The rapid expansion of sustainable finance and digital assets has created a new frontier where environmental performance and financial systemic risk intersect. This study explores the dynamic spillover structure between sustainable financial instruments (green bonds and green equity indices) and cryptocurrencies classified according to environmental efficiency into âgreenâ (Proof-of-Stake) and âconventionalâ (Proof-of-Work) digital assets. Employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) connectedness framework with daily data from 2022â2024, we quantify evolving return spillovers and systemic interdependencies. Results indicate a pronounced surge in total connectedness in early 2023, followed by stabilization into a new equilibrium regime. Green cryptocurrencies display stronger integration with sustainable financial instruments, while conventional cryptocurrencies function as primary systemic shock transmitters during stress episodes. These findings demonstrate that environmental efficiency has become a financially material characteristic shaping digital asset behavior, linking blockchain technological design to ESG-oriented financial dynamics.
Although many academic studies have examined volatility spillovers and dynamic correlations between stock markets, they have largely overlooked the perspective of Small and Medium-Sized Enterprise (SME) markets. On this basis, this study explores the interconnectedness and volatility correlation between Decentralized Finance (DeFi) markets and SME markets. To understand the correlation between these markets, we empirically analyse six European SME market indicesâthe FTSE AIM All-Share Index (AIM), BIST SME Industrial Index (BISTSME), Euronext Growth All-Share Index (EURONEXT), First North All-Share Index (FIRSTNORTH), IBEX Medium Cap Index (IBEXC), and Scale All-Share Performance Index (SCALE)âalongside three cryptocurrencies: Aave (AAVE), Ethereum (ETH), and Uniswap (UNI); two stablecoins: Dai (DAI) and USD Coin (USDC); and one synthetic asset: Synthetix (SNX). The study employed BEKK-GARCH and DCC-GARCH to analyse the existence of spillover effects and correlations from October 5, 2020, to August 18, 2024. The findings indicate that AAVE, ETH, and UNI, in particular, transmit significant volatility to the EURONEXT and FIRSTNORTH markets. However, bidirectional volatility spillover was detected between EURONEXT and AAVE, ETH, UNI, USDC, and SNX, and FIRSTNORTH and AAVE, ETH, UNI, and SNX. This suggests volatility interdependence between these markets and the existence of potential risk contagion channels.
This study investigates the relationship between dirty and clean cryptocurrencies and traditional stock index returns using the Quantile-Quantile (QQR) and Quantile-Quantile Granger Causality (QQGC) methods. The analyses were conducted using daily data from January 2018 to May 2025. QQR results show both positive and negative relationships between dirty and clean cryptocurrencies and the returns of the S&P 500, FTSE 100, TSX, and ASX indices at the low, medium, and high quantiles. According to the QQGC results, both dirty and clean cryptocurrencies showed predictive power for the returns of the S&P 500, FTSE 100, TSX, and ASX indices at different quantiles. Furthermore, it was found that both dirty and clean cryptocurrencies exhibit strong predictive power for S&P 500 and FTSE 100 returns, particularly in the middle quantiles. The results obtained reveal that distinguishing between dirty and clean cryptocurrencies under different market conditions provides important insights for investors' portfolio diversification strategies and risk management practices.
The rapid expansion of cryptocurrencies and decentralized finance (DeFi) has redefined global financial systems, creating new challenges in asset pricing, risk measurement, and systemic stability. This study conducts a comprehensive review of 93 peer-reviewed articles published between 2019 and 2024 to consolidate the fragmented literature on mathematical models applied to cryptocurrencies and DeFi platforms. Using a mixed bibliometricâsystematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates performance indicators, conceptual mapping, and qualitative synthesis to identify methodological advances and research trends. The findings reveal a progressive convergence between econometric models, such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), stochastic volatility, and LĂŠvy processes, and data-driven approaches based on machine learning (ML), deep learning (DL), and reinforcement learning (RL). These hybrid frameworks enhance predictive accuracy and adaptability in high-frequency and non-linear blockchain markets. The review also highlights optimization-based decision models that integrate Conditional Value-at-Risk (CVaR), network theory, and portfolio analytics for decentralized finance operations. However, interpretability, governance, and environmental sustainability remain underexplored dimensions. The study contributes by classifying mathematical approaches to pricing, volatility, and risk propagation, identifying methodological gaps, and recommending future research on explainable artificial intelligence (AI), environmental and cyber-risk modeling, and real-time validation for transparent and resilient decentralized financial ecosystems. ⢠Review 93 studies analyzing mathematical models in cryptocurrency and digital finance systems. ⢠Identify emerging methods for pricing, risk, and portfolio decisions under high volatility. ⢠Compare deep learning models to traditional methods for forecasting and risk evaluation. ⢠Evaluate decision models that include environmental, risk, and governance factors. ⢠Recommend future research on interpretable tools for real-time decision-making.
This research investigates the predictive power of news sentiment from Google News on Bitcoin price movements, leveraging a five-year dataset of news headlines (2019 to 2024). By correlating sentiment scores with historical Bitcoin prices, the study employs various machine learning algorithms to forecast price trends. The results indicate that while Decision Tree and Random Forest models offer balanced predictions, Logistic Regression and Support Vector Machines achieve high AUC scores but suffer from class imbalance. In contrast, NaĂŻve Bayes and KNN models prove less effective. The findings suggest that sentiment analysis of news headlines can provide moderate short-term predictions for Bitcoin price fluctuations. This study introduces an innovative tool for investors and market analysts, offering insights into the influence of news sentiment on cryptocurrency prices.
This paper investigates the Granger causality relationship in Bitcoin mining from environmental, sustainable, and minerâs financial perspectives for the period of February 2017 to January 2025. Using a time-varying Granger causality approach of Shi et al. (2018,2020), we explore how the hashrate, a measure of computational power in the Bitcoin mining process, affects energy consumption, electronic waste, and minersâ revenues. Our findings reveal that an increase in hashrate leads to a significant rise in energy use and e-waste and affects minersâ revenues. In addition, we show that mining revenue Granger causes the hashrate, suggesting economic incentives drive the network security through the hashrate. These results offer new insights for investors, policymakers, and environmental economists. ⢠A time-varying Granger causality approach is adopted. ⢠Higher computational power directly increases electricity demand and electronic waste. ⢠The intensity of competition, as measured by hashrate, has a significant impact on mining profitability. ⢠Higher mining revenues incentivise the use of greater hash power.
ABSTRACT Based on the rationale that returns and volatility are interrelated, we apply a multilayer network framework involving the return layer and volatility layer of cryptocurrencies, NFTs, and DeFi assets over the period January 1, 2018âJanuary 23, 2024. The results show significant connectedness in each of the return and volatility layers, with major cryptocurrencies such as Bitcoin and Ethereum playing a central role. Large spikes in the level of connectedness are noticed around COVIDâ19 pandemic and RussiaâUkraine conflict, and Bitcoin and Ethereum emerge as net transmitters of returns and volatility shocks, emphasizing their significant role around these crisis periods. Notably, a strong positive rank correlation exists between the return and volatility layers, highlighting the significant riskâreturn relationship in the digital asset class. The findings suggest that economic actors should not ignore the interconnectedness between the return and volatility layers in the system of cryptocurrencies, NFTs, and DeFi assets for the sake of a comprehensive analysis of information flow. Otherwise, a share of the information flow concerning the returnâvolatility nexus across these digital assets would be missed, possibly leading to inferences regarding asset pricing, portfolio allocation, and risk management.
M. S. F. Nasrifa, R. P. D. M. Amarasinghe, W. M. P. K. Weerasinghe
The purpose of this research is to explore how investor attention, measured by GSVI, influences cryptocurrency market behavior under varying conditions. For this the study examines the impact of Google Search Volume Index (GSVI) on cryptocurrency returns, considering market uncertainty, news sentiment, and the COVID-19 pandemic. A regression analysis was conducted using datasets covering BNB, Bitcoin, Dogecoin, Solana, and Tether from 2015 to 2022. Stata was used to estimate the relationships between cryptocurrency returns and key variables, ensuring accurate and reliable results to quantify the relationships. Our findings indicate that abnormal increases in GSVI positively affect cryptocurrency returns, particularly during high uncertainty periods and when news sentiment is favorable. Moreover, the effect of investor attention on returns was significantly amplified during the COVID-19 pandemic, suggesting that global crises has heightened the role of behavioral factors in cryptocurrency markets. This research contributes to the literature by integrating investor attention with uncertainty and sentiment measures, offering a comprehensive view of cryptocurrency price dynamics. Unlike previous studies that examine these factors in isolation, our study highlights their combined effect, providing valuable insights for investors, policymakers, and analysts in understanding market trends and decision-making strategies.
The bitcoin market has exhibited highly volatile return movements, experiencing a sharp surge starting from in November 2022 to 2024. This significant fluctuation underscores the importance of analyzing the factors influencing bitcoinâs return dynamics. This study utilizes daily data with a final sample of 590 observations. All time-series variables must be stationary before being processed in the statistical model. The analysis was conducted using Stata 16 software. To ensure the absence of unit roots, the stationarity of the research variables was tested using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. The findings indicate that market capitalization, gold, and litecoin have no significant impact on bitcoin returns. In contrast, minersâ revenue has a significant negative effect, while hashrate, mining difficulty, and the S&P 500 exhibit a significant positive influence on bitcoin returns. This study highlights bitcoinâs role as a store of value and investment asset, emphasizing the impact of hashrate and mining difficulty on its returns and integration into financial markets, particularly the S&P 500. The findings provide insights for investors on portfolio diversification and assets like a gold and equities. Additionally, the study underscores the importance of sustainable mining practices and regulatory policies to balance cryptocurrencyâs economic potential with environmental sustainability. Keywords: Market capitalization; Minesâs Revenue; Hashrate; Mining difficulty; Commodity Asset, Cryptocurrency
The cryptocurrency market, characterized by high volatility, has evolved into a significant financial asset class, attracting both retail and institutional investors. Understanding its interconnectedness with macroeconomic factors is crucial for risk management and financial stability. This study empirically analyzes the dynamic relationships between two primary crypto assets, Bitcoin (BTC) and Ethereum (ETH), and the monetary policy shifts of the U.S. Federal Reserve (The Fed). Using a Vector Autoregression (VAR) model on daily time-series data from January 1, 2022, to June 16, 2025, this research investigates the short-term dynamics, Granger causality, and shock transmissions within this system. The findings reveal a significant one-way causal relationship from The Fed's interest rate changes to both Bitcoin and Ethereum returns, challenging the weak-form Efficient Market Hypothesis. Furthermore, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses provide robust evidence of Bitcoin's market leadership, with shocks in Bitcoin explaining nearly 70% of the variance in Ethereum's movements. These results highlight a clear hierarchical structure: The Fed influences broad market sentiment, while Bitcoin leads internal market dynamics, offering critical insights for investors and policymakers navigating the digital asset ecosystem.
The study examined the connectedness among bitcoin, green bonds (represented by the US S&P Green Bond Index), renewable energy (represented by the OMX Biofuel Index), and gold, utilizing a novel quantile connectedness approach from 14 November 2017 to 30 May 2024. This approach contributes to understanding the transmission mechanisms, influence, and connectedness among the bitcoin, green bond, renewable energy, and gold markets. The result indicates that significant values appear at specific intervals. A significant spike was observed at specific intervals around 2019, mainly due to the trade war between the U.S. and China. A subsequent shock occurred between 2020 and 2021, driven by the COVID-19 pandemic. Moreover, the US credit crisis exacerbated volatility spillovers and financial contagion across markets, worsening these effects in 2023 and intensifying volatility spillovers and financial contagion across markets, exacerbating their outcomes. Additionally, the results suggest that Bitcoin primarily serves as a receiver of shocks. At the same time, the green bond transmits the shocks, and renewable energy and gold have switched between transmission and receiving shock roles during the period. The findings offer valuable insights into sustainable portfolio construction, highlighting that green bonds serve as primary transmitters of shocks and suggest a role as diversification anchors during market stress. Additionally, recognizing Bitcoin as a shock absorber and the shifting roles of renewable energy and gold help investors optimize risk-hedging strategies and enhance portfolio resilience across varying market conditions. This indicates that understanding how these assets correlate across various market scenarios is crucial to maximizing portfolio performance while accounting for sustainability constraints.
This paper applies wavelet quantile correlation to research on the relationship among renewable energy stocks, investor sentiment, and the cryptocurrency market. The empirical results indicated that under extremely negative conditions, in both the short and medium run, renewable energy stocks and cryptocurrencies are negatively correlated, implying that during such periods, renewable energy stocks can be used as a safe haven for cryptocurrencies. The opposite happens when the market is average or booming. This indicates that investors tend to invest simultaneously in these two promising asset classes when the market performs well. Under varied market conditions, FGI correlates positively with cryptocurrency, demonstrating sentiment influences price patterns. Moreover, the correlation between FGI and renewable energy stocks further validates the relationship between cryptocurrencies and renewable energy stocks. These findings can be used to improve the prediction of market trends by investors using sentiment indices and to devise more effective portfolio diversification strategies that minimize risk amid an evolving market.
Despite the growing emphasis on the nexus between growth and macroeconomic indicatorsÂ, research on the influence of cryptocurrencies on economic performance remains limited. This study compares the impact of two leading cryptocurrencies, Bitcoin and Ethereum, on economic growth, alongside inflation, market uncertainty, and oil and gold prices, using panel data from 14 countries between Q3 2015 and Q3 2023. The results demonstrate robust cross-sectional dependence, indicating that economic shocks in one country affect the entire group. Therefore, second-generation tests are employed to confirm the presence of stationarity in the variables. Except for Bitcoinâs trading volume, panel fully modified ordinary least squares estimations reveal a significantly positive impact of cryptocurrencies on growth. Cointegration is present in the long run, while in the short run, strong bi- and unidirectional causality is found for all cryptocurrency proxies. The study provides insights that can help policymakers develop strategies to align economic growth with the crypto market, benefiting the broader economy.
This study contributes to the growing literature on the determinants of Bitcoin volatility by examining its relationship with financial stress. Building on prior research linking Bitcoin volatility to broader economic and financial uncertainty, we employ a combination of regression analysis, a GARCH-MIDAS framework, and a Vector Autoregression (VAR) model to evaluate both the static and dynamic effects of financial uncertainty on Bitcoin. Preliminary regression results indicate that financial stress measures significantly and negatively predict Bitcoin volatility. The GARCH-MIDAS model confirms these results, showing a strong negative impact of financial stress on the long-term component of volatility. VAR analysis further reveals that Bitcoin volatility decreases in response to shocks in financial stress indicators. These findings highlight Bitcoinâs sensitivity to systemic financial conditions and carry important implications for risk management among cryptocurrency traders, institutional investors, and financial regulators.
This study examines the interrelation among gold, oil, and cryptocurrency markets and their implications for economic growth in the context of geopolitical turmoil. Employing panel data from 2000 to 2023 of exporter, importer, and mixed economies, we employ Nonlinear Autoregressive Distributed Lag (NARDL) and Panel Vector Autoregression (PVAR) to ascertain asymmetric as well as dynamic relations. Evidence shows that oil and gold price shocks exert significant effects on growth with geopolitical risk increasing volatility, while cryptocurrencies are heterogeneously resilient in panels. The results provide fresh evidence of cross-asset linkages, risk transmission mechanisms, and provide policy implications for policymakers and investors under volatile geopolitical environments.
Bitcoinâs extreme volatility continues to challenge accurate forecasting and risk management. Traditional econometric approaches struggle with the nonlinear and shifting dynamics of cryptocurrency markets, while deep learning models such as the Gated Recurrent Unit (GRU) often lack interpretability and adaptability to regime changes. To address these limitations, this study introduces a hybrid Gaussian Hidden Markov ModelâGated Recurrent Unit (HMM-GRU) framework for Bitcoin price forecasting. The HMM identifies latent market regimes from four years of daily closing prices and integrates these states as auxiliary features for the GRU network. Experimental results show that the hybrid model consistently surpasses the standalone GRU in predictive accuracy. Under the optimal configuration, HMM-GRU achieves a Mean Absolute Error (MAE) of 1,557.33 and a Mean Absolute Percentage Error (MAPE) of 1.42%, compared with 1,713.30 and 1.57% for GRU, representing an approximate 9% improvement in both absolute and relative error performance. The inclusion of regime-based features enables the model to better capture market transitions and mitigate overfitting to short-term noise. Beyond performance gains, the proposed approach enhances interpretability by linking forecasts to identifiable market regimes. These findings highlight the value of combining statistical regime detection with deep learning for volatile financial assets, providing practical insights for both investors and researchers in time-series forecasting.
Industry 4.0 and digital transformation have accelerated the emergence of virtual assets such as cryptocurrencies. Among them, Bitcoin, a virtual currency, has captured significant attention from both finance theorists and practitioners, achieving the highest market capitalization to date. The objective of this study is to examine the behavior and interrelationships between Bitcoin and several traditional financial assets within the framework of an international diversification strategy that combines conventional and crypto assets. In this context, Bitcoin is considered as a potential new asset class for portfolio diversification. To explore this relationship, we analyze the links between Bitcoin and a selection of major currenciesâEUR, GBP, and JPYâas well as certain commodities. The study employs the Value at Risk (VaR) approach using three empirical methods, complemented by Conditional Value at Risk (CVaR) as a robustness measure, given its ability to capture tail risk more effectively than VaR. Using daily data from October 29, 2016, to October 23, 2020, the findings reveal that including Bitcoin in a diversified portfolio can significantly enhance riskâreturn characteristics. These results provide new insights for portfolio managers and investors seeking optimal diversification strategies in the context of digital finance.
This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoinâs safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.
Abstract This study explores the dynamic volatility spillovers and interconnectedness between cryptocurrency and traditional futures markets. Using a multi-method approach that integrates wavelet coherence analysis, TVP-VAR connectedness, and DCC-GARCH modeling, the research identifies notable shifts in spillover patterns during crises, such as the COVID-19 pandemic, the FTX collapse, and the Russia-Ukraine conflict. The results reveal that the correlations between Bitcoin futures and traditional asset classes depend on the market conditions and intensify during crises. The connectedness analysis shows that Bitcoin futures play a dual role, acting as a transmitter of long-term shocks and a receiver of short-term shocks during periods of crisis. Equity futures emerged as the primary long-term transmitters of shocks, whereas other assets acted as shock receivers during the pandemic. Furthermore, the study evaluates hedge ratios and portfolio weights using the DCC-GARCH model. The portfolio analysis reveals that Bitcoin futures require a minimal allocation within diversified portfolios, suggesting their limited effectiveness as a hedge and safe-haven asset. These results aim to inform portfolio managers in developing efficient hedging strategies and assist regulators in monitoring financial market stability. This study fills gaps in the existing literature by understanding how decentralized financial instruments interact with financial markets and providing insights into risk management in modern markets.
This paper explores how Bitcoin and Ethereum differ from traditional financial assets such as gold, Brent crude oil, the S&P 500 and Apple Inc. in terms of risk, return and integration with the traditional financial market over the period of 2018-2025. The thesis evaluates whether these digital assets can serve as viable components of a diversified investment portfolio. The motivation stems from the recent institutionalization of cryptocurrencies, including the recent approval of spot Bitcoin and Ethereum ETFs and wide public interest. 2858 observations of log returns were used to analyse correlation, multivariate regression, volatility, CAPM regression and Sharpe ratio. The results show that Bitcoin and Ethereum exhibit very low correlations with traditional assets, which supports their ability to act as diversifiers. The regression models revealed that gold and the S&P 500 have small but statistically significant explanatory power for cryptocurrency returns, while Apple Inc. and Brent crude oil do not. Volatility analysis confirms that Bitcoin and especially Ethereum are much more volatile than all traditional assets in the sample. CAPM results show that both digital assets respond positively to market movements, implying slow financial integration. Returns of cryptocurrencies were extremely high, but when the Sharpe ratios were computed, cryptocurrencies showed weak risk-adjusted performance, compared to Apple Inc. and gold. Overall, the findings show that cryptocurrencies are assets with high risk and are driven more by crypto-specific factors, but are increasingly integrating into the broader traditional financial market. They provide diversification benefits but only in small allocations.
The major focus of this research study is to understand the impact of the Russia-Ukraine crises or war on three major Crypto currencies like Bitcoin, Binance coin and Ethereum. This study also provides insight about the reaction of the Crypto market during the ongoing war situation and how the Cryptocurrencies react during the war crises, either bitcoin, ethereum, and the binance coin have the positive impact or the negative impact during the war, or the war has no impact on Cryptocurrencies. The relationship between these cryptocurrencies are also examined during this research. The major findings show that the ARCH effect exist in the Binance coin, Bitcoin, and the Ethereum market series. The research study used the GARCH methodology for analysis of results. For Bitcoin and Binance coin there is no direct impact in it, and factor of volatility exist in it. For Ethereum there is no direct impact of war, and factor of volatility does not exist in it. The research gives valuable insights to investors and policy makers.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesiaâs macroeconomic environmentâspecifically interest rates, inflation, and exchange ratesâaffect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
This paper investigates the role of artificial intelligence (AI) stocks and AI cryptocurrencies in portfolio diversification, reflecting on the rising interest in technology-oriented assets. While much research has focused on the diversification, hedging, and safe-haven properties of digital assets, such as Bitcoin and Ethereum, this study focuses on whether AI cryptocurrencies and AI stocks provide untapped diversification potential. Using mean-variance, risk parity, and higher-order moments approaches, we construct portfolios that combine AI stocks, AI cryptocurrencies, and traditional assets under various optimization frameworks. The findings reveal that the mean-variance framework is more conservative in allocating to AI cryptocurrencies, while the higher-order moments approach accommodates for greater flexibility. Seemingly, investors may benefit from expanding their asset pool to incorporate AI stocks and AI cryptocurrencies. Across most portfolio settings, gold and commodities dominate allocations, followed by AI stocks, with AI cryptocurrencies receiving only marginal weights owing to their high volatility. However, allocations to AI cryptocurrencies increase as investor risk tolerance increases, thereby highlighting their potential for risk-seeking portfolios. Overall, the results indicate that AI stocks and AI cryptocurrencies can enhance portfolio diversification and improve risk-return outcomes. These results offer valuable insights for investors seeking to optimize their portfolios, through exposure to emerging technology-driven assets while balancing traditional risk considerations. ⢠The study explores the diversification potential of AI Stocks and AI Cryptocurrencies to a traditional portfolio. ⢠Dominated by NVIDIA and Tesla, AI stocks perform better than AI cryptocurrencies. ⢠AI cryptocurrencies offer limited diversification benefits while significantly increasing portfolio risk. ⢠Unlike AI stocks, AI cryptocurrencies are not dominated by a single player in portfolio diversification. ⢠Allocation to AI cryptocurrencies is highly sensitive to investor risk aversion, particularly driven by their explosive market behaviour.
The environmental footprint of cryptocurrency networks, particularly the electricity-intensive Bitcoin (BTC) blockchain, has raised growing concern among policymakers, investors, and environmental organizations. This study examines how major global environmental events and climate policy announcements influence Bitcoinâs return and risk dynamics, linking digital asset markets to sustainability debates. Thirteen events between 2010 and 2024âincluding multilateral agreements (e.g., the Paris Agreement), COP summits, extreme weather disasters, and national policy interventionsâare analyzed using an event study framework integrated with the Capital Asset Pricing Model (CAPM) and GARCH-based volatility modelling. We hypothesize that highly visible policy events generate stronger short-run abnormal returns than climate disasters, while disasters produce more persistent effects on volatility. Results confirm this distinction: events such as the U.S. Paris Agreement withdrawal triggered immediate and significant reactions, whereas major weather disasters induced longer-term volatility adjustments. While overall systematic risk remained stable, event-specific responses revealed shifts in Bitcoinâs sensitivity to global equity markets. Climate-related signals shape speculative digital asset markets, with implications for sustainable finance, climate risk assessment, and regulatory policy design. Climate-related news can shape investor perceptions of energy-intensive digital assets, with implications for environmental policy design, sustainable finance strategies, and climate risk assessment. For policymakers, the results highlight the potential of environmental signals to influence speculative markets, supporting the case for integrating financial market behaviour into environmental management and regulatory planning.