The rapid growth of decentralized finance (DeFi) has revolutionized the global financial landscape, providing decentralized alternatives to traditional financial services. This study investigates the asymmetric multifractal behavior of nine DeFi markets—AAVE, Pancake Swap (CAKE), Compound (COMP), Curve Finance (CRV), Maker DAO (MKR), Synthetix (SNX), Sushi Swap (SUSHI), UniSwap (UNis), and Yearn Finance (YFI)—using Asymmetrical Multifractal Detrended Fluctuation Analysis (A-MFDA). The use of generalized Hurst exponents, Rényi exponents, and singularity spectrum functions revealed that DeFi markets exhibit multifractal behaviors. The analysis uncovered clear differences between uptrend and downtrend fluctuation functions, highlighting asymmetric multifractal behavior. The asymmetry intensity was analyzed through excess differences in uptrend and downtrend generalized Hurst exponents. AAVE, COMP, SNX, UNis, SUSHI, and MKR exhibit negative asymmetry, with stronger correlations during negative trends. CAKE shifts from positive to negative asymmetry, showing sensitivity to both trends. CRV is more volatile in negative trends, while YFI consistently displays positive asymmetry across market fluctuations. The results also reveal that long-term correlations and heavy-tailed distributions contribute to the multifractality of DeFi assets. This study highlights the need for dynamic risk management in DeFi markets, urging investors to adopt adaptive strategies for volatile assets and prepare for sudden price fluctuations to safeguard investments.
Whilst previous studies have primarily focused on the hedge effects and co-movements between cryptos and traditional assets, cryptos’ features that are associated with hedge effects and co-movements have often been neglected in extant studies. This research aims to investigate how specific cryptocurrency features influence their dynamic volatility and co-movements with stock markets. Using cointegration analysis and Granger causality tests, we explore the hedge effects and co-movement between the top 100 cryptos and eight leading stock markets. Additionally, we use logistic regression models to assess the role of crypto-specific features in driving these dynamics. We find that consensus mechanisms and having limited supply are key features influencing co-movements during and after the Covid-19 pandemic, while acting as a means of payment predominantly affects co-movement after the pandemic. We highlight cryptos underlying characteristics and functionalities that could significantly affect their demand and people’s attitudes toward them. Based on finance theory, these differing characteristics could affect cryptos’ versatility thereby impacting their demand, pricing, hedge effects and co-movement in their returns compared to stock returns. This paper makes significant theoretical contributions by addressing the role of crypto features in their co-movements and hedge effects on representative stock markets.
• Matching trading periods and investment horizons between equities and cryptocurrencies are fundamentally challenging. • Monday returns and intermarket connectedness of cryptocurrencies notably differ when alternative benchmark (closing) prices are used. • Using inconsistent return estimation methods from different sources delivers spurious intermarket connectedness results. • THETA, GNO, GLM, ENJ, WAXP, KCS, and WAVES are most vulnerable to the return estimation method. • Seemingly inconsequential choices critically affect the main conclusions drawn by the existing studies on market interconnectedness. Cryptocurrencies trade continuously, unlike traditional assets limited to weekdays, creating challenges in calculating Monday returns. This paper investigates the impact of four benchmark closing prices—Friday, Saturday, Sunday, and a weekend average—on intermarket connectedness. Analyzing 72 cryptocurrencies (2018–2024) and their relation to the S&P500 using the TVP-VAR model, we find significant variations in economic and statistical outcomes, influencing both the magnitude and direction of spillovers. Mixed log- and non-log-based return methods yield inconsistent results for specific cryptocurrencies like THETA, GNO, GLM, and WAVES. These findings highlight the critical importance of consistent return methodologies in cryptocurrency market analysis.
Especially new generation investors may prefer to use stocks of popular companies that use advanced technologies and cryptocurrencies as investment instruments. Gold, one of the classical investment instruments, still maintains its place among the commodity assets in the portfolios of investors around the world. These asset groups were evaluated in this study. As the first group investment tool, decacorn and hectocorn technology companies called the new generation the magnificent five; Company stock returns of Apple, Microsoft, Amazon, Alphabet, Nvidia Corporation and Tesla were analyzed. In addition, as the second financial asset, cryptocurrencies, which are used as investment instruments as well as being used in daily life with the evolution of technology, and Bitcoin (BTC), which remains popular among these cryptocurrencies, were the subject of the study. Finally, the study evaluated gold mines, one of the world's oldest valuable investment instruments, compared with other financial assets. The study examined the magnificent five stocks, BTC and gold ounce prices between the periods of 2020:01 and 2023:12, using mutual cointegration, vector error correction (VEC) and Granger causality analyses. Findings of the study; Short-term shocks caused by variables in BTC stabilise after about a month. In this process, as NVDA shares increase, BTC value decreases, and as gold value increases, BTC value increases.
Yang Zhou, Chi Xie, Gang‐Jin Wang, Jue Gong · 5 authors
Abstract Cryptocurrency is a remarkable financial innovation that has affected the financial system in fundamental ways. Its increasingly complex interactions with the conventional financial market make precisely forecasting its volatility increasingly challenging. To this end, we propose a novel framework based on the evolving multiscale graph neural network (EMGNN). Specifically, we embed a graph that depicts the interactions between the cryptocurrency and conventional financial markets into the predictive process. Furthermore, we employ hierarchical evolving graph structure learners to model the dynamic and scale-specific interactions. We also evaluate our framework’s robustness and discuss its interpretability by extracting the learned graph structure. The empirical results show that (i) cryptocurrency volatility is not isolated from the conventional market, and the embedded graph can provide effective information for prediction; (ii) the EMGNN-based forecasting framework generally yields outstanding and robust performance in terms of multiple volatility estimators, cryptocurrency samples, forecasting horizons, and evaluation criteria; and (iii) the graph structure in the predictive process varies over time and scales and is well captured by our framework. Overall, our work provides new insights into risk management for market participants and into policy formulation for authorities.
Gabriel A. Giménez Roche, Antoine Noël, Loïc Sauce
We analyze the determinants of Bitcoin (BTC) trade volume in decentralized exchanges (DEXs) and test the claim that BTC trades on these platforms are censorship-resistant. The study finds that overall economic freedom, particularly monetary freedom, correlates indirectly with BTC trade volumes, while capital restrictions on residents' transactions abroad correlate in two different directions. Purchase transactions inversely correlate with BTC volume in DEXs, while sales transactions correlate directly. These results suggest that BTC can be used to hedge against poor institutional frameworks, particularly against poor monetary governance, and as a vehicle for institutional hedging against repressive capital controls and institutional failures. The study's originality lies in its use of on-chain panel data on the volume of BTC transactions, which are country-specific and allow for comparing the impact of country-specific socio-institutional variables on BTC volumes. • Decentralized exchanges leverage blockchain for innovative financial services. • BTC provides an institutional hedging option against poor governance frameworks. • On-chain data reveal BTC country dynamics and institutional hedging potential.
Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereum’s pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem.
The recent economic downturn due to the labour disruption caused by the COVID-19 pandemic has again brought challenges to individual and household financial stability. Motivated by the controversial view regarding the reliability of crypto investments in times of market turbulence, we examined whether investing in cryptocurrencies would be a good option for improving individual financial satisfaction during an economic downturn. Utilizing data from the most recent 2021 cohort of the National Financial Capability Study (NFCS2021) and an instrumental variable design, we find that crypto investments relate to a lower level of financial satisfaction. Moreover, while people with higher income levels are more financially satisfied, working during the pandemic is associated with a lower level of financial satisfaction. In addition to the well-known volatile feature of cryptocurrencies, our findings provide additional insights about their influence on individual financial well-being during economic downturns.
The paper investigates how attention to the Russia-Ukraine war affects cryptocurrency returns by creating a Google search volume index (GSVI) using Google trends keywords. It finds that crypto returns react positively to attention to war and negatively to the volatility index (VIX), demonstrating that investor fear during times of crisis may increase interest in cryptocurrencies. The research provides specific insights into crypto markets that can aid portfolio managers and regulators. It also adds to the limited studies on the impact of war on cryptocurrency returns.
Purpose This study aims to examine whether rising air pollution impacts cryptocurrency returns across different categories. Design/methodology/approach This study uses panel regression to investigate the impact of air pollution on cryptocurrencies between January 2014 and June 2023. Cryptocurrency prices are sourced from www.coinmarketcap.com . Air quality is measured using the air quality index (AQI) values provided by the World Air Quality Index Project. Generalized method of moments (GMM) estimators for dynamic panel regression have also been used to control for endogeneity concerns. Findings High AQI levels are observed to negatively affect cryptocurrency returns. This impact remains absent during good air quality and for cryptocurrencies with lower energy consumption like stablecoins, clean energy and health cryptocurrencies, supporting the argument that rising air pollution leads to lower returns for cryptocurrencies more prone to damaging the environment. Practical implications The findings of this study could offer investors valuable insights in formulating more efficient cryptocurrency trading strategies. It also demonstrates how environmental variables influence the performance of volatile assets like cryptocurrencies. The presence of lower returns for currencies perceived as damaging to the environment could put the focus on promoting sustainability in the production of such digital currencies. Originality/value No prior study has investigated the influence of AQI on cryptocurrency returns. This study aims to focus on the behavioral aspect of financial decision-making. As cryptocurrency adoption rates rise across the globe, the findings of this study can provide useful insights to cryptocurrency traders.
Ahmed Bouteska, Taimur Sharif, Layal Isskandarani, Mohammad Zoynul Abedin
This research investigates how market-wide conditions (macro aspects) and individual cryptocurrency-specific characteristics (micro aspects) influence the efficiency of cryptocurrency markets. Macro aspects encompass the impacts of overall market liquidity, volatility, and global uncertainty events (e.g., the COVID-19 pandemic and geopolitical conflicts) on market efficiency. Micro aspects focus on cryptocurrency-specific attributes, such as liquidity and volatility levels, and their effects on price delays. Our findings reveal that rising liquidity and declining volatility enhance market efficiency at both macro and micro levels. Furthermore, we observe that during the periods of uncertainty, inefficiencies are exacerbated among less liquid and more volatile cryptocurrencies. We propose that the perceived uncertainties and substantial transaction costs associated with cryptocurrencies that lack liquidity and exhibit high volatility act as deterrents, diminishing the eagerness of active traders to participate in arbitrage trading. Consequently, this leads to inefficiencies in the market. The results of this study offer valuable insights for financial market regulators and authorities as well as investors associated with the crypto market, particularly during the times of financial turmoils.
As a theoretical foundation and overview, the paper explains how blockchain technology influences energy trade and finance through decentralized, safe, and transparent peer-to-peer transactions. It examines the current energy crisis that arises with a steep, rising curve of rather unorthodox consumption of energy and calls for cleaner, more reliable sources of energy. It also discusses how blockchain-based platforms could help eliminate persistent challenges in centralized energy systems. By combining the previous literature on distributed ledgers, smart contracts, and decentralized market mechanisms, we find that blockchain provides faster settlements, lower overheads, and enhanced resilience against single points of failure. This study will review how blockchain-enabled energy finance solutions speed transactions, build trust, and allow for innovative funding approaches, such as green bonds and energy banking. All in all, the findings support blockchain as a viable way of achieving a more flexible, customer-oriented, and environmentally sustainable energy sector while showcasing the technological, regulatory, and operational gaps that research and responsible policy actions must address. • Examines Blockchain's decentralized role in energy trade and finance. • Explores Blockchain's advantages and challenges in energy finance integration. • Reviews Blockchain models for platform, tech, privacy, and security solutions. • Highlights Blockchain's potential to enable trust and direct energy transactions. • Discusses future needs for advanced algorithms and supportive regulations.
This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations.