Blockchain Papers

Umar Islam, Babar Shah, Abdullah A. Al‐Atawi, Gioia Arnone · 7 authors

No abstract is available for this record.

Qiuying Cheng, Xinyu Wang, Zhuqing Wang, Song Shi

The COVID-19 outbreak triggered an unparalleled health crisis and financial shock. We re-examine whether gold and Bitcoin can function as hedges and safe havens for U.S. financial assets before and after the COVID-19 outbreak. This study employs the quantile-on-quantile and causality-in-quantiles methods to detect the nonlinear and asymmetric relationship of gold and Bitcoin with U.S. financial assets. The results reveal negative dependence of the U.S. dollar, real estate, crude oil, and natural gas on gold and Bitcoin in some quantiles during both periods, indicating that gold and Bitcoin are hedges for these four assets. Following the COVID-19 outbreak, the negative correlations that exist for stock and clean energy with Bitcoin almost all turn positive, gold and Bitcoin lose their ability to hedge stocks and clean energy. Bitcoin can still hedge bonds in middle and high quantiles, whereas gold does not possess this capability against the bond. Additionally, there is an asymmetric causality in the mean and variance from U.S. financial assets to gold and Bitcoin, which generally exists in the middle quantiles but not in the extreme (high and low) quantiles. Our findings provide clear guidelines to market participants on risk management and policy decisions according to market conditions.

Ushaa Eswaran, Vishal Eswaran, Vivek Eswaran, Keerthna Murali

The intersection of artificial intelligence (AI) and decentralized finance (DeFi) heralds a transformative era in the financial landscape, promising unprecedented efficiency, personalization, and innovation. However, this convergence also introduces significant challenges, particularly in the realms of security, risk management, and ethics. This chapter aims to provide a comprehensive exploration of how AI-driven technologies can enhance security and risk management within DeFi ecosystems while addressing the ethical considerations essential for sustainable and responsible innovation. By analyzing current practices, future scenarios, and emerging trends, this chapter seeks to equip finance professionals, technologists, and decision-makers with actionable insights and strategies to navigate the complex dynamics of AI in DeFi. Through real-world case studies and best practices, readers will gain a robust understanding of the critical issues and solutions that will shape the future of secure, ethical, and resilient decentralized financial systems.

sanshao peng, Syed Shams, Catherine Prentice, Tapan Sarker

This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.

Julija Božan, Josip Visković

Aim: As a new asset class, Bitcoin and other cryptocurren-cies can be interesting for investors in the context of return stabilization, especially in times of crisis. We aimed to anal-yse whether Bitcoin can serve as a safe haven for investors in times of crisis. Methods: The data covers the period from September 17, 2014, to April 29, 2021, with 382 observations. Yahoo! Finance served as the source for the Bitcoin prices and Investing.com for the values of the Standard & Poor’s 500 (S&P500) Index. We used the maximum likelihood method to estimate the dynamic conditional correlation model. Results: Due to the high volatility during the analysed peri-od, Bitcoin achieved a higher risk-adjusted return compared to the S&P500 Index. The DCC model showed a positive cor-relation between the returns of the S&P500 and Bitcoin during the analysed period. Conclusions: Our results suggest that Bitcoin may not serve as a safe haven for investors in times of crisis. However, its role in this context should be further evaluated by examin-ing its relationship with other traditional asset classes (gold, commodities) and other types of cryptocurrencies such as stablecoins.

Manali Agrawal, Rui Dias, Mohammad Irfan, Rosa Galvão · 5 authors

Decentralised Finance (DeFi) provides a new way to perform complex financial transactions by exploiting blockchain's ability to maintain a decentralised ledger of transactions without being constrained by centralised systems or human intermediaries. DeFi provides alternative financial instruments that might lessen portfolio risk, especially given the erratic state of the financial markets today. This study analyses the association between the year of the coin in which it was introduced and the market capitalisation of the respective companies. Furthermore, the study also tries to understand the volatility associated with cryptocurrencies using EGARCH & GJR-GARCH models. The results reveal that market capitalisation is not similar for all three stages of the age of cryptocurrency. Also, negative news tends to impact Bitcoin more than positive news, and the volatility is persistent and long-lasting. Ethereum, BNB & Solana see more volatility from absolute past shocks; however, Tether exhibits low but persistent volatility as a stablecoin.

Dora Almeida, Andreia Dionísio, Paulo Ferreira

• Comparative analysis of Bitcoin and Ethereum electricity consumption and returns. • Ethereum's transition to PoS shows a stronger link between returns and energy use. • Shannon and Rényi transfer entropy reveal bidirectional information flow dynamics. • Ethereum returns significantly impact energy consumption, unlike Bitcoin. • A dynamic approach captures time-varying effects of market changes on energy use. Understanding energy consumption associated with cryptocurrency mining gained increasing attention, with the literature focusing mainly on Bitcoin. This study uses data from the two energy consumption indices, to estimate static and dynamic transfer entropies. The results provide a nuanced understanding of the bidirectional relationships and their implications. The dominant direction of information flow for Bitcoin is from electricity consumption to returns, while for Ethereum, it is from returns to electricity consumption, suggesting that Ethereum's returns significantly impact electricity consumption patterns. Results highlight the need for policies that integrate energy forecasting and environmental sustainability considerations and has significant implications for policymaking.

Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.

Jying‐Nan Wang, Samuel A. Vigne, Hung‐Chun Liu, Yuan‐Teng Hsu

No abstract is available for this record.

R. Queiroz, Ladislav Krištoufek, Sérgio Adriani David

No abstract is available for this record.

Esther Lea Ledoux, Nadia Smaïli

Purpose The purpose of this paper is to analyze FTX cryptocurrency frauds. FTX is a former cryptocurrency exchange platform that went bankrupt because of fraud in 2022. Design/methodology/approach Using a qualitative method and a case study of FTX, the authors document the multiple fraud schemes perpetrated. The authors collected media and research articles that discussed the FTX case. The authors analyzed 18 articles. Findings Based on this case, the authors highlight the governance and ethics weaknesses in the FTX environment. The authors also discuss cryptocurrency risks and regulation of cryptocurrencies. The FTX affair has shaken up the international regulatory world, which has been seeking solutions to protect customers and investors and helping banks take positions since 2022. Originality/value This study contributes to the fraud literature by deeply examining cryptocurrency fraud risks. In addition, the findings could help financial institutions and guide them in the cryptocurrency world.

Quang Phung Duy, Oanh Nguyen Thi, Phuong Hao Le Thi, Hai Duong Pham Hoang · 6 authors

Purpose The goal of the study is to offer important insights into the dynamics of the cryptocurrency market by analyzing pricing data for Bitcoin. Using quantitative analytic methods, the study makes use of a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and an Autoregressive Integrated Moving Average (ARIMA). The study looks at how predictable Bitcoin price swings and market volatility will be between 2021 and 2023. Design/methodology/approach The data used in this study are the daily closing prices of Bitcoin from Jan 17th, 2021 to Dec 17th, 2023, which corresponds to a total of 1065 observations. The estimation process is run using 3 years of data (2021–2023), while the remaining (Jan 1st 2024 to Jan 17th 2024) is used for forecasting. The ARIMA-GARCH method is a robust framework for forecasting time series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung–Box test. Findings Using the Box–Jenkins method, various AR and MA lags were tested to determine the most optimal lags. ARIMA (12,1,12) is the most appropriate model obtained from the various models using AIC. As financial time series, such as Bitcoin returns, can be volatile, an attempt is made to model this volatility using GARCH (1,1). Originality/value The study used partially processed secondary data to fit for time series analysis using the ARIMA (12,1,12)-GARCH(1,1) model and hence reliable and conclusive results.

David Umoru, Beauty Igbinovia, Isah Aisha Shaibu, Muhammed Adamu Obomeghie

The study examined the volatility of Bitcoin prices and volatility of exchange rates of oil-producing countries. The study used ARIMA, GARCH estimators for analysis. The study found ARCH effects in the data (heterskedasticity test; p<.05). The GARCH results laid credence to a confirmation of adjustments in the Bitcoin market having significant volatility influence on local currencies. Persistent volatility and volatility clustering found in some of the sampled countries denote increased risk and uncertainty in foreign exchange markets that stimulates increased borrowing costs and reduced liquidity. The actual and forecast values based on the ARIMA method match with an Out-of-Sample period plotted for forecast (27/12/2022 to 27/12/2024) except for Nigeria. The ARIMA models for UAE and Kuwait stand out with excellent fit and prediction accuracy. The poor ARIMA model for Nigeria was ascribed to the hyper-inflation in the economy and extremely volatile money market. In line with the efficient market hypothesis, significant interactions are pegged on available information being already reflected in the current value of the currencies. In effect, past currency rates and Bitcoin trading prices are useful predictors of future prices having factored in the relevant information that could influence currency's value. In addition, future values of local currencies can be forecasted from past values at a significant level of accuracy. Countries should ensure adequate regulation of the foreign exchange markets so as to curtail the wave of volatility risks on returns associated with Bitcoin trading and exchange rates.

Emna Mnif, Yomna Daoud, Akrout Zied, Anis Jarboui

This study explores the potential of Islamic gold-backed cryptocurrencies in sustainable finance by focusing on volatility transmission between these cryptocurrencies and conventional digital assets like Bitcoin and PaxGold. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) approach, we analyze data from December 2019 to July 2023, including the COVID-19 pandemic period. Findings reveal a complex volatility network, with Bitcoin and HelloGold as major transmitters of spillover shocks, while X8X and PaxGold mainly serve as net recipients. Notable interactions between Bitcoin and Islamic gold-backed cryptocurrency markets show short-lived pairwise volatility interactions. Islamic gold-backed cryptocurrencies, net recipients of volatility, absorb market shocks, making them suitable for hedging against volatility. This can aid in diversifying portfolios, mitigating adverse market impacts, and informing risk management and regulatory approaches. Our research emphasizes the importance of Islamic finance in ethical and sustainable investment within the evolving cryptocurrency market.

S.M Liu, Yiluan Yang, Yuxin Fan

Since the financial crisis, bitcoin has become a pioneer among virtual currencies, and much attention has been focused on its mechanisms, market risk and expected development. Despite extensive research into these aspects, the broader significance of bitcoin's existence has gone unnoticed. A critical facet is Bitcoin mining, notorious for its substantial energy consumption and subsequent carbon emissions. This dynamic interplay with the environment and energy market is a pivotal yet understudied aspect of Bitcoin's impact. Consequently, this paper seeks to fill this research gap by synthesizing existing literature on the repercussions of bitcoin mining on energy consumption and the environment. By delving into the intricate relationship between Bitcoin mining and its environmental consequences, the paper aims to shed light on a critical yet often neglected dimension. Furthermore, the analysis extends to examining the responsiveness of prevailing government policies to address the environmental concerns associated with Bitcoin mining. This endeavor underscores the necessity for a comprehensive understanding of the broader consequences of cryptocurrency activities, particularly in the realm of energy consumption and environmental sustainability.

Muhammad Nabil Rateb, Sameh Alansary, Marwa Khamis Elzouka, Mohamad Galal

No abstract is available for this record.

Ding Wang

Stock price prediction is currently a research focus in the financial field, especially in blockchain research. The central focus of this research is to forecast Bitcoin's closing price through the integration of deep learning techniques, specifically employing Long Short-Term Memory (LSTM). This study takes into account that Bitcoin is a mainstream virtual currency, and predicting its future price can help investors make better judgments in trading. The goal of this exploration is to identify the most favorable parameter combinations and function prediction applications, ultimately obtaining the most accurate prediction results. The research process includes dataset selection, data processing, model construction, and training. Then adjust and improve the parameters used in the model, and record the process. Finally, test the model and output the test results. And model testing and result output. At the end of the experiment, the effects of different optimizers and parameters on the training results were compared, and the optimal combination was found. The model's predictive accuracy was evaluated through the examination of test data. This study can provide valuable references for researchers and firms.

Sonal Sahu, Alejandro Fonseca Ramírez, Jong‐Min Kim

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.

Konstantinos A. Dimitriadis, Demetris Koursaros, Christos S. Savva

This study investigates whether representative sectoral stock indices, gold, oil, Bitcoin, and wheat can mitigate risk and improve portfolio performance during normal times versus crises. The cutting-edge Quantile Vector Autoregressive model and the Generalized Dynamic Conditional Correlations (Generalized-DCC) framework are adopted covering from 9 January 2017 until 30 August 2022. Econometric findings by the Q-VAR reveal that oil presents the strongest connection with commodities and stock indices and that Bitcoin and wheat despite their significant linkages with financial markets fail to act as safe havens. Moreover, GDCC-GARCH indicates that the returns of sectoral indices are weakly related but display powerful volatility co-movements. Gold serves efficiently as a hedger and oil follows and both act as better shelters during crises. Nevertheless, Bitcoin partly abides by conventional markets in stressed periods. Notably, wheat reliably works as a hedger overall but does not become a safe haven during crises.

J M de Almeida, Tiago Gonçalves

No abstract is available for this record.

Raj Patel, Jaya Chauhan, Naveen Kumar Tiwari, Vipin Kumar Upaddhyay · 5 authors

No abstract is available for this record.

Sarmini Sarmini

Cryptocurrency has become a significant subject in the global financial market, attracting investors and traders with its high volatility and profit potential. This study analyzes the daily volatility and GARCH volatility of six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), USD Coin (USDC), Tether (USDT), and Ripple (XRP). Daily percentage change data and GARCH volatility are analyzed over specific time periods. The analysis reveals that Bitcoin (BTC) has an average daily percentage change of 0.366%, while Ethereum (ETH) has 0.376%. Litecoin (LTC) shows a daily percentage change of 0.166%, whereas USD Coin (USDC) and Tether (USDT) have very low daily percentage changes, nearly approaching zero. In terms of GARCH volatility, Ethereum (ETH) stands out with a volatility of 0.198, followed by Bitcoin (BTC) with a volatility of 0.121. The study's results indicate that cryptocurrencies are vulnerable to extreme price fluctuations, evidenced by their asymmetry distribution and kurtosis. Volatility correlation analysis reveals significant relationships, important for risk management and portfolio diversification. These findings contribute to understanding cryptocurrency volatility characteristics and aid stakeholders in making informed investment decisions.

Amél Belanès, Foued Saâdaoui, Amina Amirat, Hana Rabbouch

No abstract is available for this record.

Saliha Theiri

Purpose This study aims to examine the influence of geopolitical uncertainty on cryptocurrency markets (CM). Design/methodology/approach Utilizing two distinct sets of daily returns data spanning from January 1, 2019, to May 4, 2023, the analysis employs the geopolitical risk (GPR) index formulated by Caldara and Iacoviello (2022), which encapsulates two pivotal events: the COVID-19 pandemic and the Russia–Ukraine conflict. The cryptocurrency market (CM) encompasses Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and Dogecoin (DOGE). Employing the DCC-GARCH model and supplementing it with wavelet coherence analysis to discern perceptual distinctions between short- and long-term market reactions. Findings The main findings indicate that the GPR index clearly impacts the return of CM in the short-, mid- and long-term periods. BTC exhibited the highest volatility in response to changes in the GPR index. The cryptocurrency market offers a better diversification opportunity, and the impact of geopolitical events varies across time, with their direction and magnitude closely related to the specificity of the CM. Practical implications This research is helpful for financial market investors, portfolio and risk managers, make informed decisions about including cryptocurrencies in their investment portfolios to mitigate the risks in uncertainty period. Originality/value Cryptocurrency market volatility is treated weakly during the risk period. With advanced statistical method, this study links two important events: the COVID-19 pandemic and the Russia–Ukraine conflict and selects the top four cryptocurrencies constituting 80% of the market. This study examines the impact of geopolitical risk on the cryptocurrency market and shows that this market is considered a safe haven.