This study addresses the growing importance of cryptocurrency (CC) as a financial asset and its increasing popularity as an investment option. Given the rapid expansion of research in this field, the main objective is to systematically synthesize the existing literature on cryptocurrency investment and decision-making, focusing on its intellectual structure, dominant themes, theoretical foundations, and emerging research trends. To achieve this, a hybrid review framework is employed, combining a theory-based systematic literature review with bibliometric analysis, following PRISMA guidelines. The analysis covers 1,184 articles indexed in Scopus and published between 2015 and 2025. Additionally, the study integrates the TCCMR, ADO, and PICO frameworks to provide a comprehensive, multidimensional evaluation of the selected body of literature. The findings reveal that cryptocurrency research is predominantly focused on volatility, market connectedness, portfolio diversification, and behavioral aspects of investment. The results also indicate a strong reliance on econometric and predictive modeling approaches. Emerging research directions highlight increasing attention to sustainability concerns, regulatory challenges, and the application of artificial intelligence in investment analytics. Based on these insights, the study proposes a future research agenda emphasizing theoretical integration, methodological diversification, sustainability perspectives, and decision-oriented modeling. The implications of the research are relevant for investors, regulators, and financial institutions, as they provide a deeper understanding of risks, governance, and decision-making processes in cryptocurrency markets. This study contributes to the literature by offering a comprehensive knowledge structure and research roadmap, representing one of the first attempts to combine bibliometric mapping with TCCMR, ADO, and PICO frameworks in the context of cryptocurrency investment research.
The paper examines the volatility spillover effects and long-term relationship between cryptocurrencies and traditional financial markets in Türkiye using BEKK-GARCH and DCC-GARCH models. It analyses the perception of crypto assets as a “digital safe haven” in an economy marked by high inflation, exchange rate fragility, and financial uncertainty. Using monthly price data for Bitcoin, Ethereum, BIST-100, and Republic Gold from January 2010 to February 2025, the study applies unit root tests, Johansen cointegration, ARDL bounds, and Engle-Granger tests. Results show no long-term price cointegration, but Bitcoin and Ethereum returns are strongly correlated, with DCC-GARCH results showing a dynamic correlation above 50%, while gold and BIST-100 correlate weakly or negatively. BEKK-GARCH highlights significant volatility transmission from Bitcoin to Ethereum, with BIST-100 maintaining persistent volatility. The study concludes that crypto and traditional markets in Türkiye are not integrated long-term, but short-term interactions exist at the return level, with implications for portfolio diversification and financial stability.
An analysis of the ethical and intergenerational dimensions of contemporary energy-finance transitions by systematically mapping the scholarly intersection between crude oil price volatility and cryptocurrency markets is conducted in this study. Drawing on a comprehensive bibliometric and topic-modelling analysis of 4,147 Scopus-indexed publications published between 2014 and 2024, the research investigates how emerging digital financial systems interact with oil market instability and broader sustainability concerns. By integrating Latent Dirichlet Allocation topic modelling with co-citation and keyword network analysis, the study reveals evolving research themes related to energy financialization, decentralized finance, environmental externalities, and regulatory uncertainty. Beyond its technical contributions, the findings highlight critical ethical questions surrounding climate responsibility, distributive justice, and intergenerational equity, particularly in relation to energy-intensive cryptocurrency mining and speculative responses to oil price shocks. The paper advances the concept of moral imagination by demonstrating how financial and technological innovation can either reinforce unsustainable trajectories or support ethically grounded sustainability transitions. The results offer policy-relevant insights for regulators, investors, and institutions seeking to balance economic resilience with long-term environmental responsibility and justice for future generations.
Ahmad Yani, Septiana Sihombing, Yogi Cahyo Ginanjar
Bitcoin has emerged as a prominent digital asset that blends financial innovation, technological advancement, and speculative behavior. However, its growing adoption raises sustainability concerns due to energy-intensive mining and environmental impacts. This study investigates the determinants of Bitcoin prices within the framework of sustainable digital finance by integrating blockchain fundamentals, technical indicators, and macroeconomic variables. Using daily data from 24 November 2021 to 21 November 2024 (753 observations), the analysis conducted with Stata 16—examines miners’ revenue, hashrate, transactions per block, unique addresses, mining difficulty, and trade volume as internal factors, along with gold prices, WTI crude oil, and the S&P 500 index as external factors. Results show that miners’ revenue, hashrate, and transactions per block have positive and significant effects on Bitcoin prices, emphasizing the importance of mining performance and network activity. Trade volume and unique addresses also display positive but less consistent influences, while mining difficulty remains statistically insignificant. Among external factors, WTI crude oil significantly affects Bitcoin prices. Overall, findings suggest that Bitcoin operates as both a financial asset and a technology-driven ecosystem shaped by blockchain dynamics and macroeconomic conditions. The study highlights the need for sustainable mining practices and transparent regulatory frameworks to enhance environmental efficiency.
This study investigates the dynamic relationship between network activity and transaction fees in the Ethereum blockchain by analysing the interaction between Gas Used and Gas Price through a multivariate time series model. The objective is to determine whether variations in network demand influence short-term gas price fluctuations. Daily data of Gas Used and Gas Price were transformed into different logarithmic forms to ensure stationarity. The Augmented Dickey–Fuller test confirmed that both variables are stationary at the five percent significance level, with ADF statistics of −6.21 for Δlog (Gas Used) and −7.12 for Δlog (Gas Price), and p-values below 0.001. The Vector Autoregression model was estimated with an optimal lag length of fourteen days, selected using the Akaike Information Criterion, reflecting the persistence of network and fee dynamics. The results of the Granger causality test indicate a unidirectional causal relationship from Gas Used to Gas Price, with an F-statistic of 3.72 and a p-value of 0.018, suggesting that fluctuations in network demand significantly precede changes in gas pricing. The reverse direction is not significant, with an F-statistic of 1.26 and a p-value of 0.28, indicating that transaction fees do not predict network activity. The impulse response analysis shows that a one standard deviation shock in Gas Used increases Gas Price for two to three days before returning to equilibrium, while shocks in Gas Price have minimal effects on Gas Used. These findings confirm that Ethereum’s fee market operates primarily as a demand-driven mechanism were congestion and transaction volume shape short-term gas price movements.
This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.
This research examines the determinants of Bitcoin (BTC) valuation from January 2011 to December 2025 using Autoregressive Distributed Lag (ARDL) models. The empirical evidence supports the hypothesis that the monetary policy of the United States Federal Reserve—specifically liquidity expansion and interest rate adjustments—drives price dynamics, confirming a pro-cyclical nexus. At the microeconomic level, the density of active institutional addresses and the marginal cost of production significantly influence price trajectories. Furthermore, heightened market volatility, represented by the VIX, exerts a statistically significant negative impact on BTC returns. The findings suggest that Bitcoin has transitioned into a sophisticated value asset, underpinned by production efficiencies and an expanding institutional base. Consequently, Bitcoin represents a viable alternative to centralised financial systems, offering a potential hedge against inflation and the erosion of purchasing power. The study concludes that digital assets warrant inclusion within conservative institutional portfolios, notwithstanding the inherent speculative nature of the market.
Daniel González Cortés, Monomita Nandy, Suman Lodh
Abstract This research analyzes the performance and interconnectedness of major global stock market indices and decentralized finance assets, specifically cryptocurrencies, over the period from 2015 to 2025. The study includes indices such as the S&P 500 and Nasdaq Composite from the United States, the FTSE 100, DAX, and CAC 40 from Europe, and the Nikkei 225 from Japan, and two more indices from China and India representing different economic regions. Additionally, Bitcoin and Ethereum are included to assess the impact of decentralized finance on traditional financial indices and asset allocation strategies. By employing Artificial Intelligence algorithms like ConvLSTM, the research measures the dynamic asset allocation and volatility management through an interconnected spillover matrix. The findings reveal that integrating ConvLSTM enhances the understanding of the interconnectedness between cryptocurrencies and traditional assets, offering improved diversification opportunities due to their low correlation, decentralization, and inflation-hedge characteristics. The study’s results suggest that investors can make more informed decisions regarding dynamic asset allocation in high-volatility portfolios, providing indicators of rising systemic risk and market stress.
The article examines digital financial assets as an instrument of investment portfolio diversification in the Ukrainian investment context. The study argues that Bitcoin and Ethereum should not be assessed through general statements about financial innovation, but through their measurable contribution to portfolio return, volatility and risk-adjusted performance. The empirical part is based on an annual scenario model for 2020–2025 and compares portfolios with 0%, 1%, 3%, 5% and 10% exposure to BTC and ETH. The benchmark portfolio includes domestic government bonds, the USD/UAH currency component, gold and the S&P 500 as a global equity benchmark, while the local Ukrainian equity segment is interpreted cautiously because of its limited liquidity. The results show that portfolios with 1–5% exposure to BTC and ETH improved risk-adjusted efficiency compared with the baseline portfolio. The P3 scenario provided the most balanced relationship between return growth and risk growth, while P5 generated a higher average annual return with still acceptable volatility. The P10 scenario produced the highest geometric average annual return but almost tripled volatility compared with the baseline portfolio, making it more suitable for an aggressive investor profile. The article concludes that digital financial assets may have practical value only as a limited high-risk addition to a diversified portfolio, not as a stable hedging instrument or a substitute for traditional instruments.
Abstract This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.
This study aims to analyze the volatility dynamics and spillover phenomena among major crypto assets (Bitcoin, Solana, and Ethereum) and their relationship with the Jakarta Composite Index (JCI), a proxy for the Indonesian capital market. In the era of digital financial integration, the link between speculative crypto asset markets and conventional stock markets is a crucial issue for financial system stability. This study uses daily price time series data for the period 2020-2025. The analysis was conducted using the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the Diebold-Yilmaz spillover index approach to measure the magnitude of shock transmission between markets. The results indicate significant volatility transmission among the three crypto assets, with Bitcoin remaining the primary source of volatility. Furthermore, this study finds an increasing dynamic correlation between the global crypto market and the Indonesian capital market during periods of economic uncertainty. These findings have important implications for investors in portfolio diversification strategies and for Indonesian regulators in monitoring systemic risks originating from digital assets.
This research examines 42 countries and investigates the relationship between geopolitical risk and global non-fungible token (NFT) investor attention. We use Google search volumes related to NFTs across different regions as a proxy for such attention. Our findings indicate that geopolitical risk positively impacts global NFT investor attention, suggesting that investors in countries with higher geopolitical risk may pay more attention to the NFT market. We further explore the effects across different NFT segments and find that geopolitical risk particularly influences investor attention in the metaverse segment. This positive nexus is further amplified during the Russia-Ukraine war and the COVID-19 pandemic.
I replicate and extend the cross-sectional trend-factor methodology of Liebi, Stulz, and Tsyvinski (2024) on a contemporaneous out-of-sample period and a liquidity-restricted coin universe. Using 141 USDT spot pairs over 128 weekly observations from November 2023 to April 2026, an Elastic-Net cross-sectional regression aggregating 29 technical indicators generates a long-short portfolio with a mean weekly return of 3.82% (Newey-West t = 5.03) and an annualized Sharpe ratio of 4.54. The strategy delivers market-neutral alpha of 3.82% per week (t = 7.09) with a CAPM beta of 0.020. Three findings warrant emphasis. First, value-weighting destroys the alpha entirely, confirming concentration in smaller, dispersed names. Second, twelve of the top fifteen Elastic-Net coefficients are negative, indicating that the underlying pattern is short-term reversal rather than trend continuation. Third, returns are heavily regime-dependent, with Sharpe ratios near 1.0 in trending markets and above 7.0 in dispersive regimes. Cross-sectional dispersion-harvesting alpha persists in cryptocurrency markets, but its empirical realization is sharply sensitive to universe liquidity, weighting scheme, rebalance horizon, and market regime.
This paper investigates the resilience and dynamic behavior of energy-conserving cryptocurrencies (ECCs) during two major global crises: the COVID-19 pandemic and the Russia–Ukraine conflict. Unlike traditional proof-of-work (PoW) assets, ECCs—primarily proof-of-stake (PoS) and low-energy blockchain tokens—are increasingly promoted as sustainable digital alternatives. Using a balanced panel of major ECCs across 10 countries with cryptocurrency markets from January 2019 to December 2023, we apply a panel ARDL–PMG model combined with panel causality tests and structural break analysis to examine the long- and short-run effects of global uncertainty on ECC returns and volatility. Our findings show that ECCs exhibit stronger crisis resilience compared with high-energy cryptocurrencies, with limited long-run exposure to pandemic shocks but moderate sensitivity to geopolitical tensions following the Russia–Ukraine conflict. COVID-19 uncertainty has a short-run negative pressure on ECC markets, whereas geopolitical risk (GPR) driven by the conflict generates asymmetric responses. Cross-country results reveal that ECC markets in technologically advanced, energy-transition economies (EU, Singapore, UAE) exhibit greater stability than those in emerging markets. These findings highlight the potential role of ECCs in sustainable finance, offering policymakers, investors, and regulators insights into the feasibility of promoting energy-efficient digital assets amid extreme global uncertainty.
This study examines dynamic interdependencies and risk transmission among major cryptocurrencies and traditional financial assets, including Bitcoin, Ethereum, U.S. equities, and gold, over the period 2017–2024. Particular attention is given to the structural shift associated with the 2024 U.S. spot Bitcoin exchange-traded fund (ETF) approval, which marked a significant milestone in the institutionalization of cryptocurrency markets. Using daily data, the analysis distinguishes volatility-driven co-movement from structural spillover effects across markets. Dependence structures are modeled using tail-sensitive Student-t copulas applied to GARCH-filtered returns to capture nonlinear and extreme co-movements, while a vector autoregressive framework combined with generalized impulse response functions and Diebold–Yilmaz connectedness measures is employed to evaluate order-invariant shock transmission dynamics across pre- and post-ETF regimes. The results reveal three main findings. First, cryptocurrencies display strong internal dependence and short-horizon contagion, with Bitcoin consistently acting as the dominant transmitter of shocks to Ethereum over an approximately three-day transmission window. Second, linkages between cryptocurrencies and equity markets remain moderate and largely regime-dependent rather than indicative of persistent structural spillovers. Third, gold remains weakly connected throughout the sample, maintaining its role as a diversification asset. Portfolio analysis further indicates that including Bitcoin can reduce portfolio variance by 4–7% and Value-at-Risk by up to 5%, although economic gains are sensitive to transaction costs. Overall, the findings suggest that cryptocurrencies function as a partially segmented asset class, offering conditional diversification benefits despite increasing institutional adoption.
Javier Cifuentes-Faura, Hind Alofaysan, Magdalena Radulescu, Buhari Doğan
This study employs novel decomposed connectedness and portfolio analysis to assess the dynamic spillover effects among carbon finance, artificial intelligence, green energy markets, and bitcoin. The findings indicate that the average total connectedness index is 62%, especially during extreme market conditions. The decomposition of this measure into contemporaneous and lagged connectedness reveals that 56% of the metric can be attributed to contemporaneous dynamics. The portfolio exhibits high Hedging Effectiveness, particularly in extreme market conditions, suggesting that green assets can mitigate risks during periods of financial and geopolitical turmoil. The outcome shows that investments in Bitcoin and technology-related assets often yield the highest returns from 2018 to 2023. Based on the findings, relevant investment policies have been suggested for investors and policy decision-makers.
Type of the article: Research ArticleAbstractCryptocurrency markets are highly volatile, making price prediction a complex yet essential task for investors, financial engineers, and institutions. The purpose of this study is to evaluate whether Bayesian optimization of technical indicator parameters significantly improves the forecasting performance of Long Short-Term Memory (LSTM) models compared to baseline configurations. The study used daily Bitcoin and Ethereum price data from January 2016 to September 2025. Six technical indicators representing trend, momentum, volatility, and volume-based technical indicators are constructed and dynamically optimized through Bayesian optimization. The optimized indicators are then used as inputs to an LSTM forecasting framework. The study found that the baseline LSTM model achieved moderate predictive accuracy, where Ethereum outperformed Bitcoin. After optimization, both models exhibited improved performance, reducing the forecasting error for Bitcoin by 36.4% and for Ethereum by 12.2%. LSTM model with Bayesian optimized indicators showed a higher forecasting accuracy as compared to the baseline model, with 32% and 18.6% improvements for Bitcoin and Ethereum, respectively. These findings suggest that combining optimized technical indicators with LSTM models enhances predictive power in cryptocurrency markets. The approach offers a robust forecasting framework for traders, analysts, and algorithmic systems in high-volatility environments.Acknowledgment“This work was funded by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU261690].”
Abstract This study proposes a methodological strategy composed of econometric techniques and time series modelling to analyse the dynamic asynchrony between Bitcoin and a basket of traditional sustainable financial assets and emerging markets over a 10-year period marked by major economic and financial changes. The centrepiece of this proposal is the Relation Index that combines vector autoregression and detrended cross-correlation analysis to capture linear and nonlinear dependencies, causality, and time-scale sensitive correlations. Thus, this research fills existing gaps in understanding cross-market interdependencies by integrating cryptocurrencies, sustainability indices, and emerging economies into a rigorous multivariate time series framework. Sustainability indices, emerging markets and Bitcoin have shown a growing correlation since 2020, with both interest rates and Bitcoin having strong autoregressive components. The findings indicate that emerging market equities have undergone a structural shift towards synchronisation with global risk assets, with a correlation index that frequently exceeds 0.6 in periods of systemic stress. This evolution highlights the decline in the advantages offered by diversification in developed and developing economies in a complex and interrelated financial environment.
Bitcoin and major precious metals are frequently discussed as hedges against equity drawdowns, inflation surprises, and policy uncertainty, which implicitly assumes a degree of functional equivalence in their risk behavior. Existing work remains limited in assessing high-dimensional dependence structures in the Bitcoin and precious metals system, without relying on bivariate conditional risk measures or restrictive copula frameworks. This study therefore aims to quantify bilateral and multivariate tail dependence and systemic spillovers between Bitcoin and selected precious metals and to identify the best-performing multivariate tail-risk specification, with model comparison conducted using AIC and BIC. The analysis uses 3,665 daily observations of adjusted closing prices spanning January 2013 to September 2024, sourced from Yahoo Finance. Marginal returns are fitted with an ARFIMA–FIGARCH skewed-t model, while cross-asset tail dependence is estimated via vine-copula quantile regression (C- and D-vines) using a 252-day rolling window (one-day step) with 10,000 copula simulations. Results indicate that Bitcoin exhibits substantially larger downside systemic contributions than precious metals, whereas gold displays the smallest systemic risk profile. Across information criteria, the D-vine–based SCoVaR specification provides the best overall fit, indicating that vine-based multivariate tail-risk measures better characterize systemic spillovers between the cryptocurrency and traditionally defensive assets under extreme market conditions. These results motivate future research on broader cryptocommodity networks and macro-financial conditioning, while practitioners and regulators can use the D-vine SCoVaR to monitor and mitigate downside spillovers in mixed-asset portfolios.
Abstract: This study investigates the dynamic relationships between Bitcoin, oil prices, and the US dollar (USD) using a Vector Autoregressive (VAR) model. Utilizing daily data from 2018 to 2023, the analysis reveals that both Bitcoin and oil prices exert significant short-term impacts on the USD, though these effects diminish over the long term. Bitcoin, characterized by its high volatility and safe-haven attributes, serves as an alternative asset during periods of economic uncertainty, while oil prices influence the dollar through trade flows and inflationary pressures. The findings highlight the transient nature of these interactions, with Bitcoin and oil acting as short-term pressure factors on the USD. These insights are crucial for investors and policymakers in managing risks and optimizing strategies in a volatile financial environment. This study contributes to the literature by providing empirical insights into the interconnectedness of cryptocurrencies, commodities, and currencies, offering valuable implications for financial decision-making. Keywords: Bitcoin, Oil Prices, US Dollar (USD), Vector Autoregressive (VAR) Model, Cryptocurrencies, Exchange Rates, Safe-Haven Assets JEL Classification Number: C32, E44, G15, Q43
The amount of international capital invested in sustainability-focused investments and decentralized financial technologies has been growing fast. Thus, this research focuses on the transmission of volatility and optimal portfolio composition among decentralized finance (DeFi) assets, S&P renewable energy and technology market indices, and conventional energy commodities for the period from March 15, 2018, to August 30, 2024. The sample period was divided into three sub-periods to examine the impact of COVID-19, which increased in parallel with the adoption of DeFi and a focus on sustainability: pre-COVID, during-COVID, and post-COVID. This research utilizes the Diebold-Yilmaz and Baruník-Křehlík techniques for time-and frequency-domain analyses, and the Dynamic Conditional Correlation model for portfolio optimization. First, the findings reveal that DeFi tokens (sustainable markets) (brown investments) display moderate (high) (very low) internal connectedness. Second, DeFi tokens demonstrate very low volatility connectedness with both sustainable and brown markets, which suggests strong diversification effects. Third, volatility connectedness among sustainable markets and conventional energy commodities is equally low. Fourth, sustainable markets (conventional energy commodities) make the highest (lowest) contribution to total volatility connectedness, and they operate as net transmitters (receivers) of volatility. Moreover, the total volatility connectedness is 33.7%, which is relatively low, suggesting significant opportunities for diversification of investment portfolios. Furthermore, the outcomes for optimal portfolio weights present greater allocations to green markets compared to conventional energy commodities and DeFi assets, revealing an escalating global transition toward sustainability. Additionally, COVID-19 significantly influenced volatility transmissions and portfolio allocations.
Abstract Dynamics of financial contagion rapidly and drastically transformed by diversifying the investment preferences. Eventually increased diversification in the investment environment coupled with successive global events induced more complex and non-linear connections between the traditional and emerging markets. In this respect, this research explores the dynamic, asymmetric, and non-linear volatility transmissions among the Decentralized Finance (DeFi), Commodity, Energy, Technology, and Clean Energy Markets by incorporating Long Short Term Memory (LSTM) into the Time Domain of Time Varying Parameters Vector Auto Regression (TD-TVPVAR) model to eliminate the shortcomings of the former studies. Results compare the outputs of the Frequency Extension of TVPVAR (FC-TVPVAR) and LSTM-TVPVAR methods and verify the achievements of the new methodology. Consequently, new approach identify Bitcoin (BTC), gold, and oil markets as the primary sources of volatility, since clean energy market is determined to be the only significant destination of risk. Finally, prediction accuracy and the reliability of the incorporated model are validated by performance metrics and the achievements of the new approach are verified by bootstrapping test results.
This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.
Abstract Purpose – The study evaluates the connectedness among the less riskier Digital Assets by investigating the functions of gold backed cryptocurrency alongwith Fan Tokens, Non-fungible Tokens and Real estate tokens, as a new alternative asset class that can be utilized by portfolio managers and investors alongwith policy makers. Design/methodology/approach – This study uses Quantile Vector Auto Regression analysis to measure the quantile cohesiveness among Islamic Cryptocurrencies, Non-Fungible Tokens, Fan Tokens and Real Estate Tokens, recommended by Ando et al., (2022) given extreme quantiles, which specify tail features among different markets performing under extreme conditions. The quantile cohesiveness proposed by Ando et al. (2022) is the blend of quantile vector auto regression with Diebold and Yilmaz (2012) methodology of spillovers for measuring the cohesiveness of the volatilities of markets for extreme higher (95th) and extreme lower (5th) quantiles. Findings – The findings of the QVAR divides spillover in two market condition i.e. median and extremes. In median market condition or we can say normal conditions findings of QVAR shows that Fan token is the major transmitter of shocks while Islamic crypto like X8X is major receiver of the shocks. In extreme condition the major transmitter remains the same i.e. Fan Tokens but major receiver of shock is Real Estate Tokens. Originality/ Value: – Study offer valuable insights to policy makers, portfolio managers and individual investors. For instance study enable the portfolio managers and investors to understand spillovers among Islamic Crypto, Non-Fungible Tokens, Fan Tokens and Real Estate tokens, which will help them in making suitable portfolios. By investigating the function of Islamic gold-backed cryptocurrencies as a new and alternative asset class that can be utilized by both portfolio managers and investors, looking to invest in Islamic Products, to lower their risk of investment. Research Implications: - This study brings novel insight for portfolio optimization and diversification. The findings of this study will have implications for global investor, researcher and policy makers.