This study aims to understand the contagion effect of the major equity market sentiment events, defined as jumps in the VIX index, on cryptocurrency price jumps and the corresponding feedback effect on investors' sentiment. Using recent high frequency intraday data with multivariate Hawkes processes, we find that several noteworthy contagion effects exist between Bitcoin and the market sentiment proxied by the VIX index. First of all, we find that positive market sentiment jumps tend to trigger a moderate level of cross-contagion on both positive and negative jumps in the Bitcoin market, whereas negative market sentiment jumps have no contagion effect on the Bitcoin prices. We also find that the Bitcoin market shows stronger positive self-contagion and cross-contagion effects than the equity market, in general. We also observe a lasting fear of missing out (FOMO) phenomenon in Bitcoin. It is supported by the evidence that the effect of positive jumps in the Bitcoin market lasts three times as long as that in both the equity market and the negative Bitcoin price jumps, and these positive jumps in Bitcoin may serve as a harbinger of equity market movement. These findings provide a better understanding of risk control and policy guidance for the cryptocurrency market.
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.
This paper focuses on automating the analysis of financial news for stocks and cryptocurrencies, thereby providing traders and analysts with actionable insights. In today's fast-paced markets, being up-to-date is the need of the hour; however, manual analysis takes time. To overcome this challenge, the paper employs Python and deep learning tools to make the process of collecting, summarizing, and conducting sentiment analysis on relevant news articles much easier. The key assets analyzed are popular stocks and cryptocurrencies, such as Tesla, Gamestop, Bitcoin, and Ethereum. The system uses Hugging Face's Pegasus model, which is a state-of-the-art transformer, to summarize lengthy financial news into concise, manageable summaries. This reduces the effort required to sift through large amounts of information while preserving essential details. Moreover, the system applies pre-trained sentiment analysis models to gauge the market's overall sentiment-positive, negative, or neutral-toward specific assets, thus making quick and informed trading decisions possible. The paper workflow includes automatically scraping web sources such as Google News and Yahoo Finance, cleaning and processing the data, and exporting results in structured CSV files for further analysis. The files include the ticker symbols, sentiment scores, confidence levels, URLs, and summary text. It is scalable and flexible so that users can input their stock tickers and run real-time analysis with changes in market conditions. Overall, this paper provides an efficient, end-to-end solution for financial news analysis, allowing users to make informed decisions with reduced time and effort on data gathering and interpretation.
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.
The research paper will focus on the influence of major cryptocurrencies, especially Bitcoin and Ethereum, on world financial markets and traditional financial systems. It looks at how, because of their decentralized nature, these digital assets have brought new dynamics to financial markets in the price of other assets, their volatility, and their means of investment. The research design is of a mixed-methods nature, combining quantitative data from financial market indices with qualitative insights from expert interviews. Some of the main lessons learnt are declining value with other financial assets, the interdependency between movements in crypto assets and other linked assets and disruptions in banking, payments and investment. Besides, there are regulation decisions that should consider the fluctuations of the market and security requirements, as well as the analysis of many initiatives in order to provide sufficient regulation frameworks on the international level. The concluding advice proposed how not only to accommodate the disturbance of current financial stability through innovations but also to integrate the utilization of cryptocurrencies.
The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi marketsâAAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)âusing Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, RĂŠnyi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptosâ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and peopleâs attitudes toward them. Based on finance theory, these differing characteristics could affect cryptosâ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.
We develop an economic model of a decentralized exchange with concentrated liquidity (e.g., Uniswap v3 and v4), with a particular focus on the economics of liquidity provision. We demonstrate that providing liquidity for a risky/risk-free asset pool is comparable to investing in a covered call, except that the call option therein is sold at intrinsic rather than market value. Hence, when providing liquidity, liquidity providers forgo the time premium of the call option in exchange for fees, and thus equilibrium liquidity provision decreases in the time premium. Finally, we provide an expression for equilibrium liquidity provision that is useful for empirical work. This paper has been This paper was accepted by Lin William Cong for the Virtual Special Issue on Digital Finance.