Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causalânoncausal invertibleânoninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMAâGARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewnessâcommon in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news (Îą) while overstating volatility persistence (β). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics.
Abstract Since its introduction as a decentralized digital currency for peer-to-peer transactions, Bitcoinâs role in financial markets has undergone significant evolution. We employ bibliometric analysis to explore research trends in Bitcoin, identifying two primary perspectives in the recent financial economic literature: Bitcoin as a speculative asset and as a safe-haven asset. The speculative nature of Bitcoin is evident through its high volatility and frequent price jumps, largely influenced by rapid shifts in investor sentiment and attention, which create both risks and opportunities for traders. Conversely, Bitcoin exhibits characteristics of a safe-haven asset due to its asymmetric tail dependence and negative correlation within certain asset classes.
The cryptocurrency market, known for its inherent volatility, has been significantly influenced by external shocks, particularly during periods of global crises such as the COVID-19 pandemic and the RussiaâUkraine war. This study investigates the volatility of the top seven cryptocurrencies by market capitalizationâBitcoin (BTC), Ethereum (ETH), Tether (USDT), Binance Coin (BNB), USD Coin (USDC), XRP, and Cardano (ADA)âfrom 1 January 2020 to 1 September 2024, employing a range of GARCH models (GARCH, EGARCH, TGARCH, and DCC-GARCH). This research aims to examine the persistence of leverage effects, volatility asymmetry, and the impact of past price fluctuations on future volatility, with a particular focus on how these dynamics were shaped by the pandemic and geopolitical tensions. The findings reveal that past price fluctuations had a limited impact on future volatility for most cryptocurrencies, although leverage effects became evident during market anomalies. Stablecoins (USDC and USDT) showed a distinct volatility pattern, reflecting their peg to the US Dollar, while platform-associated BNB demonstrated unique volatility characteristics. The results underscore the marketâs sensitivity to price movements, highlighting the varying reactions of investor profiles across different cryptocurrencies. These insights contribute to understanding volatility transmission within the cryptocurrency market during times of crisis and offer important implications for market participants, particularly in the context of risk management strategies.
Abstract During the last years, financial market contagion has become a critical concern for policymakers and investors, particularly with respect to the financial stability of cryptocurrency platforms. This paper explores the contagion effect among crypto exchanges employing the SusceptibleâInfectedâRecovered (SIR) model with time delay and investigates possible cooperative strategies. The SIR dynamical system is integrated with the replicator equation of evolutionary game theory to study the interplay between the spread of risk and the propensity of cryptocurrency platforms to become cooperative under the pressure of financial contagion. Different equilibrium points which correspond to both pure and mixed cooperative strategies characterize the resulting model. We carry out a theoretical analysis of the problem by studying the asymptotic behavior in the steady state. In addition, using extensive cryptocurrency market data from 2017 to 2023, we identify the key factors driving contagion and assess the dynamics of cooperative versus non-cooperative behavior. Our findings point out that cooperative strategies are essential to ensure financial stability, particularly in the long term, as they mitigate systemic risks and foster resilience. These results provide critical insights for policy makers and investors, offering actionable strategies to enhance the robustness of crypto markets and address the growing challenges of financial contagion in the digital asset ecosystem.
Abstract This paper examines the dependence, systemic risk spillover, return and volatility spillover, and portfolio implications across various timescales between the Green Bond (GB) and U.S. S&P 500 Stock (SP), Vanguard Total World Stock Index Fund (VT), Bitcoin (BTC), Ethereum (ETH), Ripple, OIL, and GOLD markets. The sample period is August 07, 2015âOctober 6, 2023, covering periods of instability during the COVID-19 pandemic and the RussiaâUkraine conflict. Using the waveletâcopulaâconditional value-at-risk and wavelet-multivariate asymmetric-GARCH framework, our main results show that the systemic risk and return, volatility spillovers, and diversification opportunities are portfolio-specific and timescale-dependent. Specifically, there is a negative long-term correlation for the pairs GB-SP and GB-OIL, whereas the pair GBâGOLD pair is positively correlated in the short term. GB can mitigate the risk of other markets. In terms of the portfolio implications, GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term. Regarding hedging effectiveness, the role of GB for GOLD and VT is noted.
Inzamam Ul Haq, Muhammad Abubakr Naeem, Chunhui Huo, Walid Bakry
This study examines the interlinkages among diverse cryptocurrency classes and their multiscale relationship with media climate change concerns to examine how cryptocurrency returns respond to rising climate change concerns. The analysis includes 11 cryptocurrencies classified as dirty, gold-backed, energy, and sustainable and their behavior regarding media climate change concerns, including transition and physical risks. Using squared wavelet coherence and partial wavelet coherence (PWC) on daily data from January 1, 2014 to June 29, 2024, this study shows time-frequency-dependent market integration among cryptocurrency pairs. During rising climate change concerns, returns decrease for some cryptocurrencies while increasing for XRP, implying higher investors' trust in sustainable cryptocurrencies. PWC analysis reveals significant influence of climate change concerns on pairwise returns connectedness among various cryptocurrency classes. This study highlights the need for cryptocurrency traders to incorporate media climate change information into their investment decisions, contributing insights into using diverse crypto-assets for risk management. ⢠We find high market integration after 2018 cryptocurrency crash. ⢠PLG and gold-backed cryptos show weak dependence with respective cryptocurrencies. ⢠Rising climate change concerns significantly increase PLG and XRP returns across time-frequency. ⢠We find that transition risks predict cryptocurrency returns more than physical risks. ⢠We find that climate change concerns drive cryptocurrency co-movements.
Adi Wolfson, Gerard Khaladjan, Yotam Lurie, Shlomo Mark
Cryptocurrencies are decentralized digital financial services that do not physically exist in the world of tangible products and goods, and therefore purportedly offer some positive environmental sustainability features. However, since they are based on blockchain technology, which requires a relatively large input of energy, their climatic impact is not benign. Furthermore, they are very volatile and characterized by low levels of transparency and control, thus creating some negative economic and social sustainability effects. Stablecoins, which are a pegged type of cryptocurrency, exhibit much less volatility and have higher levels of management and interoperability. This raises the following question: are stablecoins more sustainable compared to other cryptocurrencies? To explore this, a sustainability assessment was conducted, comparing cryptocurrencies and stablecoins across environmental, social, and economic dimensions while identifying the key characteristics of sustainability. It was found that stablecoins can mitigate the economic and social risks associated with cryptocurrencies and thus increase their overall sustainability. Moreover, since stablecoins are managed and governed to a greater extent, a key consideration in their development is the selection and implementation of more appropriate mechanisms that can reduce energy use and enhance sustainability. Finally, stablecoins offer more effectiveâand not just more efficientâsolutions, based on value co-creation between several providers and a customer.
We examine if the day-of-the-week effect is present in Bitcoin return series. The model specification in use accounts for conditional heteroscedasticity, which is captured in the form of a stochastic volatility process that allows for periodic time-varying parameters. We find periodicity in Bitcoin returns, which is evidence against the market efficiency of Bitcoin.
Sami Ben Jabeur, Zouhaier Dhifaoui, Yassine Bakkar, Houssein Ballouk
This study provides evidence on the role of the quality of political signals in predicting six major cryptocurrency asset classes. Including communications from the U.S. presidential election in 2024, we find that political news affects cryptocurrency returns in the short-term (from ⟠2 to ⟠4 months). For most cryptoassets, text sentiment measures demonstrate superior predictive performance compared to historical cryptocurrency time series in out-of-sample forecasts. ⢠Analyzes the effect of political signals on cryptocurrency returns. ⢠Political news influences cryptocurrency returns in the short term. ⢠Political signals enhances the accuracy of Bitcoin return forecasts.
Lingli Qing, Ibrahim Alnafrah, Abd Alwahed Dagestani
The energy-intensive nature of cryptocurrency mining, largely reliant on fossil fuels in its early development, has raised growing environmental concern. Consequently, the Index of Cryptocurrency Environmental Attention (ICEA) has emerged, gauging public attention towards this issue. This study investigates the complex interplay between ICEA, cryptocurrency price and policy volatilities, green energy investments, and dirty energy prices. Utilizing a dataset spanning from January 2015 to June 2023, we employ a multifaceted approach encompassing cross-quantilogram, time-varying parameter vector autoregression (TVP-VAR), and wavelet coherence techniques to uncover the dynamic interconnectedness of these three markets. Our findings challenge a simplistic narrative that anticipates a direct link between ICEA and immediate reductions in electricity consumption within the cryptocurrency mining sector. Instead, we discern a nuanced picture wherein ICEA drives significant structural transformations, influencing investments in clean energy markets. Our analysis suggests that ICEA stimulates green energy investments, encouraging miners to explore alternative energy sources with lower environmental impacts . This transition paves the way for more sustainable investments , with green energy sources like renewables playing an increasingly prominent role in powering the cryptocurrency industry .
This study investigates the return propagation dynamics between cryptocurrencies and Emerging market sectoral indices (EMSI), focusing on portfolio impact from Bitcoin, Ethereum, and two gold-backed cryptocurrencies (PAXG and X8X). Using data from 2019 to 2024, we apply a novel DCC-GARCH-based R 2 decomposed connectedness approach to analyse return connectedness among these high-risk assets. We also utilize innovative concepts such as minimum dynamic pairwise connectedness and minimum R 2 decomposed connectedness portfolios in our multivariate hedging portfolios. Our findings reveal that total connectedness is time-variant and influenced by economic events. Bitcoin and Ethereum are identified as net transmitters of shocks, while other assets, particularly gold-backed cryptocurrencies, serve as net shock receivers with minimal impact. Moreover, few EMSIs (financials, industrials, and materials sectors) show significant connectedness in the system. Although our suggested portfolio analysis offers improved returns, none consistently outperform the market. This research offers valuable insights for investors and policymakers regarding the interconnectedness and risk management of cryptocurrencies and EMSI.
This study employs wavelet analysis to examine the contagion between cryptocurrency returns and sovereign bond yields within the West African Monetary Zone (WAMZ) economies, capturing both the frequency-dependent nature of the relationship and time-varying behavior. We analyze daily data spanning 01/26/2021 to 10/07/2022, with a total observable value of 444. The study selected periods of uncertainty within financial markets, namely, the COVID-19 pandemic and the RussiaâUkraine war because there was a need to understand how securities react during such times to help investors plan accordingly. Our results show a negative correlation between sovereign bond yields and cryptocurrency returns, suggesting that investors can use these asset classes as hedge agents, diversifiers, and safe-haven instruments. These findings provide valuable insights for investors and policymakers, shedding light on the potential interdependencies and diversification benefits between these two asset classes.
David Umoru, Beauty Igbinovia, Anthony Aziegbemin Ekeoba, Georgina Asemota ¡ 5 authors
There has been an increase in curiosity about the relationship between the returns on Bitcoins and returns on exchange rates in the last few years. This is especially important since Bitcoin is becoming more and more well-liked as a substitute for fiat money. This study therefore estimates the dynamic impact of exchange rates and their returns on Bitcoin return and also the value-at-risk (VaR) associated with each exchange rate and Bitcoin. The variance series derived from an estimation of the variations between the current and historical prices of Bitcoin using the exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model was used to compute data on Bitcoin volatility. Accordingly, return on Bitcoins was modelled as the natural logarithm of the difference between current day Bitcoin price and previous day price. The joint ARMA-FIGARCH models were estimated in this study to model the returns on Bitcoins transactions and currency trading rates based on time series from February 1, 2010 to August 30, 2024. The research findings underscore the presence of a significant dynamic adjustment of Bitcoin returns to exchange rate returns across all countries. This goes to indicate that there is a high possibility of incurring losses when making investments with digital currencies like Bitcoin. The originality of the paper lies on the fact the research assessed the effect of returns on currency exchange rates of rich countries and also estimated the dynamic effect of Bitcoin returns on exchange rates of the selected countries. The study establishes a substantial volatility feedback effect for returns on Bitcoins, whereas for each of the currencies, the incidence of a less significant volatility feedback effect was made evident. Investors in the foreign exchange market who chose to maximize profits at a lower risk, trading with the pound sterling/US dollar rate, the Euro/US dollar rate, the Australian dollar/US dollar rate; Canadian dollar/US dollar rate, Swiss Franc/dollar rate, New Zealand dollar/US dollar rate, and Luxembourg Franc/US dollar rate are profitable options. In policy circles, monitoring volatility dynamics is crucial for promoting forex market stability and investor confidence. This research benefits policy makers and marketers of financial assets in OECD countries.
This research paper discovers whether Bitcoin provides protection against inflation in Turkey which experienced hyperinflation and financial instability during covid-19. This topic gets attention after global economic fluctuations. Using stationarity tests, GARCH volatility models, and Quantile Regression analysis, the study finds that Bitcoin does not exhibit a consistent negative correlation with inflation. Instead, Bitcoin reacts more to monetary expansion (growth rate) and interest rate changes, behaving similarly to speculative risk assets rather than a stable store of value.The results suggest that Bitcoin is not a reliable inflation hedge but rather a liquidity-sensitive asset influenced by macroeconomic policies. While Bitcoin has seen increased adoption in hyper inflationary economy Turkey, its high volatility limits its effectiveness as a long-term inflation protector. Policy recommendations include strengthening financial regulations, implementing transparent risk disclosures for investors, exploring Central Bank Digital Currencies (CBDCs) as stable alternatives, and promoting diversified inflation-hedging strategies. The findings provide critical insights for policymakers, investors, and financial institutions regarding Bitcoinâs role in global economic stability and inflation management.
The purpose of the paper is to present the results of the research on the potential inclusion of different types of crypto assets, such as Bitcoin, NFTs (Non-Fungible Tokens), and DeFi (Decentralised Finance), within optimal portfolios to help reduce variance or increase returns compared to equity investments. The analysis includes comparisons of different crypto assets and countries, specifically the Czech Republic, Hungary, and Poland. The author constructs optimal equity-crypto portfolios in the Markowitz environment for the period from 16 February 2021 to 8 January 2024, which was adjusted to NFT data availability from this date. Calculations are conducted under two scenarios: minimizing portfolio variance and maximizing returns. The research demonstrates that Bitcoin, NFTs and DeFi can be part of a well-diversified equity portfolio, primarily due to their low correlation with equity markets in the Czech Republic, Hungary and Poland. The paper is important for investors seeking diversification possibilities. Although diversification has been increasingly difficult recently due to increasing correlation coefficients between assets, new asset classes, such as crypto assets, have been created, offering new potential for portfolio creation. The conclusions drawn may also be vital for policymakers who should consider them when formulating regulations concerning systematic risk. The paper contributes value in four aspects. 1) The paper demonstrates that including NFTs, DeFi and Bitcoin in a stock portfolio creates diversification benefits for most portfolios. This is partially due to their slightly higher returns but mostly because of the lower risk that results from the low correlation of crypto assets with traditional markets. 2) Optimal shares of crypto assets differ depending on the equity and the crypto involved. 3) The paper considers Czech, Hungarian, and Polish markets while existing papers concentrate mostly on the American market. 4) The paper shows that there are minimal connections between the Czech, Hungarian, and Polish equity markets and crypto assets.
Magdalena RĂŁdulescu, Kamel Si Mohammed, Abdelmohsen A. Nassani, Nicoleta Dascalu
This study investigates the impact of Bitcoin's energy and water consumption on environmental sustainability, focusing on the load capacity factor (LCF) and the roles of energy transition green technology in major cryptocurrency-producing nations. Utilizing the method of moments quantile regression (MMQR) approach, the findings reveal a negative impact of mining energy consumption on environmental sustainability, particularly in the lower quantiles, with a stronger negative effect in the higher quantiles. Energy transition plays a critical role in moderating this impact, though the shift towards cleaner energy sources has not been sufficient to mitigate the adverse environmental effects. The water footprint has limited influence on LCF across upper and lower quantiles. Moreover, the results do not support the LCF hypothesis. An increase in mining activity leads to a rise in LCF, while this effect turns negative in the 90th quantile. These findings underscore the importance of energy transition in reducing Bitcoin's environmental footprint and emphasize the need for policymakers to swiftly enact regulations and foster innovative technologies to promote environmentally sustainable digital currencies while providing valuable insights into water resource management.
Blockchain technology, originating from the Bitcoin system, is a prominent notion in both practical applications and scholarly discourse. Numerous subtopics may be seen, including the definition of blockchain, its historical significance in the evolution of currency, its durability, and its magnitude of influence within the literature. In other words, sufficient study on blockchain exists in the literature. Likewise, several studies exist about auditing, particularly concerning accounting and taxation within the setting of the Turkish economy. An examination of official declarations and legislation in Turkey reveals that the state's view on the bitcoin industry lacks definiteness. The perspectives are transitioning from negative to positive. Nevertheless, contradicting remarks have also been seen. Upon assessing the existing circumstances, the strategic plans of nations with comparable developmental stages and active cryptocurrency markets are identified. The most appropriate stance for Turkey is neither entirely liberal nor entirely restrictive. The market requires active management and oversight. This control includes accounting and taxation. Turkey should transition from a passive observation approach to one that incorporates a definitive hybrid therapy. This hybrid encryption encompasses the fundamental components of the cryptocurrency system and the corresponding regulation of pertinent regulations.
Naveed Khan, OlaOluwa S. Yaya, Xuan Vinh Vo, Hassan Zada
In this paper, we examine the volatility and time-frequency connectedness among the financial stress index (FSI), cryptocurrencies namely, Bitcoin , Ethereum, Tether, BNB, Solana, and commodities namely, Gold, Silver, Copper, Platinum, and Brent Oil, using the quantile vector autoregressive (QVAR) frequency connectedness, wavelet coherence, and hedging effectiveness techniques, for the period spanning from June 2020 to December 2023. Findings indicate that the spillover effect among FSI, cryptocurrencies, and commodities substantially varies across different volatility conditions. Also, some cryptocurrencies are net receivers of shocks during normal market conditions, while other cryptocurrencies are net transmitters during extreme market conditions. We also find that, during the bullish market, some commodities (Platinum and Brent oil) are net receivers, while other commodities are net transmitters under extreme market conditions (lower quantiles). Similarly, findings further show that, under extreme volatility conditions (higher quantiles), cryptocurrencies and commodities are net receivers of shocks, while FSI is a net transmitter during these volatility conditions. Using frequency co-movement analysis, we find strong and weak correlations between these series in the short- and long-run for shorter periods. Furthermore, findings provide important implications for policymakers and portfolio managers to pay attention to long-term dynamics and design appropriate policies that mitigate the spillover effects.
This paper examines how gold and Bitcoin have changed in terms of value and function in the context of the central banking system in the 21st century. Over the past decades, central banks have held gold as one of their primary reserve assets, given its stability, relative rarity, and traditional status as an inflation hedge and financial crisis buffer. However, with the advent of Bitcoin, central banks now have the opportunity to hold a new asset, one that has been compared to âdigital goldâ. On one hand, Bitcoin revolutionizes the monetary system because it is decentralized, has built in scarcity, and serves as a store of value. However, on the other hand, Bitcoin has traditionally been highly volatile, suffered from regulatory issues, and possesses a relatively short history; all of which hinder Bitcoin from becoming more accepted among central banks. Factors are discussed that affect central bank reserve management: the enduring role of gold, Bitcoin as an additional reserve, and the growing significance of central bank digital currencies. It is argued that while it remains unclear whether central banks will fully integrate Bitcoin into current reserves, its acceptance thus far may impact the decision of global monetary systems regarding incorporating digital technologies alongside more conventional assets, such as gold.
We examine the roles of Gold and Bitcoin as a hedge, a safe haven, and a diversifier against the coronavirus disease 2019 (COVID-19) pandemic and the Ukraine War. Using a rolling window estimation of the dynamic conditional correlation (DCC)-based regression, we present a novel approach to examine the time-varying safe haven, hedge, and diversifier properties of Gold and Bitcoin for equities portfolios. This article uses daily returns of Gold, Bitcoin, S&P500, CAC 40, and NSE 50 from January 3, 2018, to October 15, 2022. Our results show that Gold is a better safe haven than the two, while Bitcoin exhibits weak properties as safe haven. Bitcoin can, however, be used as a diversifier and hedge. This study offers policy suggestions to investors to diversify their holdings during uncertain times. JEL Codes: G1, G11, G12
Cryptocurrencies do not have proper economic fundamentals. Consequently, economic variables cannot predict crypto prices. According to economic theory, cryptocurrencies are unbacked assets that are inherently unforecastable. However, a growing strand of literature suggests global crypto markets to be informationally inefficient. It implies the possibility of return predictability based on past information. Forecasting the allegedly unforecastable becomes feasible. Keeping it sophisticatedly simple, past infomation can be captured by autoregressive integrated moving average (ARIMA) processes of principal components. However, Principal Component Analysis (PCA) for crypto price series is due to their non-Gaussian property not applicable and requires the assumption of a stochastic trend model. Making use of the Central Limit Theorem, Independent Component Analysis (ICA) overcomes this deficiency. We show that ICA combined with ARIMA modeling more than triples the predictability of global crypto price dynamics. ⢠Crypto markets are found to be inefficient in the sense of majority games. ⢠ICA based ARIMA more than triples predictability of crypto price dynamics. ⢠ICA based ARIMA is most reliable for directional out-of-sample predictions.
The paper demonstrates the nonsense of using Bitcoin in financial investments. By using mean-variance financial analysis, stochastic dominance, CVaR, and the Shapley value theory as analytical statistical models, I show how Bitcoin performs poorly by comparing it against other traded assets. The conclusion is reached by analyzing daily freely available market data for the period 2018â2023.
ABSTRACT This study examines the connection between Bitcoin and global factors, including the VIX, the oil price, the US dollar index, the gold price, and interest rates estimated using the Federal funds rate and treasury securities rate, for forecasting analysis. Deep learning methodologies, including LSTM, GRU, CNN, and TFT, with machine learning algorithms such as XGBoost, LightGBM, and SVR, were employed to identify the optimal prediction model for the Bitcoin price. The findings indicate that the TFT model is the most successful predictive approach, with the gold price identified as the most relevant component in determining the Bitcoin price. After the gold indicator, the US dollar index was a substantial factor in the explanation of the Bitcoin price. The TFT model also included regulatory decisions and global events. It was estimated that the Bitcoin price was significantly influenced by the COVIDâ19 pandemic. After that, global climate events and China mining ban strongly affected the Bitcoin price. These findings indicate that regulatory decisions and global events determine the Bitcoin price in addition to macroeconomic factors. The VAR analysis was employed as a robustness check. The results indicate that gold and oil prices have a strong negative influence on Bitcoin, particularly in the long term. The paper has significant policy implications for investors, portfolio managers, and scholars.
Cryptocurrency is the most innovative financial and technological breakthrough of this generation. Investment in cryptocurrency grew from USD 11.18 billion in December 2016 to USD 2.147 trillion in April 2024; however, the rationality of investor exuberance is uncertain. This paper explores stakeholders' perceptions of cryptocurrency using a machine-learning approach based on artificial intelligence (AI). In particular, we employ a lexicon-based emotion-detection sentiment analysis to investigate stakeholder perceptions, using 2.3 million open-source data points. We divide the findings into positive, neutral, and negative stakeholder perception pillars based on factors such as trustworthiness in cryptocurrency, motives, cryptocurrency awareness and knowledge, ownership, socioeconomic characteristics of users, and usage. Our analysis reveals that 51 percent of the stakeholders have a positive perception of cryptocurrency, whereas 40 percent have a neutral perception and 9 percent a negative perception. After identifying the perceptions, we investigate the relationship between cryptocurrency prices and stakeholder perceptions using the autoregressive distributed lag (ARDL) framework with time-series data from August 2017 to July 2023. The long- and short-term results confirm that positive and negative perceptions have statistically significant effects on cryptocurrency prices. Individual investors comprise the largest share of those with a positive perception, as 54 percent have a positive view of cryptocurrency. Institutional investors, however, have the largest share of those with a neutral perception because of the lack of a well-established regulatory framework for cryptocurrency. However, 39 percent of institutional investors hold a positive perception is growing, a sign of a growing trend, as they are among the major investor groups with an interest in investing in crypto. Other stakeholders, such as the government, academia, and other miscellaneous groups, have a negative perception. Our results demonstrate that cryptocurrency has affected social change, social inclusion, and sustainability. Moreover, our findings offer social insights about crypto stakeholdersâ perceptions about the design of strategies to promote cryptocurrency and the establishment of a sustainable crypto ecosystem. ⢠The study investigates the stakeholders' perception towards cryptocurrency. ⢠AI-based sentiment analysis identifies that 51% of stakeholders have a positive perception towards cryptocurrency. ⢠The Autoregressive Distributed Lag model integrated with the UECM model was used to investigate the cointegration between cryptocurrency and stakeholder perception. ⢠A positive perception of cryptocurrency has a significant positive effect on its price.