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.
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.
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.
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.
Investment in cryptocurrencies has garnered substantial attention in the recent past as the prices for these digital currencies started recording all-time highs. While there are numerous contenders in the cryptocurrency market, bitcoin has emerged to be the most popular and sought after digital currency. Despite its popularity, the theoretical understanding of the value of this cryptocurrency is still limited. Hence this study aims to find out the significant predictors of the bitcoin price and build a machine-learning based model to evaluate and predict the complex phenomenon of bitcoin price. Here we contribute to the extant literature by searching for the potential contributors of bitcoin prices ranging from fundamental, macroeconomic, financial, speculative, and technical sources to the most marked event of 2020 i.e., Covid19 pandemic. For this purpose, we have used state-of-the-art machine learning, deep learning, and statistical time-series models (univariate and multivariate) to forecast bitcoin price. The study revealed that deep learning models performed almost at par with Random Forest model for both pre- and whilst-Covid19 era. Traditional time-series models, namely VAR and VECM gave the most consistent performance within acceptable margins for both pre- and whilst-Covid era. We have also found that macroeconomic factors play an important role in determining bitcoin price formulation process during both periods, while mining difficulty and market sentiment factors gain more importance during pre-Covid period. In addition, number of covid cases is also found to be a significant factor for the prediction of bitcoin price during whilst-Covid period.
The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold ARCH (TARCH) models with long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and multivariate Bi-LSTM models. Model effectiveness is evaluated by means of root mean squared error (RMSE) and root mean squared percentage error (RMSPE) scores. The multivariate Bi-LSTM model emerges as mostly effective, achieving an RMSE score of 0.0425 and an RMSPE score of 0.1106. This comparative scrutiny contributes to understanding the dynamics of Bitcoin volatility prediction, offering insights that can inform investment strategies and risk management practices in this quickly changing environment of finance.
Muhammad Abubakr Naeem, Nadia Arfaoui, Larisa Yarovaya
Artificial Intelligence (AI) stands as a transformative force across business, technology, and science, yet its comprehensive impact on innovative industries remains relatively unexplored. This study delves into the interconnectedness between AI and pivotal sectors such as cryptocurrency , blockchain, metaverse, democratized banking, and Cleantech, among others. Employing the conditional autoregressive value-at-risk (CAViaR) and time-varying parameters vector autoregressions (TVP-VAR) methods, we scrutinize daily data spanning from June 1, 2018, to October 11, 2023, encompassing 12 stock indices representing each industry. Our findings unveil a strong contagion effect from AI to other innovative sectors, with the exception of Cleantech, which appears to have decoupled from the AI surge. Notably, democratized banking and the metaverse emerge as key recipients of this contagion. Examination of tail-risk spillovers highlights AI as one of the most influential risk transmitters during market tumult, while cryptocurrency and blockchain consistently function as net risk receivers throughout the sample period. The implications of these findings are multifaceted, offering substantive insights into the risk profiles of these critical innovative sectors. Investors and regulatory bodies stand to benefit significantly from this analysis, as it illuminates potential avenues for portfolio diversification and deepens understanding of contagion mechanisms within these evolving industries.
Abstract This study examines the spillover of Bitcoin's jumps and diffusive variations to traditional assets using high‐frequency data. For our cross‐asset analysis, we detect positive spillovers from Bitcoin to risk assets and negative spillovers to defensive assets. We also find evidence of positive jump and diffusion spillovers from Bitcoin to U.S. equity sectors, particularly the financials, technology, consumer discretionary, and communication services sectors. By examining the source of these risk transmissions, we show that these spillovers are exacerbated by increased economic exposures to blockchain and cryptocurrency technologies by U.S. companies. The empirical findings reveal that the price fluctuations of an unregulated asset such as Bitcoin can materially affect the price dynamics of regulated assets.
Agoestina Mappadang, Bayu Adi Nugroho, Setyani Dwi Lestari, Elizabeth Elizabeth · 5 authors
A significant amount of historical returns is needed for the generalized autoregressive conditional heteroscedasticity (GARCH) models to be calibrated. Newer cryptocurrencies, such as non-fungible tokens (NFTs), have relatively limited data to create robust parameter estimates. This study uses a newly developed method, the exponentially weighted moving average (EWMA) model, that takes into account the fat-tailed distributions of returns and volatility response to forecast Value-at-Risk (VaR) and Expected Shortfall (ES). We employ thorough back tests of daily VaR and ES forecasts, which are widely utilized for regulatory approval and are considered to be industry standards. We also use loss function ratios to select the best model. Our results indicate that simpler models are just as good as the complicated ones, provided the simpler models capture fat-tailed distributions of returns. The primary findings hold up through several tests.
The “gas fee” paid for inclusion in the blockchain is analyzed in two parts. First, we consider how “effort” in terms of resources required to process and store a transaction turns into a “gas limit,” which, through a fee comprised of the “base” and “priority fee” in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering “multidimensional gas.” Second, we assume that the “gas” price is given deus ex machina by a fractional Ornstein–Uhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade “forwards” in addition to the existing “spot” inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this article offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.
Abraham Itzhak Weinberg, Pythagoras Petratos, Alessio Faccia
Abstract This paper explores the coexistence possibilities of Central Bank Digital Currencies (CBDCs) and blockchain-based cryptocurrencies within a post-quantum computing landscape. It examines the implications of emerging quantum algorithms and cryptographic techniques such as Multi-Party Computation (MPC) and Oblivious Transfer (OT). While exploring how CBDCs and cryptocurrencies might integrate defenses like post-quantum cryptography, it highlights the substantial hurdles in transitioning legacy systems and fostering widespread adoption of new standards. The paper includes comprehensive evaluations of CBDCs in a quantum context. It also features comparisons to alternative cryptocurrency models. Additionally, the paper provides insightful analyses of pertinent quantum methodologies. Examinations of interfaces between these methods and blockchain architectures are also included. The paper carries out considered appraisals of quantum threats and their relevance for cryptocurrency schemes. Furthermore, it features discussions of the influence of anticipated advances in quantum computing on algorithms and their applications. The paper renders the judicious conclusion that long-term coexistence is viable provided challenges are constructively addressed through ongoing collaborative efforts to validate solutions and guide evolving policies.
This article assesses the temporal and dynamic interconnectedness of cryptocurrency, gold, energy, and stock markets, essential for portfolio diversification. Using a TVP-VAR model, we analyze the return and realized volatility from November 11, 2013, to August 22, 2022. The study focuses on Bitcoin, gold, and renewable energy dynamics. Findings show that volatility shocks are most significant in the crude oil market, while Bitcoin's relationship with other assets is weak during non-crisis periods. Gold and Bitcoin's connection is less pronounced during crises. These results provide insights for portfolio optimization in both crisis and non-crisis periods.
This study uses a hybrid model of the exponential generalised auto-regressive conditional heteroscedasticity (eGARCH)-extreme value theory (EVT)-Gumbel copula model to investigate the dependence structure between Bitcoin and the South African Rand, and quantify the portfolio risk of an equally weighted portfolio. The Gumbel copula, an extreme value copula, is preferred due to its versatile ability to capture various tail dependence structures. To model marginals, firstly, the eGARCH(1, 1) model is fitted to the growth rate data. Secondly, a mixture model featuring the generalised Pareto distribution (GPD) and the Gaussian kernel is fitted to the standardised residuals from an eGARCH(1, 1) model. The GPD is fitted to the tails while the Gaussian kernel is used in the central parts of the data set. The Gumbel copula parameter is estimated to be α=1.007, implying that the two currencies are independent. At 90%, 95%, and 99% levels of confidence, the portfolio’s diversification effects (DE) quantities using value at risk (VaR) and expected shortfall (ES) show that there is evidence of a reduction in losses (diversification benefits) in the portfolio compared to the risk of the simple sum of single assets. These results can be used by fund managers, risk practitioners, and investors to decide on diversification strategies that reduce their risk exposure.
Barbara Będowska-Sójka, Piotr Wójcik, Sabrina Giordano
Abstract This study aims to explore the dependencies on the cryptocurrency market using social network tools. We focus on the correlations observed in the cryptocurrency returns. Based on the sample of cryptocurrencies listed between January 2015 and December 2022 we examine which cryptos are central to the overall market and how often major players change. Static network analysis based on the whole sample shows that the network consists of several communities strongly connected and central, as well as a few that are disconnected and peripheral. Such a structure of the network implies high systemic risk. The day-by-day snapshots show that the network evolves rapidly. We construct the ranking of major cryptos based on centrality measures utilizing the TOPSIS method. We find that when single measures are considered, Bitcoin seems to have lost its first-mover advantage in late 2016. However, in the overall ranking, it still appears among the top positions. The collapse of any of the cryptocurrencies from the top of the rankings poses a serious threat to the entire market.
The acceleration of the globalization process and the structural changes in technology that emerged in the 2000s have affected financial markets. This interaction in the financial markets has made the emergence of new financial assets necessary. According to the ARDL boundary test results, there is no significant relationship between cryptocurrency markets and stock returns in both the long and short term for the UK financial markets. For the German financial markets, it has been determined that there is a significant and positive long-term relationship between the cryptocurrency market assets Bitcoin and Tether and stock market returns. In the short term, no significant relationship has been detected. For the long term in the U.S. financial markets, it has been determined that there is a significant and positive relationship between Bitcoin, a cryptocurrency market asset, and stock market returns, while there is no significant relationship between Ethereum and Tether with stock market returns. In the short term, no significant relationship has been detected. These findings offer significant implications for policymakers, investors, and market analysts.