Blockchain Papers

Heng Lei, Minggao Xue, Jing Ye

No abstract is available for this record.

SWU “Neofit Rilski”, Blagoevgrad, Bulgaria, Gancho Ganchev, Mariya Paskaleva, SWU “Neofit Rilski”, Blagoevgrad, Bulgaria

The current research aims to reveal whether cryptocurrencies may be included in investors’ portfolios as instruments for diversification and hedging against global systematic risk. The main contribution of the research is the fact that it provides proof of the usage of cryptos for hedging against global financial systematic risk. This seems to confirm the main hypothesis in the study about the role of money and cryptos in the contemporary global financial economy. The research reveals evidence that cryptocurrencies can play the role of global market predictors.

Vikrant Vikram Singh, Harendra Singh, Aleem Ansari

Purpose: Bitcoin, the most popular form of virtual currency, currently holds the highest market capitalization among cryptocurrencies and serves as a benchmark for the typical cryptocurrency. The main goal of this research is to evaluate Bitcoin’s potential as a distinct asset class. This will be achieved by building upon previous studies and investigating its utility as both a hedging instrument and a tool for portfolio diversification. Methodology: In this study, Bitcoin is compared with other asset classes, such as key stock indices of India’s Nifty-50 and Sensex, and key currency pairs with the Indian Rupee, including the US dollar ($), Euro (€), Pound sterling (£), and Japanese Yen (¥). Gold, as one of the most precise commodities, is analyzed using descriptive statistics to verify and confirm its properties as a distinct asset class. Additionally, the study employs the DCC-GARCH model to ascertain whether Bitcoin qualifies as both a hedging instrument and a tool for portfolio diversification. Findings: The findings of this study indicate that Bitcoins constitute a unique and separate category within alternative assets and investment classes. Various descriptive statistics confirm that Bitcoins exhibit characteristics of an asset class. Additionally, the study reveals and verifies the hedging and portfolio diversification capabilities of Bitcoin based on the results of the DCC-GARCH model. Practical Implications: The findings of this study will prove useful for investors considering cryptocurrency (Bitcoin) as an alternative asset class for diversifying their portfolios and hedging against volatility. Originality/Value: This study contributes to the research paradigm of Bitcoin finance by providing a perspective from a developing nation on Bitcoin as an asset class, which differs from other asset classes such as Nifty-50, Sensex, USD–INR, EUR–INR, GBP–INR, JPY–INR, and gold. While previous research has predominantly focused on developed nation contexts, this study underscores the importance of examining Bitcoin’s role in portfolio diversification and hedging strategies. To enhance our understanding, this research presents daily observations of recent economic data spanning from 2011 to 2021. Assessing whether Bitcoin qualifies as an alternative investment and a distinct asset class is crucial, as it could significantly influence investment decisions and serve as a valuable tool for risk management and diversification purposes for investors.

Aktham Maghyereh, Salem Adel Ziadat

Abstract The main objective of this study is to investigate tail risk connectedness among six major cryptocurrency markets and determine the extent to which investor sentiment, economic conditions, and economic uncertainty can predict tail risk interconnectedness. Combining the Conditional Autoregressive Value-at-Risk (CAViaR) model with the time-varying parameter vector autoregressive (TVP-VAR) approach shows that the transmission of tail risks among cryptocurrencies changes dynamically over time. During crises and significant events, transmission bursts and tail risks change. Based on both in- and out-of-sample forecasts, we find that the information contained in investor sentiment, economic conditions, and uncertainty includes significant predictive content about the tail risk connectedness of cryptocurrencies.

Taicir Mezghani, Mustafa Raza Rabbani, Yousra Trichilli, Boujelbène Abbes

The study investigates the dynamic interconnections and opportunities for hedging among cryptocurrency, commodity, and Islamic stock markets using DCC-GARCH and Spillover connectedness models. Using daily data covering the Russia-Ukraine war and COVID-19 outbreak from December 1, 2019 to April 15, 2022, we document weak and frequently negative correlation between Bitcoin and Islamic stock markets. Thus, Bitcoin could be viewed as a haven from Islamic stock market losses. The results also indicate that Bitcoin's diversification benefits are normally steady and increase considerably during turbulence. Furthermore, the net return spillovers from the Bitcoin market remain above zero during most of the study period. We also find that utilizing Bitcoin as a hedge during the COVID-19 pandemic phase leads to higher expenses. The outcomes of this investigation are expected to carry substantial ramifications for Indonesian investors and portfolio managers who adhere to Shariah law since they will enable them to comprehend the advantages of diversifying portfolios across various periods of stock holding or investment horizons.

Xiaochun Guo

Abstract COVID-19 has significantly influenced global financial markets, including Bitcoin. Recent studies have focused on investigating the first wave of the COVID-19 outbreak and accounting for market changes, which were mostly due to the pandemic. This research not only analyzes the contagion effects of COVID-19 but also considers aftermath events beyond the first pandemic wave to examine spillovers of Bitcoin. The study employs Diebold and Yilmaz’s method to explore the static and dynamic spillovers of the selected variables and identifies several major global events, including crypto-specific affairs, macroeconomic policies, and geopolitical conflicts, to explain the new market dynamics of Bitcoin using network analysis. The findings identify a few high-contagion periods related to Bitcoin. The paper also found that Bitcoin is more likely to produce extreme returns and is more connected to other markets. Contagion effects “from” and “to” other markets are asymmetrical in terms of arrival time and market response. Bitcoin is more likely to be affected by other markets in extreme situations and receives spillovers from them sooner than it transmits spillovers to others. In the context of various global events, impacts arising from developed countries are stronger. China still has some impact on cryptocurrency markets, but they are waning. Bitcoin is thus not a safe haven from the shocks of global events, but can sometimes work as a hedge or diversifier. The results offer alternative explanations for Bitcoin’s different market dynamics and enrich our understanding of Bitcoin’s safe haven, hedge, and diversifier properties within a diversified portfolio.

Imran Yousaf, Shoaib Ali, mohamed marei, Mariya Gubareva

This study investigates the static and dynamic interdependence of the Islamic cryptocurrency and metal markets using the TVP-VAR methodology. The empirical findings suggest that Islamic cryptocurrencies are the recipients of both return and volatility spillovers, while most metals act as transmitters of these spillovers. The dynamic spillovers are intensified in the COVID-19 period compared to the pre-COVID-19 period. We find that the return connectedness is short-lived lived whereas the volatility connectedness is a long-term phenomenon. Finally, we also compute the optimal weights, hedge ratios, and hedging effectiveness during COVID-19. The results suggest that investors should add Islamic cryptocurrencies to metals portfolios in order to get maximum risk-adjusted returns during the pandemic crisis. Our findings are helpful for portfolio managers and investors in making decisions regarding diversification, portfolio allocation, forecasting, and hedging.

Praise Otito Obanya, Modisane Seitshiro, Carel P. Olivier, Tanja Verster

Cryptocurrencies are widely regarded as volatile and less predictable assets by financial participants. The behaviour and dynamics of Bitcoin’s daily volatility, obtained by fitting GARCH models, are investigated for a period of 8 years using permutation entropy which is represented by the variable H for calculations. The best fitting GARCH models selected are the FIGARCH(1,0.7,1) and SGARCH(1,1) models based on maximum likelihood estimation, Akaike Information Criterion and Bayesian Information Criterion. Simulated volatilities are also obtained from the best fitting GARCH models using their respective parameters, to confirm how well the models fit. The results obtained show that the H values of Bitcoin are generally low and that the dynamics of Bitcoin’s volatility is quite predictable, as Bitcoin’s volatility is most likely to decline over time than increase or have an alternating movement. Also, the simulated volatilities show good agreement with the real-world volatility, confirming the models as good fits.

Amro Saleem Alamaren, Korhan K. Gökmenoğlu, Nigar Taşpınar

Abstract This study investigates volatility spillovers and network connectedness among four cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin), four energy companies (Exxon Mobil, Chevron, ConocoPhillips, and Nextera Energy), and four mega-technology companies (Apple, Microsoft, Alphabet, and Amazon) in the US. We analyze data for the period November 15, 2017–October 28, 2022 using methodologies in Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Economet 16(2):271–296 2018). Our analysis shows the COVID-19 pandemic amplified volatility spillovers, thereby intensifying the impact of financial contagion between markets. This finding indicates the impact of the pandemic on the US economy heightened risk transmission across markets. Moreover, we show that Bitcoin, Ethereum, Chevron, ConocoPhilips, Apple, and Microsoft are net volatility transmitters, while Tether, BNB, Exxon Mobil, Nextera Energy, Alphabet, and Amazon are net receivers Our results suggest that short-term volatility spillovers outweigh medium- and long-term spillovers, and that investors should be more concerned about short-term repercussions because they do not have enough time to act quickly to protect themselves from market risks when the US market is affected. Furthermore, in contrast to short-term dynamics, longer term patterns display superior hedging efficiency. The net-pairwise directional spillovers show that Alphabet and Amazon are the highest shock transmitters to other companies. The findings in this study have implications for both investors and policymakers.

Yaoyue Tang, Karina Arias-Calluari, M. N. Najafi, Michael Harré · 5 authors

This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.

Reshma Sudra

Digital currencies and coins are methods of computer-generated currency which uses cryptography for safety and operate individually of a dominant authority, like governments and economic institution. They are spread out and usually utilize blockchain technology to note transactions strongly. Cryptocurrencies such as Bitcoins, Ethereum, and some others have grown popularity in the latest eons for their latent to deliver borderless, secure, and fast transactions. Yet, they also arisen with threats like security concerns, regulatory uncertainty, and price volatility. It is vital for operators to conduct detailed research and comprehend the threats included before capitalizing or utilizing cryptocurrencies. Even though cryptocurrencies and coins have grown famous and are being gradually used for countless transactions, it is significant to remind that they still have not passed the traditional banking systems. Traditional banking sectors still perform an important part in the worldwide economic system, offering services like payment processing, savings accounts, and lending. Conversely, the growth of cryptocurrencies has directed to conferences about the possible influence on the financing sector and the necessity for traditional banking systems to acclimate to the varying setting of digital economics. It is important to observe these growths closely to comprehend the evolving association among the crypto-currencies and system of traditional banking. Hence, the current study gives the deep knowledge in the usage of crypto currencies, digital coins and their role in financial sectors that is dominating the traditional banking sector. And analyzed the impact of crypto currencies and digital coins on investors and economy of the nation and also regarding the easy accessibility of finance.

Muneer Shaik, Mustafa Raza Rabbani, Mohd Atif, Ahmet Faruk Aysan · 6 authors

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Anupam Dutta

Abstract The main objective of this paper is to forecast the realized volatility (RV) of Bitcoin futures (BTCF) market. To serve our purpose, we propose an augmented heterogenous autoregressive (HAR) model to consider the information on time-varying jumps observed in BTCF returns. Specifically, we estimate the jump-induced volatility using the GARCH-jump process and then consider this information in the HAR model. Both the in-sample and out-of-sample analyses show that jumps offer added information which is not provided by the existing HAR models. In addition, a novel finding is that the jump-induced volatility offers incremental information relative to the Bitcoin implied volatility index. In sum, our results indicate that the HAR-RV process comprising the leverage effects and jump volatility would predict the RV more precisely compared to the standard HAR-type models. These findings have important implications to cryptocurrency investors.

Inés Jiménez, Andrés Mora‐Valencia, Javier Perote

No abstract is available for this record.

Joscha Beckmann, Teo Geldner, Jan Wüstenfeld

We provide a novel perspective on the Bitcoin market, investigating determinants of investor positions and their response to public information proxied by sentiment indicators. We distinguish between investors by size and observe their respective behaviour concerning incoming information. We find that price dynamics and media coverage lead to different decisions depending on the Bitcoin portfolio size. Retail investors react strongly to incoming public information and media narratives, with their decisions strongly influenced by sentiment and media attention. Conversely, the response of large-scale investors to such information is much weaker because they arguably have different, non-public information and divergent investment objectives.

Eugene Msizi Buthelezi

No abstract is available for this record.

Guizhou Wang, Kjell Hausken

Bitcoin has been gaining increasing attention in academia and industry.This article investigates Bitcoin's research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection.The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market.The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research.The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012-2016), cryptocurrency and market efficiency (2017)(2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019-2022).The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy.The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.

Providence Mushori, Delson Chikobvu

This paper analyses the extreme tail behaviour of Bitcoin returns by fitting a Generalised Pareto Distribution (GPD). The GPD is used to model the extreme daily Bitcoin returns over the period 2008 to 2023. The returns above the chosen thresholds, for both Bitcoin gains and losses, are selected. The GPD is then fitted to the selected excess returns. The Anderson Darling (AD) and Kolmogorov Smirnov (K-S) goodness-of-fit tests reveal that the GPD captures the distribution of the Bitcoin excess returns. The Value at Risk (VaR) and Expected Shortfall (ES) under the GPD are used to measure the extreme tail risk of the Bitcoin returns. The upside risk (gains) is found to outweigh downside risk (losses), and this gives insight to investors interested in Bitcoin.

Dhoha Mellouli, Imen Zoglami

Purpose: This paper pioneers exploring the relationship between cryptocurrencies, considering the case of non-fungible tokens (NFTs) and traditional cryptocurrencies Design/Methodology/Approach: The analysis is performed through an innovative TVP-VAR frequency connectedness approach, revealing a substantial level of dynamic integration and return transmission among cryptocurrencies systems. Findings: Our findings are multifaceted. Firstly, that there is higher total connectedness in the bearish and bullish market conditions compared to normal conditions. Secondly, the degree of connectedness is even stronger during tranquil and turbulent times such as the Covid-19 pandemic and the Russian-Ukrainian war. Thirdly, the network's net transmission behavior is predominantly by the short-term dynamics for NFT and by the long-term dynamics for Conventional cryptocurrencies, and assets' roles as net-transmitter and net-receiver can change over time. Practical Implications: These findings inform investors, traders, and portfolio managers to prioritize risk management during high-risk periods, such as COVID-19 and the Russian-Ukrainian conflict, as crises involve non-diversifiable systematic risks, demanding careful risk mitigation. Originality/Value: One of the main challenges of cryptocurrencies is determining the nature of the dynamics of their connectivity. The originality and the value of this research is to investigate whether cryptocurrencies evolve in a similar manner to each other.

Umar Nawaz Kayani, Fakhrul Hasan

The advent of cryptocurrencies and blockchain technology has sparked a revolutionary shift in the financial sector. This study sets out on a wide-ranging investigation to understand the nuanced dynamics, repercussions, and potential future paths of this shifting environment in the UK and USA. The primary goals of the research are to examine how cryptocurrencies affect financial markets and conventional banking systems; to examine how blockchain technology might be used in the financial sector; to assess policy and regulatory considerations; and to predict and plan for the future. This research digs into how cryptocurrencies have revolutionized the banking and finance sectors. Analysis of adoption rates, market volatility, and integration methods sheds light on the changing position of cryptocurrencies in investment portfolios, reconfiguration of asset classes, and coping mechanisms of conventional financial institutions. When looking at the financial sector as a whole, the transformational potential of blockchain technology becomes clear. The advent of DeFi, smart contracts, and asset tokenization offers new prospects to improve financial transactions, increase transparency, and broaden participation in the investment market. The research analyzes cryptocurrencies and blockchain technology from a policy and regulatory perspective. The delicate balancing act between stimulating innovation and guaranteeing consumer protection, market integrity, and financial stability is highlighted by a comparison of the regulatory methods adopted in the United Kingdom and United States, as well as proposals from international organizations. The research identifies potential future paths for these technologies and their implications. Opportunities and challenges that will influence the future of finance emerge, with a focus on central bank digital currencies (CBDCs), sustainable blockchain solutions, and interdisciplinary collaborations. As this deep dive comes to a close, the transformational power of cryptocurrencies and blockchain technology is highlighted. It sheds light on the forces that are altering the structures of the world’s financial markets, conventional banking structures, and regulatory frameworks. The findings and critical assessment stress the need for well-considered choices, ethical innovation, and interdisciplinary cooperation in order to succeed in an ever-changing environment. To further democratize access, improve transparency, and reshape the economic fabric of our planet, the future of finance resides at the confluence of tradition and innovation, where cryptocurrencies and blockchain technology exist.

Nezir Köse, Hakan YILDIRIM, Emre Ünal, Boqiang Lin

Abstract This study examines the Bitcoin price by taking into account global factors, including the Chicago Board Options Exchange's Market Volatility Index (VIX), the US dollar index, the gold price, the oil price, and Bitcoin price volatility. The analysis is conducted using the structural vector autoregression (SVAR) model. The variance decomposition findings revealed that the influence of the VIX on the Bitcoin price was initially restricted, but progressively intensified over time. Among the indicators, Bitcoin price volatility had the highest explanatory share in both daily and weekly data analysis. The impulse response functions demonstrated a statistically significant inverse relationship between the VIX and the Bitcoin price. Furthermore, the analysis revealed that the Bitcoin price was mostly impacted by its own volatility. This implies that investing in Bitcoin requires a certain level of risk‐taking.

Takeshi Tsuyuguchi, Haibo Wang

This article investigates the Ethereum Merge, which occurred on 15 September 2022, and we employ the time-series difference in differences (DiD) model and vector autoregression (VAR) models and analyse how the protocol change from proof-of-work to proof-of-stake (PoS) affects the dynamic relationship between cryptocurrency returns and network factors. The results show that the Merge caused a structural change between Ethereum and Bitcoin networks. The network factors of Ethereum show a significant increase compared to Bitcoin, the cointegration has been strengthened and the lag length is shortened after the Merge. The spillover effect on the Bitcoin network can be seen from both DiD and VAR, indicating the increasing impact of the Ethereum network on Bitcoin. The concern of losing the number of participants due to the implantation of PoS on cryptocurrency is not apparent on Ethereum Merge, and it increases the investors’ attention and involvement.

Siqi Yu

The significant increase in cryptocurrency trading on digital blockchain platforms has led to a growing interest in employing machine learning techniques for the effective prediction of highly nonlinear and nonstationary data, becoming increasingly popular among both individual and institutional market participants. The aim of this research is to deal with the challenging task of predicting the closing prices of two prominent cryptocurrencies, Binance Coin (BNB) and Ethereum (ETH), utilizing machine-learning techniques. This study evaluates the efficacy of various machine learning models in predicting cryptocurrency prices, with a particular focus on Support Vector Machines for Regression (SVR), least-squares Boosting (LSBoost), and Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System (ANFIS). These models are compared under various metrics. ANFIS models exhibited superior predictive performance on both training and testing datasets based on diverse performance metrics. Comparatively, SVR with a linear kernel demonstrated strong generalization capabilities, particularly on the testing set. LSBoost, while showing promise in training accuracy, indicated results with higher test errors. ANN models maintained a balance between training and testing. This comparison showed the models’ effectiveness, particularly the robustness of ANFIS in capturing the volatile cryptocurrency market trends. The experimental data suggest that certain of the above models can be utilized to predict the ETH and BNB closing price in real time with promising accuracy and experimentally proven profitability.

Ruijie Chen

Cryptocurrencies have become a popular investment option and the Ethereum has become a mainstream cryptocurrency because of the additional functionality that can be accomplished with the backing of the powerful Ethereum network compared to Bitcoin. The high volatility of Ethereum offers both profits and risks, making it crucial to improve the forecasting ability for its price volatility. The results of this study could be useful for investors and policymakers who are interested in understanding and managing the risks associated with investing in Ethereum. Several studies have explored similar topics using heterogeneous autoregressive (HAR) models for cryptocurrencies, but this paper offers a more expansive approach. This paper employs five-minute high-frequency data to construct 4 HAR models to predict the volatility of Ethereum, taking into account the impact of structural breaks, Bitcoin, SP500 and VIX. The model that considers all factors outperforms other models for out-of-sample predictions for the 1-week forecasting. Due to the nature of the Ethereum price, the HAR-RV model has achieved a perfect fit in 1-day and 1-month forecasting. Therefore, other models have a very small improvement in fitness and prediction accuracy. This paper contributes to the understanding of Ethereum’s volatility and its impact on the cryptocurrency market.