risk mitigation strategies for cryptocurrency investments.
Klaus Grobys, James W. Kolari, Davide Sandretto, Syed Jawad Hussain Shahzad ¡ 5 authors
Abstract This paper explores the tail behavior of cryptocurrency momentum strategies and the profitability of volatility-managed momentum portfolios. Our main results derived from using a sample of large-cap cryptocurrencies and equal-weighted momentum portfolios indicate that cryptocurrency momentum is subject to severe crashes. Even a single cryptocurrency can cause insignificant momentum portfolio returns. In line with the literature on volatility-managing equity portfolios, our findings suggest that volatility management is a useful tool for mitigating cryptocurrency momentum crashes. Further corroborative evidence suggests that cryptocurrency momentum appears to be a phenomenon associated with large-cap cryptocurrencies.
Since its creation in 2008, Bitcoin has often been compared to precious metals due to their shared characteristics as safe havens, hedges, and risk diversification tools. This study uses the DCC-GARCH model to analyze dynamic conditional correlations and volatility spillovers between Bitcoin and the returns of gold, copper, silver, and platinum. The findings reveal persistent volatility and clustering in the returns of both Bitcoin and these metals. There is a one-way volatility spillover from gold to Bitcoin, and from Bitcoin to copper, silver, and platinum. Significant dynamic conditional correlations are observed between Bitcoin and both gold and copper, while no significant correlations are found with silver and platinum. These results provide valuable insights for portfolio diversification strategies and inform policymaker decisions in financial markets.
Erveton P. Pinto, Marcelo A. Pires, Rone N. da Silva, SÄąĚlvio M. Duarte QueirĂłs
We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa \,\, 90 \%$ of the total crypto market capitalization. With that, we focus on the basic elements of price motion that compare with the random walk backbone features associated with mathematical properties of the Efficient Market Hypothesis. From the location of each crypto on the Binary Complexity-Plane (BiCEP) we define an inefficiency score, $\mathcal I$, and rank them accordingly. The results based on the BiCEP analysis, which we substantiate with statistical testing, indicate that only Shiba Inu (SHIB) is significantly inefficient, whereas the largest stake of crypto trading is reckoned to operate in close-to-efficient conditions. Generically, our $\mathcal I$-based ranking hints the design and consensus architecture of a crypto is at least as relevant to efficiency as the features that are usually taken into account in the appraisal of the efficiency of financial instruments, namely canonical fiat money. Lastly, this set of results supports the validity of the binary complexity analysis.
Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causalânoncausal invertibleânoninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMAâGARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewnessâcommon in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (Îą) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
Abstract During the last years, financial market contagion has become a critical concern for policymakers and investors, particularly with respect to the financial stability of cryptocurrency platforms. This paper explores the contagion effect among crypto exchanges employing the SusceptibleâInfectedâRecovered (SIR) model with time delay and investigates possible cooperative strategies. The SIR dynamical system is integrated with the replicator equation of evolutionary game theory to study the interplay between the spread of risk and the propensity of cryptocurrency platforms to become cooperative under the pressure of financial contagion. Different equilibrium points which correspond to both pure and mixed cooperative strategies characterize the resulting model. We carry out a theoretical analysis of the problem by studying the asymptotic behavior in the steady state. In addition, using extensive cryptocurrency market data from 2017 to 2023, we identify the key factors driving contagion and assess the dynamics of cooperative versus non-cooperative behavior. Our findings point out that cooperative strategies are essential to ensure financial stability, particularly in the long term, as they mitigate systemic risks and foster resilience. These results provide critical insights for policy makers and investors, offering actionable strategies to enhance the robustness of crypto markets and address the growing challenges of financial contagion in the digital asset ecosystem.
Inzamam Ul Haq, Muhammad Abubakr Naeem, Chunhui Huo, Walid Bakry
This study examines the interlinkages among diverse cryptocurrency classes and their multiscale relationship with media climate change concerns to examine how cryptocurrency returns respond to rising climate change concerns. The analysis includes 11 cryptocurrencies classified as dirty, gold-backed, energy, and sustainable and their behavior regarding media climate change concerns, including transition and physical risks. Using squared wavelet coherence and partial wavelet coherence (PWC) on daily data from January 1, 2014 to June 29, 2024, this study shows time-frequency-dependent market integration among cryptocurrency pairs. During rising climate change concerns, returns decrease for some cryptocurrencies while increasing for XRP, implying higher investors' trust in sustainable cryptocurrencies. PWC analysis reveals significant influence of climate change concerns on pairwise returns connectedness among various cryptocurrency classes. This study highlights the need for cryptocurrency traders to incorporate media climate change information into their investment decisions, contributing insights into using diverse crypto-assets for risk management. ⢠We find high market integration after 2018 cryptocurrency crash. ⢠PLG and gold-backed cryptos show weak dependence with respective cryptocurrencies. ⢠Rising climate change concerns significantly increase PLG and XRP returns across time-frequency. ⢠We find that transition risks predict cryptocurrency returns more than physical risks. ⢠We find that climate change concerns drive cryptocurrency co-movements.
Adi Wolfson, Gerard Khaladjan, Yotam Lurie, Shlomo Mark
Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effectiveâand not just more efficientâsolutions, based on value co-creation between several providers and a customer.
We examine if the day-of-the-week effect is present in Bitcoin return series. The model specification in use accounts for conditional heteroscedasticity, which is captured in the form of a stochastic volatility process that allows for periodic time-varying parameters. We find periodicity in Bitcoin returns, which is evidence against the market efficiency of Bitcoin.
Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.
This study investigates the return propagation dynamics between cryptocurrencies and Emerging market sectoral indices (EMSI), focusing on portfolio impact from Bitcoin, Ethereum, and two gold-backed cryptocurrencies (PAXG and X8X). Using data from 2019 to 2024, we apply a novel DCC-GARCH-based R 2 decomposed connectedness approach to analyse return connectedness among these high-risk assets. We also utilize innovative concepts such as minimum dynamic pairwise connectedness and minimum R 2 decomposed connectedness portfolios in our multivariate hedging portfolios. Our findings reveal that total connectedness is time-variant and influenced by economic events. Bitcoin and Ethereum are identified as net transmitters of shocks, while other assets, particularly gold-backed cryptocurrencies, serve as net shock receivers with minimal impact. Moreover, few EMSIs (financials, industrials, and materials sectors) show significant connectedness in the system. Although our suggested portfolio analysis offers improved returns, none consistently outperform the market. This research offers valuable insights for investors and policymakers regarding the interconnectedness and risk management of cryptocurrencies and EMSI.
Ibrahim Garba Kabo, Georgina N. Obunadike, Nuruddeen A. Samaila
Bitcoin, the leading cryptocurrency, has gained significant attention due to its high volatility and potential economic impact. Traditional financial forecasting models struggle to accurately predict Bitcoin prices due to its sensitivity to various factors, including market sentiment and macroeconomic conditions. Existing models primarily rely on historical price data, often neglecting external influences such as public sentiment and economic indicators like Gross Domestic Product (GDP). To address these limitations, this study explores a hybrid approach that integrates Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models with sentiment analysis and GDP data to enhance Bitcoin price prediction accuracy. The study evaluates the predictive capabilities of these models under different scenarios. When trained on Bitcoin price data combined with sentiment analysis and GDP data, the ARIMA model achieved a Mean Absolute Error (MAE) of 2081.66, Root Mean Square Error (RMSE) of 2518.35, and an R-squared value of 0.9143. In comparison, when trained on Bitcoin data alone, it exhibited lower accuracy. The LSTM model demonstrated superior performance, achieving an MAE of 1253.24, RMSE of 1717.65, and an R-squared value of 0.9602 when incorporating sentiment and GDP data, significantly outperforming its standalone counterpart. The results highlight the effectiveness of integrating sentiment analysis and GDP data in cryptocurrency price prediction, demonstrating that hybrid models provide greater forecasting accuracy than traditional approaches. This study offers a robust framework for financial time series forecasting, aiding investors, analysts, and policymakers in making more informed decisions in the cryptocurrency market.
Albi Isufaj, Caio De Castro Martins, Marc Cavazza, Helmut Prendinger
This paper explores the applicability of Convergent Cross Mapping (CCM) and its extension, Time Delay Convergent Cross Mapping (TDCCM), to assess the causal relationships between Bitcoin, the S&P 500 index, and gold. Unlike conventional causality analysis methods, such as Granger causality or transfer entropy, CCM accounts for non-separable, weakly connected dynamic systems, and TDCCM explicitly incorporates time lags during cross-mapping, enabling the detection of complex causal relationships in systems with shared nonlinear behavior. This makes it particularly suitable for financial time series that often exhibit chaotic and nonlinear dynamics, particularly during periods of market instability. We integrate TDCCM with simplex projection and sequential locally weighted global linear map (S-map) algorithms, applying a sliding window approach to identify short time intervals characterized by high levels of nonlinearity and chaoticity. Using this approach, we uncovered a strong causal relationship between Bitcoin and the S&P 500 index during the onset of the COVID-19 pandemic. Our analysis reveals a bidirectional causal relationship between Bitcoin and the S&P 500 index, highlighting their interconnectedness during periods of heightened economic uncertainty. Furthermore, we find a unidirectional causal influence of Bitcoin on gold, reflecting Bitcoinâs evolving role as a macroeconomic indicator and its growing relevance as an alternative store of value. These findings provide insight into the dynamics between cryptocurrencies and traditional financial markets, particularly during periods of global economic disruption. ⢠We use Time Delay Convergent Cross Mapping (TD-CCM) to identify and quantify lagged causal interactions between financial time series (Bitcoin, Gold, and the S&P 500 index), which is a more recent and less explored method for Time Series causality. ⢠We report a comprehensive and replicable methodology to apply TD-CCM to non-linear TS, based on combining the S-Map and Prediction Decay algorithm with a sliding window technique, while validating with surrogate analysis. ⢠We show evidence of strong causal influence from Bitcoin to Gold and bidirectional causality between Bitcoin and the S&P 500 Index during significant economic events like the COVID-19 pandemic.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Despite the introduction of several adjustments, mitigating data anomalies in financial datasets has proven challenging, particularly in the context of cryptocurrencies with extreme values and increased volatility. The progress in properly addressing these anomalies prior to testing remains restricted, highlighting the unique and complex nature of financial data in this domain. Thus, in this paper we propose a hybrid approach called the Win-IS strategy. It is meant to address the influence of extreme outliers in the tail and subsequently identify breaks, trend breaks and outliers in cryptocurrencies. This methodology uses the winsorization (Win) process to enhance the effectiveness of the indicator saturation (IS) approach. The study uses cryptocurrencies like Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Tether (USDT), and Ripple (XRP). The results of the research indicate that the winsorization strategy improved the detectability of the IS approach, with Win-IS outperforming the IS method in terms of the Bayesian Information Criterion. Furthermore, the Win-IS technique uncovered additional breaks, trend breaks and outliers that were previously unknown and repeated in some cases as detected by the IS strategy. The effect of winsorization is dependent on the chosen percentile and dataset attributes. Through detailed examination and comparison, the findings of this research contribute to the improvement of other detection approaches, providing a valuable perspective for researchers and practitioners in the field. Additionally, this hybrid approach can improve decision-making, risk management and model creation, benefiting investors, legislators and scholars.
The purpose of the paper is to present the results of the research on the potential inclusion of different types of crypto assets, such as Bitcoin, NFTs (Non-Fungible Tokens), and DeFi (Decentralised Finance), within optimal portfolios to help reduce variance or increase returns compared to equity investments. The analysis includes comparisons of different crypto assets and countries, specifically the Czech Republic, Hungary, and Poland. The author constructs optimal equity-crypto portfolios in the Markowitz environment for the period from 16 February 2021 to 8 January 2024, which was adjusted to NFT data availability from this date. Calculations are conducted under two scenarios: minimizing portfolio variance and maximizing returns. The research demonstrates that Bitcoin, NFTs and DeFi can be part of a well-diversified equity portfolio, primarily due to their low correlation with equity markets in the Czech Republic, Hungary and Poland. The paper is important for investors seeking diversification possibilities. Although diversification has been increasingly difficult recently due to increasing correlation coefficients between assets, new asset classes, such as crypto assets, have been created, offering new potential for portfolio creation. The conclusions drawn may also be vital for policymakers who should consider them when formulating regulations concerning systematic risk. The paper contributes value in four aspects. 1) The paper demonstrates that including NFTs, DeFi and Bitcoin in a stock portfolio creates diversification benefits for most portfolios. This is partially due to their slightly higher returns but mostly because of the lower risk that results from the low correlation of crypto assets with traditional markets. 2) Optimal shares of crypto assets differ depending on the equity and the crypto involved. 3) The paper considers Czech, Hungarian, and Polish markets while existing papers concentrate mostly on the American market. 4) The paper shows that there are minimal connections between the Czech, Hungarian, and Polish equity markets and crypto assets.
We analyze a model of heterogeneous rational bubbles that compete and complement each other. When some bubbles burst, surviving ones gain value, offsetting losses from collapsed bubbles. This âcompensation effect,â combined with diversification, enhances welfare. A portfolio of fragile bubbles may rival a single, stable bubble. The stationary equilibrium imposes a tight upper bound on bubble size, considering covariance structures, price fluctuations, and the emergence of new bubbles. These results have important policy implications, particularly for managing crypto ETFs and issuing CBDCs, highlighting the potential benefits of a diversified approach to fragile financial systems. ⢠We study a model of heterogeneous rational bubbles that compete and complement each other. ⢠A bubbleâs market size is driven by agentsâ confidence, with greater confidence leading to larger bubbles. ⢠When some bubbles burst, survivors appreciate in value, offsetting losses and mitigating welfare impacts. ⢠A diversified portfolio of fragile bubbles such as a crypto ETF may rival a single, stable bubble thanks to this âcompensation effectâ.
Yuming Huang, Jing Tang, Qianhao Cong, T. B. Richard ¡ 6 authors
In blockchains using the Proof-of-Work (PoW) consensus mechanism, a mining pool is a joint group of miners who combine their computational resources and share the generated revenue. Similarly, when the Proof-of-Stake (PoS) consensus mechanism is adopted, the staking pool imitates the design of the mining pool by aggregating the stakes. However, in PoW blockchains, the pooling approach has been criticized to be vulnerable to the block withholding (BWH) attack. BWH attackers may steal the dividends from victims by pretending to work but making invalid contributions to the victim pools. It is well known that BWH attackers against PoW face the miner's dilemma . To our knowledge, despite the popularity of PoS, we are the first to study the pool BWH attack against PoS. Interestingly, we find that, for a network only consisting of one attacker pool and one victim pool, the attacker will eventually manipulate the network while the victim will vanish by losing the stake ratio gradually. Moreover, in a more realistic scenario with multiple BWH attacker pools and one solo staker who does not join any pools, we show that only one lucky attacker and the solo staker will survive, whereas all the other pools will vanish gradually, revealing the staker's dilemma . These findings indicate that, compared to PoW, the BWH attack on PoS has a much more severe impact due to the attacker's resource aggregation advantage. Our analysis is supported by experiments on massive real blockchain systems and numerical simulations.
Thomas Conlon, Diego VĂctor de MingoâLĂłpez, Andrew Urquhart
ABSTRACT Growth in cryptocurrency funds has followed the wider expansion of the cryptocurrency sector. In this paper, we study the performance persistence and market timing ability of cryptocurrency fund managers. We show that cryptocurrency funds produce remarkable levels of abnormal returns. Moreover, sorting by previous alpha provides compelling evidence of persistence in abnormal returns. Funds with previous excess abnormal returns have high ex post abnormal returns, while cryptocurrency factors explain only a small proportion of the variation in these returns. An ex post outperformance among funds displaying ex ante market timing skills is found, while these ex post abnormal returns can, in turn, be attributed to managerial timing abilities.
Cryptocurrencies do not have proper economic fundamentals. Consequently, economic variables cannot predict crypto prices. According to economic theory, cryptocurrencies are unbacked assets that are inherently unforecastable. However, a growing strand of literature suggests global crypto markets to be informationally inefficient. It implies the possibility of return predictability based on past information. Forecasting the allegedly unforecastable becomes feasible. Keeping it sophisticatedly simple, past infomation can be captured by autoregressive integrated moving average (ARIMA) processes of principal components. However, Principal Component Analysis (PCA) for crypto price series is due to their non-Gaussian property not applicable and requires the assumption of a stochastic trend model. Making use of the Central Limit Theorem, Independent Component Analysis (ICA) overcomes this deficiency. We show that ICA combined with ARIMA modeling more than triples the predictability of global crypto price dynamics. ⢠Crypto markets are found to be inefficient in the sense of majority games. ⢠ICA based ARIMA more than triples predictability of crypto price dynamics. ⢠ICA based ARIMA is most reliable for directional out-of-sample predictions.