Iulia Cristina Iuga, Raluca Andreea Nerişanu, Larisa-Loredana Dragolea
This study explores the volatility spillover effects between clean and dirty cryptocurrencies and key financial indices, specifically focusing on Green Finance Indices (such as solar, wind, and nuclear) and Economic Indices (like the Baltic Dry Index and CRB Index). Employing the diagonal BEKK model and the DCC GARCH model, the study spans data from February 17, 2020, to September 30, 2024, to analyze how cryptocurrencies, categorized by their environmental impact, influence these indices. The results reveal significant volatility spillovers from both clean and dirty cryptocurrencies, with clean cryptocurrencies such as Cardano showing a stabilizing effect, while dirty cryptocurrencies like Bitcoin exhibit more pronounced and asymmetric volatility impacts on green finance indices. Furthermore, the persistent correlations identified through the DCC GARCH model highlight the dynamic relationships between cryptocurrency markets and green finance, suggesting that shocks in cryptocurrency volatility can significantly affect the financial dynamics of renewable energy investments. These insights are valuable for portfolio diversification and risk management, indicating that certain cryptocurrencies may serve as effective hedging instruments against risks in green finance. This study contributes to a deeper understanding of the interaction between digital financial assets and sustainable investments, offering practical implications for investors, financial managers, and policymakers committed to achieving Sustainable Development Goals (SDGs).
The aim of this article is to examine the reasons why cryptocurrency volatility hinders its potential to replace fiat money as legal tender. We focus on Bitcoin and Ethereum for this analysis. By applying an augmented Dickey-Fuller stationarity test, we demonstrate that cryptocurrencies lack a long-term trend; instead, their movement is erratic and highly volatile. Furthermore, eGARCH models indicate that volatility tends to decrease and is expected to persist in this pattern. In summary, theoretical and empirical analysis suggests that, due to their nature based solely on supply and demand and their high volatility, cryptocurrencies are not suitable as primary investment instruments or stores of value.
Bitcoin has received a lot of attention from both investors and analysts, as it forms the highest market capitalization in the cryptocurrency market. The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely observed in the empirical literature. In this paper, we consider an alternative approach involving nonparametric method to model and forecast Bitcoin return volatility. We show that the out-of-sample volatility forecast of the nonparametric GARCH model yields superior performance relative to an extensive class of parametric GARCH models. The improvement in forecasting accuracy of Bitcoin return volatility based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to the commonly used parametric GARCH models.
Li Yi Thong, Ricky Chee Jiun Chia, Mohd Fahmi Ghazali
Research Question: Does uncertainty indices have impact on cryptocurrency? Motivation: Most of the previous study investigate the impact of geopolitical risk and economic policy uncertainty on Bitcoin only and less research investigate the long run and short run relationship between the uncertainty indices and cryptocurrency. Hence, this study investigates whether the economic policy uncertainty, geopolitical risk and US equity market uncertainty have an impact on Bitcoin, Ethereum and Binance Coin by the multivariate VAR Granger non-causality. Idea: This study applied three different uncertainty indices (geopolitical risk, economic policy uncertainty and US equity market uncertainty) and top three ranking cryptocurrency (Bitcoin, Ethereum and Binance Coin) to investigate and compare the impact of uncertainty indices on cryptocurrency with different uncertainty conditions and applied top three ranking cryptocurrency in cryptocurrency market to reinforce the result. Data: This study applied monthly data with 42 observations which cover the period of December 2017 until May 2021 and data for cryptocurrency extracted from investing.com, while the uncertainty indices from policyuncertainty.com. Method/Tools: This study utilize multivariate VAR Granger non-causality to examine the cointegration relationship between the cryptocurrency and uncertainty indices. Findings: The results show that the economic policy uncertainty, geopolitical risk and US equity market uncertainty cointegrated with Bitcoin, while Binance Coin cointegrated with geopolitical risk only. Hence, the economic policy uncertainty, geopolitical risk and US equity market uncertainty plays a vital role in the Bitcoin prediction and geopolitical risk plays an important role to forecast the Binance Coin. Contributions: The Bitcoin investors may focus on the changes in economic policy uncertainty, geopolitical risk and US equity market uncertainty to predict the Bitcoin return, and Binance Coin investors focus on the geopolitical risk.
This study examines the influence of cryptocurrency's environmental footprint on market behavior through an analysis of 66,582 Reddit posts about Bitcoin and 23,231 about Ethereum. Using a vector autoregression (VAR) model, it explores the relationship between social media discussions on environmental issues, electricity use, and cryptocurrencies' market dynamics. We find a negative correlation between environmental discussions and Bitcoin volatility. Moreover, real electricity use has a more pronounced impact than social media discussions on both Bitcoin and Ethereum volatility. This indicates that crypto market investors prioritize real-world indicators over information from social media discussions. The study also reveals a bidirectional relationship between Bitcoin volatility and environmental posts, highlighting the complex interplay between market behavior and public discourse on environmental matters in the cryptocurrency domain. These results suggest the need for policies that limit energy consumption due to mining, promote renewable energy, and enhance investor education on environmental impacts to support sustainable practices in the cryptocurrency market.
Abstract The proliferation of cryptocurrencies has brought significant changes in the global economic market while introducing new risks to national security. This paper explores the economic transformations driven by the rise of digital currencies, analyzing their impact on traditional financial systems, monetary policy, and international trade. While cryptocurrencies offer opportunities for innovation and economic growth, they also pose substantial challenges for regulators, particularly in addressing illicit activities such as money laundering, terrorism financing, and tax evasion. Furthermore, the decentralized nature of these digital assets presents unique vulnerabilities for national security, as they can be used to avoid financial controls and sanctions. This paper aims to provide a comprehensive analysis of the economic benefits and security risks associated with cryptocurrencies, emphasizing the need for coordinated regulatory frameworks. By examining the intersection of technological innovation, economic impact, and security concerns, this research contributes to the ongoing debate on how to handle the main challenges brought by the growth of cryptocurrencies in a globalized, digital economy.
The sudden volatility in cryptocurrency prices, especially Dogecoin and Bitcoin, owed to Elon Musk's public statements during COVID-19 has triggered a debate to study the impact of Musk’s endorsement on cryptocurrencies and examine the hedging capabilities and leverage effect on cryptocurrencies during uncertainties. Observation of the market capitalization of Bitcoin and Dogecoin shows that the price of these cryptocurrencies is disturbed due to positive and negative comments by Musk and other public icons. Therefore, these cryptocurrencies are often looked at with suspicion by participants in the cryptocurrency market. This research aims to analyze the impact of favorable and unfavorable Musk’s remarks on Bitcoin and Dogecoin and further examine the hedging capabilities and leverage effect of Dogecoin and Bitcoin against stocks, gold, Treasury yields, the Euro, and the Pound exchange rate, particularly during the COVID-19 pandemic. The research collects daily observations from Jan 2018 to Dec 2022 from Yahoo Finance, yielding 1226 observations, and uses statistical tests to analyze the significance of Musk's tweets on cryptocurrencies. Further, this research applies the GARCH model to understand the impact of Musk's remarks on the hedging capabilities and leverage effect on Dogecoin and Bitcoin during COVID-19. The findings indicate that Musk's comments had no lasting impact on cryptocurrency prices. However, his unfavorable remarks significantly affected Bitcoin's and Dogecoin's hedging capabilities during the pandemic. The study also revealed a pronounced leverage effect in Dogecoin, contrasting with a moderate impact on Bitcoin. Dogecoin strongly responded to positive news or Musk’s favorable tweets, while Musk’s unfavorable tweets influenced Bitcoin's leverage effect. The study suggested the importance of information in the cryptocurrency market. The study also focused on the significance of long-term perspectives and correlations between traditional assets like stocks and cryptocurrency yields, which can be instrumental in guiding investment decisions and aiding in risk management during uncertainties.
Mohamad Hassan Shahrour, Ryan Lemand, Mathis Mourey
Purpose This paper examines the volatility spillover effects from traditional financial assets to cryptocurrency markets and vice versa. It aims to provide insights into the dynamic interconnectedness of these markets. Design/methodology/approach This paper employs the time-varying parameter vector autoregression technique to examine the volatility spillover among the crypto markets (across leading cryptocurrencies such as Bitcoin (BTC), USD Tether, NEAR Protocol (NEAR), Immutable and Dogecoin) and traditional financial instruments (Treasury Bills (TBILL) and Volatility Index). Findings The results reveal significant bidirectional volatility spillovers between cryptocurrencies and traditional financial assets. NEAR and BTC act as a major transmitter of volatility, both influencing others significantly (71.63 and 68.17%, respectively) and being influenced by others (54.74 and 62.3%, respectively). TBILL and Grayscale Bitcoin Trust ETF are the largest net receivers of volatility, indicating a higher dependency on other assets’ volatility. Practical implications Understanding the volatility spillover dynamics can aid investors in portfolio diversification and risk management. The findings provide actionable insights for constructing portfolios that include both cryptocurrencies and traditional financial assets, allowing for more informed investment decisions under volatile market conditions. Originality/value This paper contributes to the literature by analyzing volatility spillovers among traditional financial markets and various major cryptocurrencies. It offers a framework for assessing how shocks in one market or cryptocurrency can propagate to others, thereby enhancing the understanding of interconnectedness between markets. This understanding improves our ability to risk manage modern portfolios, which increasingly include significant alternative assets like cryptocurrencies.
The study focuses on the safe-haven and hedging properties of gold and selected cryptocurrencies against stock markets' extreme risk observed during the COVID-19 pandemic and the Russian invasion of Ukraine. The loss reduction is compared with the profit sacrifice obtained through hedging in terms of the tail thickness of the return distribution. The findings show that gold is able to reduce extreme losses more intensively than extreme profits. Tether reduces volatility and tail risk the most effectively but it is characterised by the worst profit/risk ratio. Bitcoin and Ether increase investment risk; thus, they fail to act as an effective hedge or a safe haven. On the other hand, these cryptocurrencies added to the stock portfolio increase the probability of extreme profits more than extreme losses. The paper provides new insights into the benefits of safe-haven or hedging strategies.
Block Chain Cryptocurrencies are playing vital role in Financial as well in all other sectors .Recently in the year 2022 battle between Russo-Ukrainian is in fact enduring battle between two countries Russia and Ukraine. Subsequent to the Russian military build-up on the Russia–Ukraine border from late 2021, the battle extended ominously when Russia propelled a complete incursion of Ukraine on 24 February 2022.Monetary problem obviously showcases a foremost role in wars, the 2022 war between Russia and Ukraine is the prime major battle with a major but role of crypto-currencies. Because Russian military forces attacked Ukraine the United States along with its partners have imposed exceptional sanctions on Russia. These situations have led to lot of queries, regarding whether crypto-currencies can be employed by Russian performers to circumvent the authorizations. In a broader sense, the Russia-Ukraine crisis has made the policymakers to resolve how to normalize digital possessions. This chapter emphasizes on how best Ukraine is able to manage the financial crisis during Ukraine –Russia war using crypto-currencies and Non-fungible tokens in terms of Military and humanity.
Purpose This paper aims to investigate the impact of the COVID-19 pandemic and the Russian−Ukrainian war on the volatility of several cryptocurrencies. Design/methodology/approach To do this, the study uses the GJR-GARCH and dynamic conditional correlation (DCC)-GJR-GARCH models, which allow the author to estimate the conditional variance of the cryptocurrencies’ returns and assess their dependence structure over time. Findings The results show that the health crisis had a negative impact on all cryptocurrencies studied, except for Bitcoin, which experienced a positive impact. Additionally, the study finds that the Russian-Ukrainian war had a mixed impact on the cryptocurrencies studied, with some experiencing positive impacts (BNB, Dogecoin, Ethereum and Tether) and others experiencing negative impacts (Bitcoin, BUSD, Coin and XRP). Moreover, the author analyzes the spillover effects among the cryptocurrencies and observe significant interdependence during the periods under study. Originality/value Finally, the study discusses the implications of the findings for investors, policymakers and regulators, highlighting the importance of considering external factors when making investment decisions or designing regulatory frameworks for the cryptocurrency market.
Machine learning techniques have emerged as potential tools in the field of extensive research led by the growing interest in predicting the future price of Ethereum. This paper fills a major knowledge gap in the area by reviewing and analysing important literature on Ethereum price forecasting, with a focus on Ethereum and it is applicable on other cryptocurrencies as well. By using machine learning models, such as random forest and linear regression, this study fills the gap by comparing the models' ability to predict Ethereum prices properly and provides insightful information for researchers and investors. The implications of these results for the analysis of the cryptocurrency market are noteworthy, as they may reduce the risks associated with the erratic cryptocurrency market and open the door for more studies to improve prediction techniques in this ever-changing environment. The study emphasises flexibility and effectiveness in navigating complicated cryptocurrency marketplaces, which advances the understanding of machine learning applications in Ethereum price forecasting.
Venator Santiago, Michel Charifzadeh, Tim Alexander Herberger
Purpose This study aims to investigate the impact of the 2022 collapse of the Terra-Luna ecosystem on volatility correlations among digital assets, including U.S. Terra, Luna, Bitcoin, Ether, a Decentralized Finance index and U.S.-sourced conventional assets stocks, bonds, oil, gold and the dollar index. The primary research question addresses whether correlations increased between digital and conventional assets during the collapse. Design/methodology/approach A dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model was used to examine changes in volatility correlations during the market crash. Specifically, a data set of 1,442 close prices from 30-minute interval candles of digital and conventional asset prices are considered to provide a granular view of market dynamics during the sample period from January 3rd, 2022, to May 31st, 2022, including the crash event. Findings While the dynamic conditional correlation plots of the model indicate increased volatility, the results do not offer sufficient evidence to confirm an increase in correlations between digital and conventional assets during the Terra-Luna downfall. Furthermore, the authors confirm Bitcoin’s role as a diversifier with oil and observe the dollar index maintaining a negative correlation with Bitcoin during the crash, supporting Bitcoin’s function as a hedge against the U.S. dollar. However, the findings during the crash diverge from previous studies, reflecting shifts in correlation patterns in broader market downturns. Specifically, the authors identify the need for adaptive capital allocation strategies, as gold’s oscillation during the period suggests it may not serve as an effective hedge during black swan events. Practical implications The findings provide insights for investors, financial institutions and regulators to improve risk management, portfolio diversification, trading strategies and the formulation of consumer protection regulations. In addition, the results underscore the challenges of mitigating risks beyond regulatory measures and emphasize the importance of exercising caution for investors. Originality/value This study addresses the research gap in changes between conventional and digital asset volatility correlations during collapses in the digital asset space.
• A recently developed advanced stochastic volatility modeling is utilized for cryptocurrency volatility analysis. • The suggested Bayesian Markov Chain Monte Carlo (MCMC) sampling approach proves to be effective. • The modeling accurately captures the dynamics of stochastic volatility. • We incorporate the market risk method within the Basel IV regulations. We apply stochastic volatility modeling enriched with leverage and an asymmetrically heavy-tailed distribution to analyze the returns of Bitcoin and Ethereum. Our methodology leverages the generalized hyperbolic skew Student’s t-distribution (GH-ASV-skw-st) framework, as proposed by Nakajima and Omori (2012), employing a Bayesian Markov chain Monte Carlo (MCMC) sampling technique for effectiveness evaluation. The GH-ASV-skw-st model is demonstrated to adeptly capture the stochastic volatility patterns present in the returns of cryptocurrencies. After validation with several diagnostics and robustness checks, we illustrate the model’s suitability for high-volatility series by capturing asymmetry, leverage effects, and tail risk. Our findings indicate that the model fits the data more precisely than traditional models and provides a more reliable foundation for risk measures essential to portfolio management, such as Value at Risk (VaR) and Expected Shortfall (ES).
In order to maintain the value of the national currency and control foreign debt, central banks are vital to the management of a nation’s foreign exchange reserves. These reserves, however, are vulnerable to a variety of hazards, including as money laundering, fraud, theft, and cyberattacks. These are issues that traditional financial systems frequently face because of their vulnerabilities and inefficiency. Using modern innovations in a blockchain-based solution can help tackle these serious issues. To protect data privacy, the Microsoft SEAL library is utilized for homomorphic encryption (FHE). For the development of smart contracts, Solidity is employed within the Ethereum blockchain ecosystem. Additionally, Amazon Web Services (AWS) is leveraged to provide a scalable and powerful infrastructure to support our solution. To guarantee safe and effective transaction validation, our method incorporates a hybrid consensus process that combines Proof of Authority (PoA) with Byzantine Fault Tolerance (BFT). The administration of foreign exchange reserves by central banks is made more secure, transparent, and operationally efficient by this all-inclusive approach.
V. SimhadriAppanna, M. Manohara, B.V. Sai Thrinath, D. Leela Rani · 6 authors
The proliferation of mobile devices and personal computing has revolutionized stock and crypto currency trading. While many struggle with navigating trading intricacies, adept practitioners find lucrative opportunities for wealth accumulation. Automated price prediction systems, particularly the Long Short-term Memory (LSTM) model, offer passive trading approaches, eliminating exhaustive decision-making processes. Acquiring and organizing data, followed by rigorous calculations and analysis, culminates in accurate price forecasts. Though not infallible, these models discern trends and project crypto currency trajectories. Notably, Bitcoin serves as a prime example. These systems offer invaluable insights, aiding investors in strategic decision-making amid the dynamic crypto currency landscape.
Cryptocurrency trading has gained significant adhesion in financial markets, making it essential to understand the factors influencing trading intentions. This study investigates the psychological and knowledge-based determinants of trading intentions towards Beldex coins among crypto traders in India. This study aims to evaluate how risk management, hedonic motivation, investment desire, market knowledge, peer participation, and earning desires impact trading intentions. A survey was conducted with 369 crypto traders in India, and multiple regression analysis was employed to analyze the data. The results indicate that all six factors significantly influence trading intentions, with risk management (β = 0.342, p < 0.001) and earning desires (β = 0.378, p < 0.001) having the strongest impact on Indian Cryptocurrency market arbitrage. The regression model explained 53% of the variance in trading intentions (R² = 0.53). Cryptocurrency market information is analyzed through the CoinGecko tool that provides charts, market capitalization, and blockchain data; multiple regression analysis is utilized to test the hypothesized relationships. This study reveals that traders’ investment decisions in cryptocurrencies are primarily driven by financial motivations, including potential high returns, diversification, and inflation hedging, as well as technological factors of decentralized finance, blockchain technology, and digitalized transactions. AcknowledgmentThe authors would like to convey their gratitude to Prof. Balakumar Pitchai, Director/Research, Training & Publications at the Office of Research & Development, Periyar Maniammai Institute of Science & Technology (Deemed to be University), India for his suggestions to improve the language of the manuscript.