Blockchain Papers

Shengyao Yang, Meng Nan Zhu, Haiyan Yu

No abstract is available for this record.

Myeong Jun Kim, Sung Y. Park

This study employs a Granger non-causality test in quantiles to analyze the causal relationship between Bitcoin and representative stock indices. We further bifurcate our analysis into pre- and post-COVID-19 periods, providing a unique perspective on hedge evaluation in different market conditions. The empirical findings reveal several key insights. First, a traditional causal test conducted over the entire period, which only considers causality at the mean, leads us to reject the null hypothesis that Bitcoin does not Granger cause any of the nine stock indices. However, we find that Bitcoin is not Granger caused by five out of nine stock indices. Second, by extending the analysis to the overall quantile interval, we find significant results in 12 out of 18 cases. Third, we identify robust causal relationships between Bitcoin and stock indices across lower and higher quantile intervals. Lastly, in the post-COVID-19 period, characterized by heightened price volatility and increased uncertainty, we observe a near-universal reversal in the causal relationships between Bitcoin and stock indices. Furthermore, the number of cases exhibiting causality increased markedly compared with the pre-COVID-19 period, which was characterized by more moderate price volatility and uncertainty.

Israel Nunes de Almeida, Rafael Baptista Palazzi, Marcelo Cabús Klötzle, Antônio Carlos Figueiredo Pinto · 5 authors

No abstract is available for this record.

Vahidin Jeleskovic, Claudio Latini, Zahid Irshad Younas, Mamdouh Abdulaziz Saleh Al‐Faryan

Abstract The growing interest in cryptocurrencies has brought this new means of exchange to the attention of the financial world. This study aims to investigate the effects that a cryptocurrency can have when it is considered as a financial asset. The analysis is carried out from an ex‐post perspective, evaluating the performance achieved in a certain period by three different portfolios. These are the one composed only of equities, bonds and commodities, the second one only of cryptocurrencies, and the third one is a combination of these both ones and thus made up of all considered “traditional” assets and the most performing cryptocurrency of the second portfolio. For these purposes, the classic variance‐covariance approach is applied where the calculation of the risk structure is done via the GARCH‐Copula and GARCH‐Vine Copula approaches. The optimal weights of the assets in the optimized portfolios are determined through Markowitz optimization problem. The analysis mainly showed that the portfolio composed of cryptocurrency and traditional assets has a higher Sharpe index, from an ex‐post perspective, and more stable performances, from an ex‐ante perspective. We justify our selection of the Markowitz approach over conditional VaR and expected shortfall due to their heightened sensitivity to unsystematic extreme events in crypto markets.

Parisa Foroutan, Salim Lahmiri

Abstract The notion that investors shift to gold during economic market crises remains unverified for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the application of various statistical techniques, including cointegration tests, vector autoregressive models, vector error correction models, autoregressive distributed lag models, and Granger causality analyses, we explore the relationship between these markets and assess the safe-haven properties of gold and crude oil for cryptocurrencies. Our findings reveal that during the COVID-19 pandemic, gold is a strong safe-haven for Bitcoin, Litecoin, and Monero while demonstrating a weaker safe-haven potential for Bitcoin Cash, EOS, Chainlink, and Cardano. In contrast, gold only exhibits a strong safe-haven characteristic before the pandemic for Litecoin and Monero. Additionally, Brent crude oil emerges as a strong safe-haven for Bitcoin during COVID-19, while West Texas Intermediate and Brent crude oils demonstrate weaker safe-haven properties for Ether, Bitcoin Cash, EOS, and Monero. Furthermore, the Granger causality analysis indicates that before the COVID-19 pandemic, the causal relationship predominantly flowed from gold and crude oil toward the cryptocurrency markets; however, during the COVID-19 period, the direction of causality shifted, with cryptocurrencies exerting influence on the gold and crude oil markets. These findings provide subtle implications for policymakers, hedge fund managers, and individual or institutional cryptocurrency investors. Our results highlight the need to adapt risk exposure strategies during financial turmoil, such as the crisis precipitated by the COVID-19 pandemic.

Maximilian Nagl

This paper quantifies the intricacy, i.e., non-linearity and interactions of predictor variables, in explaining cryptocurrency returns. Using data from several thousand cryptocurrencies spanning 2014 to 2022, we observe a notably high level of intricacy. This provides a quantitative measure why linear models are often outperformed by machine learning algorithms in predicting cryptocurrency returns. Furthermore, we document that the intricacy in these predictions is considerably larger compared to stocks. Our analysis reveals that interactions are gaining importance over time, while individual non-linearity of the drivers is diminishing. This adds to the emerging literature on spillover effects between cryptocurrencies, traditional finance and the economy. This finding is important for investors as well as regulators as the high intricacy proposes challenges to both actors in the market.

Woosung Jung, Haerang Park

No abstract is available for this record.

Daniela Balutel, Marcel Voia

Cet article explore les facteurs influençant les fluctuations du prix du Bitcoin et identifie les indicateurs clés pour prédire les mouvements du marché. Les tendances positives des prix sont associées à une activité accrue du réseau, à des blocs de plus grande taille et à des retours sur investissement plus élevés, offrant des opportunités potentielles aux investisseurs. À l’inverse, l’augmentation des valeurs du nombre d’actions d’adresse, de la valeur du marché des capitaux, de l’émission de Coinbase et du retour sur investissement, suggère un risque plus élevé de chocs de prix négatifs et de tendances potentielles à la baisse du marché. L’étude met également en évidence des facteurs atténuants tels que la capitalisation du Bitcoin, l’offre actuelle et la vitesse actuelle, offrant des informations précieuses pour améliorer la stabilité du marché. En outre, les résultats suggèrent qu’à mesure que l’ampleur absolue des chocs de prix augmente, la fréquence des chocs de prix négatifs dépasse celle des chocs positifs. Simultanément, on observe une diminution significative de la prévisibilité des chocs négatifs par rapport à la prévisibilité des chocs de prix positifs.

Dongxu Li, Yiran Ning, Peng Wang

No abstract is available for this record.

Roua Mili, Anyssa Trimech, Saloua Benammou

The significant release of carbon dioxide into the atmosphere poses substantial threats to both global ecosystems and human well-being. Among the primary sources of these emissions, the transportation sector emerges as a crucial contributor. There exists a direct and notable link between CO2 emissions and transportation, with this industry being a major emitter of greenhouse gases, and CO2 acting as the primary catalyst for global climate change. Additionally, the swift spread of cryptocurrencies, coupled with increasing dependence on advanced technologies and the quick pace of technological advancements, poses ongoing challenges, especially concerning environmental sustainability and carbon emissions. This research explores the tangible effects of Bitcoin transaction volumes and their energy usage on environmental sustainability. The findings reveal a strong link between CO2 emissions and Bitcoin transactions, showing a complex relationship between public awareness of environmental problems related to Bitcoin transactions. The study confirms that energy use positively influences CO2 emissions both in the short and long term.

Chiara Oldani, Giovanni S. F. Bruno, Marcello Signorelli

No abstract is available for this record.

Γεωργία Ζουρνατζίδου, Ioannis Mallidis, Dimitrios Farazakis, Christos Floros

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.

José Parra-Moyano, Daniel Partida, Moritz Gessl, Somnath Mazumdar

No abstract is available for this record.

Bilgehan Teki̇n

Abstract This study examined the structural breakdowns and co-movements of Bitcoin (BTC) and Ethereum (ETH) cryptocurrencies from the onset of the COVID-19 pandemic. The Bai-Perron test was used to determine the change in the mean and variance of the two principal actors regarding market capitalization in the cryptocurrency market. Wavelet coherence analysis was also used to detect the co-movements between BTC and ETH. As a result of the study, several similar breaks were seen in each BTC and ETH series. Only one break could be directly associated with the pandemic process. This means that the pandemic is internalized and normalized in the process. The wavelet coherence results indicate a strong positive dependency (dark warm colours) between BTC and ETH and in phase (in the same direction) in the short and long bandgaps.

Mirzat Ullah, Kazi Sohag, Hossam Haddad

This empirical research study aims to investigate the asymmetric spillovers among crypto and key financial assets such as gold, equity, bonds, and the dollar-to-ruble exchange rate volatility, focusing on new developments during the Russia-Ukraine conflict in 2022. Utilizing time- and frequency-domain methodologies, this study conducts an in-depth analysis employing daily frequency data from January 01, 2018, to May 30, 2023. The study employs value at risk and conditional value at risk estimations to assess potential losses in the portfolio during the crisis. The findings reveal that Bitcoin exhibits hedging ability, enabling investors to diversify risk among the underlying financial assets. The study observes a significant increase in Bitcoin investments during the crisis, leading to heightened volatility and uncertainty. Negative news has a stronger impact compared to positive news, underscoring the importance of prudent asset allocation for risk mitigation. The implications of our findings are particularly significant for financial policymakers and trade partners of Russia. The study urges them to differentiate their short- and long-term strategies and procurement contracts. In the long run, policymakers should be cognizant of the influence of the riskiness of crypto assets during economic crises, guiding the formulation of prudent policies and investment decision-making initiatives.

Muqaddas Noureen, Ammar Ahmed Siddiqui, Abdul Musawwer

A wide range of interest has been shown in this field by investors and decision-makers due to the correlation between cryptocurrencies, Asian stock indexes, and commodities in the global financial market through partitioning the data and applying the wavelet analysis approach to analyze the described movement of various variables. Moreover, this study seeks to provide information to fintech financiers and policymakers as well about the examined variables. Time-series data was obtained from 01st January 2019 to 31st December 2023 and analyzed from multiple internet sources about commodities, Asian Stock indices, and cryptocurrency prices. To find specific patterns and trends in a certain area, research used the wavelet approach to analyze the data and separate each series into a separate frequency band. The findings showed that there is a highly significant relationship among commodities, Asian stock indexes, and cryptocurrency in different frequency bands. On the other hand, a negative correlation was found in low frequency bands between the prices of commodities and cryptocurrency, whilst a positive correlation was found in high frequency bands between cryptocurrency and Asian Stock indices.

Mohamed Saleem, Yianni Doumenis, Epameinondas Katsikas, Javad Izadi · 5 authors

This study aims to empirically examine the relationship between cryptocurrency and various facets of the financial system. It seeks to provide a comprehensive understanding of how cryptocurrencies interact with, and influence, the stock market, the U.S. dollar’s strength, inflation rates, and traditional banking operations. This is carried out using linear regression models, Granger causality tests, case studies, including the collapse of the Futures Exchange (FTX), and the successful integration of Binance. The study unveiled a strong positive correlation between cryptocurrency market capitalization and key financial indicators like the Dow Jones Industrial Average, Consumer Price Index, and traditional banking operations. This indicates the growing significance of cryptocurrencies within the global financial landscape. However, a mild association was found with the U.S. dollar, suggesting a limited influence of cryptocurrencies on traditional fiat currencies currently. Despite certain limitations such as reliance on secondary data, methodological choices, and geographic focus, this research provides valuable insights for policymakers, financial industry stakeholders, and academic researchers, underlining the necessity for continued study into the complex interplay between cryptocurrencies and financial stability.

Christof Ferreira Torres, Albin Mamuti, Ben Weintraub, Cristina Nita-Rotaru · 5 authors

The emergence of decentralized finance has transformed asset trading on the blockchain, making traditional financial instruments more accessible while also introducing a series of exploitative economic practices known as Maximal Extractable Value (MEV). Concurrently, decentralized finance has embraced rollup-based Layer-2 solutions to facilitate asset trading at reduced transaction costs compared to Layer-1 solutions such as Ethereum. However, rollups lack a public mempool like Ethereum, making the extraction of MEV more challenging. In this paper, we investigate the prevalence and impact of MEV on Ethereum and prominent rollups such as Arbitrum, Optimism, and zkSync over a nearly three-year period. Our analysis encompasses various metrics including volume, profits, costs, competition, and response time to MEV opportunities. We discover that MEV is widespread on rollups, with trading volume comparable to Ethereum. We also find that, although MEV costs are lower on rollups, profits are also significantly lower compared to Ethereum. Additionally, we examine the prevalence of sandwich attacks on rollups. While our findings did not detect any sandwiching activity on popular rollups, we did identify the potential for cross-layer sandwich attacks facilitated by transactions that are sent across rollups and Ethereum. Consequently, we propose and evaluate the feasibility of three novel attacks that exploit cross-layer transactions, revealing that attackers could have already earned approximately 2 million USD through cross-layer sandwich attacks.

Thobekile Qabhobho, Cwayita Mpuku, Izunna Anyikwa, Andrew Phiri

since the onset of the cOViD-19 pandemic, african currencies, cryptocurrencies, and commodity markets have undergone significant fluctuations, displaying fat-tail properties that lies at the outer ends of the normal probability curve.the recent Russia-Ukraine war has further disrupted these markets, generating considerable interest among academics and practitioners.Our study delves into tail-end returns and volatility connectedness between Bitcoin, crude oil, gold, and four african currencies amidst the cOViD-19 and Russia-Ukraine war.employing a quantile vector autoregressive (QVaR) approach, we analyze tail-end spillover effects between markets from 4 november 2019, to 7 september 2022.Our findings reveal heightened connectedness at the quantile ends of co-movements, with left-tail spillovers being more pronounced for returns, while right-tail spillovers dominate for volatility.Bitcoin, and to a lesser extent gold and oil, emerge as effective tail-ended hedges for the egyptian Pound and nigerian naira but not for other african currencies like the algerian Dinar and south african Rand.consequently, users of egyptian and nigerian currencies in international financial markets can seek hedging opportunities in traditional cryptocurrencies and commodities during recent Black swan events, unlike those using south african and algerian currencies.additionally, our results suggest limited diversification benefits associated with (i) currencies linked to oil-exporting or oil-importing countries, (ii) currencies linked to shariah-compliant financial systems, but do indicate diversification benefits in high-inflation environments.these findings hold relevance for investors seeking improved hedging strategies against african currency risk and for african policymakers aiming to enhance intra-continental trade, foreign direct investment, and cross-border business expansions.

Özgür Ergül, Tuba Karakaş

We analyze the hedging feature of gold against inflation by analyzing the factors affecting gold prices for the post-2013 period, including the tapering process in the United States. Our results show that especially demand for gold Exchange Traded Funds (ETFs) and US 10-year bond rates are effective on gold prices in this period. Inflation has no statistically significant effect on gold prices over the sample period; however, in the subperiod, excluding 2014–2019, inflation has a statistically significant positive impact on gold prices. We conclude that gold does provide a partial hedge against inflation as an investment tool, at least for the recent period. Furthermore, our analysis of Bitcoin’s effect on gold prices starting in the second half of 2016 shows no statistically significant relationship.

Muhammad Bukhari, Abeera Ayaz Ansari, Muhammad Yousif, Muhammad Hassan · 5 authors

No abstract is available for this record.

Barbara Będowska-Sójka, Joanna Górka, Danial Hemmings, Adam Zaremba

No abstract is available for this record.

Huiqing Wang, Yongrong Huang, Zhide Chen, Xu Yang · 9 authors

No abstract is available for this record.

Gülcihan Özdemir

Due to cryptocurrencies' rising prices, like bitcoin, more and more people are becoming interested in them. Success in this business depends on a good price prediction. Several methods, including heuristic and machine-learning-based ones, can currently estimate the price with varied degrees of success. This study will use the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) model to predict the price's general direction over the next 10 days. Along with popular traders' indicators, the previous day's price will be used. The findings demonstrated that, despite errors, price direction predictions—an increase, a drop, or a stable price—are typically accurate.