Blockchain Papers

Muhammad Umar, Ahmad Arslan, Robert Sroufe, Zikri Muhammad

No abstract is available for this record.

Ştefan Cristian Gherghina, Daniel Armeanu, Jean Vasile Andrei, Camelia Cătălina Joldeș

No abstract is available for this record.

Perry Sadorsky, Irene Henriques

Abstract Non-fungible tokens (NFTs) are one-of-a-kind digital assets that are stored on a blockchain. Examples of NFTs include art (e.g., image, video, animation), collectables (e.g., autographs), and objects from games (e.g., weapons and poisons). NFTs provide content creators and artists a way to promote and sell their unique digital material online. NFT coins underpin the ecosystems that support NFTs and are a new and emerging asset class and, as a new and emerging asset class, NFT coins are not immune to economic uncertainty. This research seeks to address the following questions. What is the time and frequency relationship between economic uncertainty and NFT coins? Is the relationship similar across different NFT coins? As an emerging asset, do NFT coins exhibit explosive behavior and if so, what role does economic uncertainty play in their formation? Using a new Twitter-based economic uncertainty index and a related equity market uncertainty index it is found that wavelet coherence between NFT coin prices (ENJ, MANA, THETA, XTZ) and economic uncertainty or market uncertainty is strongest during the periods January 2020 to July 2020 and January 2022 to July 2022. Periods of high significance are centered around the 64-day scale. During periods of high coherence, economic and market uncertainty exhibit an out of phase relationship with NFT coin prices. Network connectedness shows that the highest connectedness occurred during 2020 and 2022 which is consistent with the findings from wavelet analysis. Infectious disease outbreaks (COVID-19), NFT coin price volatility, and Twitter-based economic uncertainty determine bubbles in NFT coin prices.

Jianjian Liu, Shuhan Wang, Lijin Xiang, Shiqun Ma · 5 authors

No abstract is available for this record.

Ahmed Bossman, Mariya Gubareva, Samuel Kwaku Agyei, Xuan Vinh Vo

The growth of digital assets in recent periods are accompanied by negative externalities which raise concerns over sustainability. This has influenced the news content on both conventional and social media outlets, leading to the creation of the index of cryptocurrency environmental attention (ICEA). Given the pivotal role of social and conventional media in forming investors’ attitudes and behavior in financial markets, we address this issue from the perspective of Islamic stocks, which by their nature represent a class of Shariah-compliant sustainable assets. With the dataset spanning from 2014 onwards up to July 2022, we analyze how the ICEA induces the market dynamics in Islamic stocks covering diverse economic sectors. By applying the bi-wavelet-based time-frequency econometric framework, our empirical findings reveal time-varying levels of coherence between the ICEA and Islamic sectoral stocks, implying that the pricing and returns-generating dynamics across various economic sectors in Islamic markets are led by media coverage on environmental attention vis-à-vis the mining and trade of cryptocurrencies. Notwithstanding, our results indicate that the real “brick-and-mortar” categories of faith-based stocks, which contains the basic materials, consumer goods, industrials, and oil & gas sectors, provide attractive diversification attributes. Our findings are important for risk, portfolio, and policy management.

Rosa Galvão, Rui Dias

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">This paper examines the efficiency, in its weak form, of the clean energy stock indices, Clean Coal Technologies, Clean Energy Fuels, and Wilderhill, as well as the cryptocurrencies classified as "dirty", due to their excessive energy consumption, such as Bitcoin (BTC), Ethereum (ETH), Ethereum Classic (ETH Classic), and Litecoin (LTC), from January 2020 to May 30, 2023. In order to meet the research objectives, the aim is to answer the following research question, namely whether: i) the events of 2020 and 2022 accentuated the persistence in the clean energy and dirty energy indices? The results show that clean energy indices such as digital currencies classified as "dirty" show autocorrelation in their returns; the prices are not independent and identically distributed (i.i.d). In conclusion, arbitrage strategies can be used to obtain abnormal returns, but caution is needed as prices can rise above their real market value and reduce trading profitability. This study contributes to the knowledge base on sustainable finance by teaching investors how to use forecasting strategies on the future values of their investments.</span></p>

Ilyes Abidi, Kamel Touhami

This paper investigates the safe haven property of Bitcoin and the main precious metals in a state of crisis. This study focuses mainly on two critical periods, namely the COVID-19 health crisis and the Russian-Ukraine conflict. To achieve this objective, we first use the DCC-GARCH model to study the dynamic correlation between the returns of oil and the main precious metals. Then, we use a bivariate specification and a Bayesian specification to estimate the TVC-VAR model. The results of this study indicate the existence of similarity between Gold and Bitcoin in hedging capabilities. In fact, both have been weak havens during the COVID-19 health crisis and strong havens during the Russian-Ukrainian war period. On the other hand, the results suggest that ruthenium and iridium yields are uncorrelated or negatively correlated with Brent yields. In this respect, investors are called upon to keep their treasury in the form of iridium and ruthenium during this period of war. Similarly, investors were required to invest in these two assets during the COVID-19 period.

Aysu Ahmadova, Taghi Guliyev, Khatai Aliyev

This paper investigates the long-run interaction between Bitcoin and Nasdaq, U.S. Dollar Index and commodities by applying weekly data from 1 January 2017 until 21 May 2023. This study uses FMOLS, DOLS and CCR methods to examine the long-run association between the variables. The results reveal a positive and significant relationship between Bitcoin and Nasdaq, as well as a similar positive association between Bitcoin and Oil prices. Notably, the U.S. Dollar Index exhibits a negative and significant impact on Bitcoin. However, results show that Gold does not have significant impact on Bitcoin. Finally, the results show that there are significant Granger causality from Nasdaq, oil and gold to Bitcoin.

Dongmei Li, Hui Guo

Background and Aim: The advent of blockchain technology has brought about a significant transformation in the realms of finance and international trade, primarily through the implementation of a decentralized ledger system for conducting transactions. The present study aims to assess the efficacy and economic advantages of employing blockchain technology in the context of international trade financing. Specifically, it focuses on the potential decrease in transaction time and cost savings that Chinese domestic banks may experience as a result of adopting this technology. Materials and Methods: This research employs a quantitative methodology to assess the efficacy of blockchain technology in the context of international trade, with a specific emphasis on banking professionals. The study utilizes a cost-benefit analysis approach to maximize advantages and minimize drawbacks. Results: The research revealed that the implementation of blockchain technology has the potential to improve operational efficiency and mitigate transaction risks. However, it is important to note that this comes at the expense of increased costs, rendering it unsuitable for widespread adoption due to its unfavorable net benefit. Conclusion: The findings of the study indicate that the use of blockchain technology leads to enhanced operational efficiency and decreased transactional risks. However, it is important to note that this implementation also entails elevated costs, rendering it impractical for widespread adoption due to its unfavorable net benefit. The report posits that the advantages of operational efficiency offered by blockchain technology are overshadowed by the accompanying expenses, thereby advocating for a prudent approach to its implementation in the realm of international trade. The recommendations encompass many strategies such as the implementation of trial projects, conducting thorough cost-benefit analyses, using hybrid techniques, ensuring ongoing monitoring, and maintaining strict adherence to legal regulations.

Chengying He, Yong Li, Tianqi Wang, Salman Ali Shah

Abstract In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven.

Maria Ijaz Baig, Elaheh Yadegaridehkordi

Abstract This study seeks to identify the determinants of economic and environmental sustainability through green supply chain management (GSCM) and explore the moderating role of blockchain adoption in the relationships between GSCM and economic and environmental sustainability. The theoretical model was developed based on a natural‐resource‐based view and stakeholder theory. The structural equation modeling‐fuzzy set qualitative comparative analysis (SEM‐fsQCA) was used to analyze the data, which were gathered from 179 organizations in Malaysia. The SEM results showed that green technology, green marketing, and customer pressure are the factors that affect GSCM and enhance economic and environmental sustainability. The fsQCA findings supported SEM results by indicating that a combination of customer pressure, green technology, green marketing, and GSCM was necessary to achieve the highest level of an organization's economic and environmental sustainability. Moreover, the assessment of the moderating effect highlighted that blockchain adoption strengthened the association between GSCM and organization economic and environmental sustainability. The findings of this study help managers and organizations understand how blockchain adoption can enhance economic and environmental sustainability.

Kübra Saka Ilgın

Purpose : It can be stated that in today’s competitive conditions, where portfolio management is very important, it has become necessary to examine the relationship between global financial assets and major cryptocurrencies, such as Bitcoin and Ethereum. This paper aims to investigate the cointegration and causalityrelationships between Bitcoin, Ethereum, and global financial assets such as gold, oil, the S&P Global 100, the Dow Jones Commodity, and the US Dollar Indices, and to determine the diversification role of Bitcoin and Ethereum comparatively for the period between April 2016 and January 2024. Methodology: The ADF Unit Root, Johansen Cointegration, Granger Causality, Rolling Window Causality tests, and Variance Decomposition Analysis methods were used in the analysis process. Results: Based on the findings obtained from the paper, it was determined that Bitcoin and Ethereum have no cointegration with selected financial asset classes. Granger causality analysis results indicated that there were unidirectional causalities from Bitcoin and Ethereum prices to Dow Jones Commodity Index prices. In addition to the results of the Rolling Window causality tests, it was also determined that there are some causalities between Bitcoin, Ethereum, and other variables, especially after the 2021-2022 period. Conclusion: It can be concluded that Bitcoin and Ethereum are effective portfolio diversifiers throughout the entire period; however, the diversification effects of Bitcoin and Ethereum weakened towards the end of the review period. Therefore, it can be said that Bitcoin and Ethereum act similarly in the global investment portfolio.

Chunyi Wang

No abstract is available for this record.

Parisa Sabbagh, Orlando Troisi, Anna Visvizi, Antonino Galati · 5 authors

No abstract is available for this record.

Lumengo Bonga‐Bonga, Muhammad Khalique

No abstract is available for this record.

Diksha Verma, Pooja Kansra, Shad Ahmad Khan

No abstract is available for this record.

Asty Khairi Inayah, Lesia Fatma Ginoga, Dahri Tanjungan, Resti Jayeng Ramadhanti · 5 authors

This study analyzes the return and volatility spillover between oil-gold and oil-Bitcoin pairs before and after the COVID-19 pandemic using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. The data used in this research consists of daily returns of oil, gold, and Bitcoin from January 2018 to December 2021 to understand volatility dynamics. The data period is divided into two phases: before and after theCOVID-19 pandemic. The analysis results show no significant volatility spillover between oil andgold. The relationship between oil and Bitcoin points to volatility spillover, although not following an identical pattern. The absence of volatility spillover indicates that markets or assets are more independent of each other. This reduces the interdependence between markets, making it more challenging to predict market movements based on the behavior of other markets.

Mortaza Ojaghlou

No abstract is available for this record.

Surachai Chancharat, Parichat Sinlapates

The shifting nature of volatility spillovers among Bitcoin, WTI oil, and the MSCI global index between two financial crises, the COVID-19 outbreak, and the Russia-Ukraine war, is examined in this paper. The BEKKGARCH model is applied to daily data for Bitcoin, crude oil, and the world stock index. Our empirical results suggest that the return spillovers for the Bitcoin-WTI, Bitcoin-MSCI, and WTI-MSCI pairings change among the four different periods. However, the volatility transmissions for the Bitcoin-WTI and Bitcoin-MSCI pairings change throughout the four study periods. These findings offer policymakers and portfolio managers useful information about risk management, forecasting, hedging, and portfolio diversification.

Hind Alnafisah, Bashar Yaser Almansour, Wajih Elabed, Ahmed Jeribi

No abstract is available for this record.

Bilgehan Teki̇n, Fatma TEMELLİ, Sadik Aden Dirir

This study examines the relations of Bitcoin (BTC) prices and fluctuations with gold, USD, oil, VIX index, hedging, and diversification features in Turkiye. For this purpose, wavelet coherence and dynamic conditional correlations (DCCs) were used in the study. Our research explores whether the bubble behavior patterns in BTC prices during the COVID-19 pandemic can be used in the short term to protect against the bubble behavior in the markets that are the subject of this research and vice versa. However, whether other assets can be used to manage and hedge BTC's downside risk is also being explored. The aim is to understand how and at what level critical financial instruments and indicators are affected by each other in times of crisis and economic recession, such as pandemics, and to present valuable results to decision-makers. The sample for this study includes Türkiye for the period between 12/31/2019 and 13/07/2022. Wavelet Coherence and DCC-GARCH results indicate significant positive and negative movements of BTC prices with gold, oil, USD prices, and the VIX fear index during the pandemic. We find evidence of volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators.

Wei Wang, Haibo Wang

This study aims to examine the intricate dynamics between BRICS traditional stock assets and the evolving landscape of cryptocurrencies. Using a time-varying parameter vector autoregression model (TVP-VAR), we have analyzed data from the BRICS stock market index, cryptocurrencies, and indicators from January 6, 2015, to June 29, 2023. The results show that three out of the five BRICS stock markets serve as primary sources of shocks that subsequently affect the financial network. The transcontinental (TCI) value derived from the dynamic conditional connectedness using the TVP-VAR model demonstrates a higher explanatory power than the static connectedness observed using the standard VAR model. The discoveries from this study offer valuable insights for corporations, investors, and regulators concerning systematic risk and investment strategies.

Xiyong Dong, Zhuhua Jiang, Seong‐Min Yoon

No abstract is available for this record.

Adela Bârã, Irina Georgescu, Simona‐Vasilica Oprea, Marian Pompiliu Cristescu

In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further consider the West Texas Intermediate (WTI) and Dubai oil prices that are the dominant in the U.S and Asian market. The investigation identifies the TGARCH(1,1) Skewed Student model as the optimal choice among 9 models considered for VaR estimation. The results show that TGARCH Skewed Student model surpasses the other models in the study, proving its superiority in forecasting Brent crude oil price volatility and facilitating VaR estimation. A VaR of 0.044 with a 95% confidence level means that there is a 95% chance that the portfolio will not lose more than 4.4% of its value. By incorporating skewness in addition to volatility asymmetry, the Skewed GARCH-type models provide a more realistic representation of the underlying return distribution. Furthermore, the most appropriate GARCH-type model for WTI crude oil is EGARCH(1,1) Skewed Student, with a VaR coverage of 0.39. The most appropriate GARCH-type model for Dubai crude oil is TGARCH(1,1) Skewed Student, with a VaR coverage of 0.17. Both WTI oil and Dubai crude oil have a coverage that exceeds 5%, implying a more conservative approach to estimating potential losses. Furthermore, the unidirectional causalities BTCUSD→BRENT and BTCUSD→S&P500 are identified. The results of the current research have practical implications for both importing and exporting countries, policy makers and investors. For companies in the oil sector, VaR informs operational decisions, such as production levels, capital expenditure and inventory management, by providing insights into market risk. Moreover, understanding the risks associated with oil aids in long-term strategic planning.