Blockchain Papers

Ahmed Bouteska, Lê Thanh Hà, M. Kabir Hassan, M. Faisal Safa

No abstract is available for this record.

Mohamed Shaker Ahmed, Mohamad Husam Helmi, Aviral Kumar Tiwari, Alanoud Al‐Maadid

Purpose This paper aims to investigate the relationship between investor attention and market activity (return, volatility and volume) using a sample of 14 clean energy cryptocurrencies (hereafter green cryptocurrency), namely, Chia, Cardano, Stellar, Tron, Ripple, Nano, IOTA, EOS, Bitcoin Green, Alogrand, Hedara, Polkadot, FLOW and Tezos. Design/methodology/approach This paper use 26040 crypto-day observations and a range of econometric techniques, including Dynamic Granger causality, Panel vector autoregression (VAR), Impulse response function and the decomposition of forecast error variance. Findings Based on 26040 crypto-day observations, this paper finds a bidirectional Granger causal relationship between investor attention and all measures of market activity, namely, return, absolute volatility, squared volatility and volume. The panel VAR and impulse response function demonstrate that market activity in the green crypto ecosystem, especially volatility and volume, is considerably responsive to changes in investor attention proxied by Google search volume (hereafter Google search volume (GSV)). The findings also demonstrate a significant asymmetric effect of return and volume on investor attention since past negative shocks “or bad news” in return and volume are more likely to grab the investor’s attention. All in all, our study emphasizes the crucial role of investor attention in the green crypto ecosystem. Originality/value (i) The research is the first to shed light on investor attention in the green cryptocurrency market. (ii) The paper uses a wide range of green cryptocurrencies to offer a comprehensive picture of the green cryptocurrency ecosystem. (iii) This paper is the first to use the panel Granger causality to investigate investor attention in the cryptocurrency market which provides several advantages over the conventional Granger causality approach. (iv) This paper is the first to provide novel empirical evidence on the prevalent influence of investor attention in the green crypto market.

Abdulrazak Abdulrahman Abubakar, Jules Clément, Abieyuwa Ohonba

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.

Raúl Gómez Martínez, María Luisa Medrano García

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.

Thomas H.A. Joubert

No abstract is available for this record.

Zih-Chun Huang, Ivan Sangiorgi, Andrew Urquhart

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.

Imran Yousaf, Afsheen Abrar, Shoaib Ali, John W. Goodell

No abstract is available for this record.

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.

Mauro Aliano, Массимилиано Феррара, Stefania Ragni

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.

Arafet Farroukh, Martina Metzger, Héla Mzoughi

No abstract is available for this record.

Shoaib Ali, Umar Nawaz Kayani, Imran Yousaf

Growth in digitalization has created a potential boost for Non-fungible tokens (NFTs) and decentralized finance (DeFis) assets in the modern world. Therefore, this study aims to examine the comovement between the recently developed comprehensive measure of news sentiment index (NSI) and selected digital assets. For this purpose, we have utilized the wavelet transform, wavelet correlation, and wavelet coherence econometric model to assess interdependency in both time and frequency between news sentiments and digital assets. Our wavelet correlation and covariance results suggest that almost all the digital assets exhibit a negative relationship with NSI. Moreover, the wavelet coherence results confirm that there is no significant comovement in the short to medium-term horizon, suggesting that both NFTs and DeFi can be used as hedges against the NSI. Furthermore, we observe small patches of significant negative comovement between NSI and digital assets in the long term, which correspond to the initial days of COVID-19. Our results confirm selected digital assets’ hedging role against news-driven uncertainty. This study finding provides essential information to policymakers, international investors, and investment managers to make effective decisions.

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.

Emmanuel Joel Aikins Abakah, John W. Goodell, Zunaidah Sulong, Mohammad Abdullah

No abstract is available for this record.

Mariem Bouzguenda, Anis Jarboui

No abstract is available for this record.

Qing Zhu, Jianhua Che, Shan Liu

No abstract is available for this record.

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.

Shoaib Ali, Ting Zhang, Imran Yousaf

No abstract is available for this record.

Ditong Liu

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.

Toshiyuki Yamawake, Joseph Sheely, Roberto Serrano, Jiro Hodoshima

No abstract is available for this record.

Houjian Li, Houjian Li, Fangyuan Luo, Lili Guo · 5 authors

No abstract is available for this record.

Peng Liu, Ying Yuan

No abstract is available for this record.

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.

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.