Manali Agrawal, Rui Dias, Mohammad Irfan, Rosa Galvão · 5 authors
Decentralised Finance (DeFi) provides a new way to perform complex financial transactions by exploiting blockchain's ability to maintain a decentralised ledger of transactions without being constrained by centralised systems or human intermediaries. DeFi provides alternative financial instruments that might lessen portfolio risk, especially given the erratic state of the financial markets today. This study analyses the association between the year of the coin in which it was introduced and the market capitalisation of the respective companies. Furthermore, the study also tries to understand the volatility associated with cryptocurrencies using EGARCH & GJR-GARCH models. The results reveal that market capitalisation is not similar for all three stages of the age of cryptocurrency. Also, negative news tends to impact Bitcoin more than positive news, and the volatility is persistent and long-lasting. Ethereum, BNB & Solana see more volatility from absolute past shocks; however, Tether exhibits low but persistent volatility as a stablecoin.
• Comparative analysis of Bitcoin and Ethereum electricity consumption and returns. • Ethereum's transition to PoS shows a stronger link between returns and energy use. • Shannon and Rényi transfer entropy reveal bidirectional information flow dynamics. • Ethereum returns significantly impact energy consumption, unlike Bitcoin. • A dynamic approach captures time-varying effects of market changes on energy use. Understanding energy consumption associated with cryptocurrency mining gained increasing attention, with the literature focusing mainly on Bitcoin. This study uses data from the two energy consumption indices, to estimate static and dynamic transfer entropies. The results provide a nuanced understanding of the bidirectional relationships and their implications. The dominant direction of information flow for Bitcoin is from electricity consumption to returns, while for Ethereum, it is from returns to electricity consumption, suggesting that Ethereum's returns significantly impact electricity consumption patterns. Results highlight the need for policies that integrate energy forecasting and environmental sustainability considerations and has significant implications for policymaking.
Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng
Due to the recent fluctuations in cryptocurrency prices, Ethereum has gained recognition as an investment asset. Given its volatile nature, there is a significant demand for accurate predictions to guide investment choices. This paper examines the most influential features of the daily price trends of Ethereum using a novel approach that combines the Random Forest classifier and the ReliefF method. Integrating the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Short-Time Fourier Transform (STFT) resulted in high accuracy and performance metrics for Ethereum price trend predictions. This method stands out from prior research, primarily based on time series analysis, by enhancing pattern recognition across time and frequency domains. This adaptability leads to better prediction capabilities with accuracy reaching 76.56% in a highly chaotic market such as cryptocurrency. The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.
Decentralized Finance (DeFi) is increasingly studied and adopted for its potential to provide accessible and transparent financial services. Analyzing how investors use DeFi is important for reaching a better understanding of their usage and for regulation purposes. However, analyzing DeFi transactions is challenging due to often incomplete or inaccurate labeled data. This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs while providing insights into account activities.
Dimitar Kitanovski, Igor Mishkovski, Viktor Stojkoski, Miroslav Mirchev
Maintaining a balance between returns and volatility is a common strategy for portfolio diversification, whether investing in traditional equities or digital assets like cryptocurrencies. One approach for diversification is the application of community detection or clustering, using a network representing the relationships between assets. We examine two network representations, one based on a standard distance matrix based on correlation, and another based on mutual information. The Louvain and Affinity propagation algorithms were employed for finding the network communities (clusters) based on annual data. Furthermore, we examine building assets' co-occurrence networks, where communities are detected for each month throughout a whole year and then the links represent how often assets belong to the same community. Portfolios are then constructed by selecting several assets from each community based on local properties (degree centrality), global properties (closeness centrality), or explained variance (Principal component analysis), with three value ranges (max, med, min), calculated on a maximal spanning tree or a fully connected community sub-graph. We explored these various strategies on data from the S\&P 500 and the Top 203 cryptocurrencies with a market cap above 2M USD in the period from Jan 2019 to Sep 2022. Moreover, we study into more details the periods of the beginning of the COVID-19 outbreak and the start of the war in Ukraine. The results confirm some of the previous findings already known for traditional stock markets and provide some further insights, while they reveal an opposing trend in the crypto-assets market.
Sonal Sahu, Alejandro Fonseca Ramírez, Jong‐Min Kim
This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market’s continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market.
Danial Saef, Odett Nagy, Sergej Sizov, Wolfgang Karl Härdle
Abstract Cryptocurrency markets have recently attracted significant attention due to their potential for high returns; however, their underlying dynamics, especially those concerning price jumps, continue to be explored. Building on previous research, this study examines the presence and clustering of jumps in an extensive tick data set covering six major cryptocurrencies traded against Tether on seven leading exchanges worldwide over nearly 2.5 years. Our analysis reveals that jumps occur on up to 58% of trading days, with negative jumps predominating in both frequency and size. Notably, we observe systematic clustering of jumps over time, especially in Bitcoin and Ethereum, indicating interconnected market dynamics and potential predictive power for market movements. By employing high-frequency econometric tools, we identify temporal patterns in jump occurrence, highlighting heightened activity during specific trading hours and days. We also find evidence of jumps influencing intraday returns, underscoring their significance in short-term price dynamics. Our findings enhance understanding of the cryptocurrency market microstructure and offer insights for risk management and predictive modeling strategies. Nevertheless, further research is needed to develop robust methodologies for detecting and analyzing co-jumps across multiple assets.
Strategies related to the blockchain concept of Extractable Value (MEV/BEV), such as arbitrage, front-, or back-running create strong economic incentives for network nodes to reduce latency. Modified nodes, that minimize transaction validation time and neglect to filter invalid transactions in the Ethereum peer-to-peer (P2P) network, introduce a novel attack vector -- a Blockchain Amplification Attack. An attacker can exploit those modified nodes to amplify invalid transactions thousands of times, posing a security threat to the entire network. To illustrate attack feasibility and practicality in the current Ethereum network ("mainnet"), we 1) identify thousands of similar attacks in the wild, 2) mathematically model the propagation mechanism, 3) empirically measure model parameters from our monitoring nodes, and 4) compare the performance with other existing Denial-of-Service attacks through local simulation. We show that an attacker can amplify network traffic at modified nodes by a factor of 3,600, and cause economic damages of approximately 13,800 times the amount needed to carry out the attack. Despite these risks, aggressive latency reduction may still be profitable enough for various providers to justify the existence of modified nodes. To assess this trade-off, we 1) simulate the transaction validation process in a local network and 2) empirically measure the latency reduction by deploying our modified node in the Ethereum test network ("testnet"). We conclude with a cost-benefit analysis of skipping validation and provide mitigation strategies against the blockchain amplification attack.
Reducing financial risk is of paramount importance to investors, financial institutions, and corporations. Since the pioneering contribution of Johnson (1960), the optimal hedge ratio based on futures is regularly utilized. The current paper suggests an explicit and efficient method for testing the null hypothesis of a symmetric optimal hedge ratio against an asymmetric alternative one within a multivariate setting. If the null is rejected, the position dependent optimal hedge ratios can be estimated via the suggested model. This approach is expected to enhance the accuracy of the implemented hedging strategies compared to the standard methods since it accounts for the fact that the source of risk depends on whether the investor is a buyer or a seller of the risky asset. An application is provided using spot and futures prices of Bitcoin. The results strongly support the view that the optimal hedge ratio for this cryptocurrency is position dependent. The investor that is long in Bitcoin has a much higher conditional optimal hedge ratio compared to the one that is short in the asset. The difference between the two conditional optimal hedge ratios is statistically significant, which has important repercussions for implementing risk management strategies.
This study addresses a critical gap by providing an in-depth examination of how cryptocurrency markets respond to U.S. monetary policy shocks at various price levels. This study contributes significantly to our understanding of the nuanced dynamics governing cryptocurrency markets under diverse monetary policy conditions, thereby enhancing our knowledge of the broader financial ecosystem. Through rigorous quantitative analysis, we utilize monthly time series data spanning from January 2015 to December 2023 and employ models such as Markov-switching dynamic regression, Autoregressive Conditional Heteroskedasticity, and Generalized Autoregressive Conditional Heteroskedasticity. This study reveals that monetary policy shocks result in a decrease in cryptocurrency prices and volatility. Moreover, monetary policy tightening stabilizes the market at low cryptocurrency prices. In higher price states, interest rate increases are associated with reduced cryptocurrency prices and volatility. The findings suggest that changes in interest rates influence the opportunity cost of holding cryptocurrencies, impacting their appeal compared with traditional interest-bearing assets.
Bitcoin has increased rapidly in value since the first day of its integration into today's markets. The increases experienced have directed the interest of global investors to this field over time. In addition to these developments, the increasing popularity of blockchain technology and the increase in the volume of cryptocurrencies have turned these currencies into an important tool for commercial activities. Although there are many studies to measure the international trade balance with exchange rates, no study has been found to examine the relationship between the change in cryptocurrency prices and the trade balance of countries. In this study, the relationship between the trade balance of Nigeria, one of the leading countries in the world in terms of cryptocurrency usage, and cryptocurrencies is analysed using NARDL analysis with coefficient symmetry test (2016/M4-2020/ M12). According to the research results, Bitcoin and Litecoin can have significant long-term impact on Nigeria's trade balance.
Given that cryptocurrencies are now involved in nearly every financial transaction due to their widespread acceptance as an alternative method of payment and currency exchange, researchers and economists have increased opportunities to analyze cryptocurrency prices. Over time, predicting the daily closing price of Ethereum has been challenging for investors, traders, and investment banks because of its significant price volatility. The daily closing price of cryptocurrency is crucial for trading or investing in Ethereum. This report aims to conduct a comparative analysis of the predictive performance of deep machine learning algorithms within a stacking ensemble modeling framework, utilizing daily historical price data of Ethereum from Coindesk, tweets from Twitter spanning from August 1, 2022, to August 8, 2022, and five additional covariates (closing price lag1, closing price lag2, noltrend, daytype, and month) derived from Ethereum's closing price. Seven models are employed to forecast the daily closing price of Ethereum: recurrent neural network, ensemble stacked recurrent neural network, gradient boosting machine, generalized linear model, distributed random forest, deep neural networks, and a stacked ensemble of gradient boosting machine, generalized linear model, distributed random forest, and deep neural networks. The primary evaluation metric is the mean absolute error (MAE). Based on MAE, the RNN forecasts outperform the other models in this study, achieving an MAE of 0.0309.
In traditional blockchains under the Proof-of-Work consensus algorithm, such as Bitcoin and the initial Ethereum, temporal fluctuations of block generation time follow an exponential distribution.Since September 15, 2022, Ethereum has changed its consensus algorithm to Proof of Stake to become Ethereum 2, which qualitatively changes the nature of the fluctuations.Careful analysis reveals that block creation time of Ethereum 2 also slightly fluctuates around 12 seconds, but the origin of this fluctuations is not well understood theoretically.This research contributes to the understanding of this fluctuations, especially focusing on Ethereum 2, by considering a mathematical model on broadcast communications between validator nodes theoretically.As a result, we find that this fluctuation directly comes from the time that it takes for the broadcast communications between validator nodes and it follows a Gumbel distribution.We also show some results of real data analysis which supports our theoretical findings.As an application of the theoretical findings, furthermore, we formulat a blockchain trilemma inequality to explain trade-offs between transaction processing performance, security, and decentralization.
Volatility as a measure of financial risk is a crucial input for hedging, portfolio diversification, option pricing and the calculation of the value at risk. In this paper, we estimate the asymmetric and time-varying volatility for Bitcoin as the dominant cryptocurrency in the world market. A novel approach that explicitly separates the falling markets from the rising ones is utilized for this purpose. The empirical results have important implications for investors and financial institutions. Our approach provides a position-dependent measure of risk for Bitcoin. This is essential since the source of risk for an investor with a long position is the falling prices, while the source of risk for an investor with a short position is the rising prices. Thus, providing a separate risk measure in each case is expected to increase the efficiency of the underlying risk management in both cases compared to the existing methods in the literature.
Hamid Cheraghali, Péter Molnár, Mattis Storsveen, Florent Veliqi
We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.
Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student’s t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices.
A. M. Benarous, İ̇hsan Tolga Medeni, Tunç D. Medeni, Vildan Ateş
This study sheds light on the achievements of digital financial technologies and blockchain technology in the stock market. This study aims to examine the relationship between blockchain technology and macroeconomic variables, as well as the impact these variables have on stock market performance. For this, authors used the methodology of correlation and regression analysis, analyzing data on cryptocurrencies, the stock market and key paper exchange rates. The study confirms a significant correlation between blockchain dynamics, particularly cryptocurrency price fluctuations, and stock market performance, indicating that movements in digital asset classes such as Bitcoin and Ethereum have measurable impacts on traditional financial markets. Traditional economic indicators continue to play a crucial role in stock market behavior, with variables like inflation rates and GDP growth showing strong correlations with market performance. The results suggest a complex interplay between blockchain technology and macroeconomic indicators, emphasizing a growing interconnectedness between emerging digital financial products and economic measures. In addition, the findings are particularly relevant for investors, financial analysts, and policymakers, highlighting the need for a holistic market analysis approach that integrates both new technological advancements in blockchain and economic indicators. The study underscores the evolving influence of blockchain technology on traditional stock markets that encompass both new digital assets and economic frameworks. Moreover, further studies could explore the impact of blockchain technology on specific sectors within the stock market, such as technology, finance, and consumer goods.
From the perspectives of asset pricing, market outreach, regulatory framework, and investor ethos, crypto markets differ substantially from traditional financial markets. Given these fundamental differences, it is interesting to examine how the notions of market efficiency apply to crypto markets, especially because the arguments of the efficient market hypothesis (Fama, 1970) and the adaptive market hypothesis (Lo, 2004, and Lo, 2008) were originally developed in the context of traditional financial markets. Research on the informational efficiency of crypto markets has attracted increasing attention in recent years. This paper examines the evolving efficiency of Bitcoin, the leading cryptocurrency, especially during the period when economies around the world were devastated by the Covid-19 pandemic. For a newly-emerged cryptocurrency with a market that is essentially global, a global shock like the Covid-19 pandemic presents ideal conditions for assessing how efficiency evolves as the market faces a series of shocks. Employing a fixed-length rolling window approach, this paper carries out the following tests: the automatic portmanteau test of Escanciano and Lobato (2009), the wild bootstrap automatic variance ratio test proposed by Kim (2009), the generalized spectral test of Escaciano and Valesco (2006), and the test proposed by Dominguez and Lobato (2003). The results provide evidence of episodes of inefficiency in a market that is efficient over extended periods. The inefficiency index constructed in this study shows that the Bitcoin market went through proportionately longer and more frequent episodes of inefficiency during the Covid-19 period. This is in line with the adaptive market hypothesis and has practical significance for investors and regulators.
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.
Research background: Despite the fact that the issue of private, decentralized digital money (cryptocurrencies) is already quite extensively described in the literature dedicated to the financial system, especially its periphery, there is a deficiency in terms of research on the opinions of participants in the financial system, based on trust in money and its widespread acceptance. International comparative studies are lacking, particularly those conducted before and after the COVID-19 virus pandemic. The pandemic showed that people had significantly changed their willingness to use different forms of money. Being isolated at home and avoiding direct contact with others, people started to use digital money more frequently. Purpose of the article: In response to the identified research gap, this study reports research results on the perception of cryptocurrencies by young financial market participants. It attempts to provide answers to the following research questions: (1) Has the COVID-19 pandemic and the lockdown of economies caused changes at the international level in perceptions and attitudes toward the traditional monetary system and cryptocurrencies? (2) Has the COVID-19 pandemic changed perceptions of cryptocurrencies as a potential alternative to current fiat money? Methods: To evaluate respondents’ opinions, a survey in the form of a questionnaire was conducted. The respondent groups in 2019/2020 were N = 171 (Germany = 143 and Poland = 128), while in 2021, N = 157 (Germany = 95 and Poland = 62). For analytical purposes, statistical analysis using the Z ratio test was used to capture the characteristics of the response distributions and the relationships between them. These two moments in time allowed us to determine whether there were significant changes between opinions before and after COVID-19. Findings & value added: The study’s results showed that while there are significant differences in perceptions of the traditional monetary system and cryptocurrencies due to a variety of factors, the COVID-19 pandemic and the shutdown of economies did not cause statistically significant differences in this regard.
Este estudio investiga los efectos del día de la semana en el mercado digital, con un enfoque en bitcoin y ethereum, abarcando desde el 1º de julio de 2020 hasta el 31 de diciembre de 2023, en el período posterior al COVID-19. Empleando pruebas paramétricas y no paramétricas junto con el modelo GARCH (1,1), se analizó la dinámica del mercado. Los hallazgos indican un efecto significativo del día de la semana en ethereum, caracterizado por notables variaciones de rendimiento entre diferentes días, mientras que itcoin no muestra anomalías de calendario discernibles, lo que sugiere una mayor eficiencia del mercado. La susceptibilidad de ethereum a estos efectos subraya las complejidades actuales del mercado. Las disparidades en las anomalías del calendario surgen de la evolución de la dinámica del mercado, las diferencias metodológicas y la naturaleza especulativa del comercio de criptomonedas. Además, el mercado descentralizado y global complica la identificación precisa de los efectos en todo el mercado. Este estudio proporciona evidencia empírica sobre los efectos del día de la semana en el mercado de criptomonedas, lo que facilita a los inversionistas refinar las estrategias comerciales y la gestión de riesgos. Se justifica realizar más investigaciones para explorar los mecanismos subyacentes y monitorear los desarrollos regulatorios y tecnológicos para obtener información de los inversionistas.
David Iheke Okorie, Joel Miworse Gnatchiglo, Presley K. Wesseh
Active cryptocurrency mining and trading comes with heavy electricity demand and increased emissions. Thus, cryptocurrency mining is prohibited in most economies. Consequently, miners relocate to regions or economies without these prohibitions and/or with relatively lower electricity rates. As such, presenting a nexus between the cryptocurrency and electricity markets, even at the global level. This article investigates the different forms of relationships existing between these markets. The conditional asymmetric volatility model with the Wald, nonparametric and parametric Granger causality tests are employed. The results confirm the existence of both unidirectional and bidirectional lead-lag return relationships between the cryptocurrency and electricity markets. Cryptocurrency returns drive electricity demand. This finding is homogeneous both on a global and strata (homogeneous groupings) basis. Also, the electricity market spills over significant volatilities to the cryptocurrency markets without feedback, nonetheless. Result-based policies are recommended towards green finance, decarbonization, and emission mitigations through the demand for electricity by the cryptocurrency markets. They include the use of clean and renewable electricity sources and technologies for cryptocurrency market activities.
The global cryptocurrency market has witnessed substantial growth, projected to expand from $910.3 million in 2021 to $1,902.5 million by 2028, with a compound annual growth rate (CAGR) of 11.1% during the forecast period. Notably, the United States leads in revenue generation, expected to reach US$23,220.00 million in 2024. With an estimated 992.50 million users by 2028, the market's trajectory indicates increasing adoption worldwide, particularly in developing nations where digital currencies serve as emerging financial exchange mediums. The surge in popularity of digital assets, such as Bitcoin and Litecoin, alongside their integration with Blockchain technology for decentralized and efficient transactions, propels market expansion. Furthermore, Artificial Intelligence (AI) advancements have begun reshaping the cryptocurrency landscape, with AI-based platforms gaining prominence and driving innovation. The growing acceptance of cryptocurrencies as legitimate payment methods by businesses, including major corporations like Tesla Inc. and MasterCard Inc., further accelerates the market growth. This research paper explores the significance of cryptocurrencies, analyzes the fluctuations of leading cryptocurrencies, and elucidates the diverse factors influencing their value, thus contributing to a deeper understanding of this dynamic and evolving market landscape. The findings highlight the complex interplay of these factors, offering insights into the dynamics of cryptocurrency markets and guiding future investment decisions. This comprehensive analysis provides a nuanced understanding of the cryptocurrency landscape, emphasizing both opportunities and inherent risks.
This paper examines the effect of the riskiness of the top four cryptocurrencies on the riskiness of stock market indexes in Egypt, being recognized as a developing country. The analysis uses daily data on cryptocurrencies and the three stock market indexes covering January 2020 to January 2023. The risk is measured using the holding period Value at Risk (VaR). The GMM results show that (a) cryptocurrency volatility is negatively associated with the volatility of stock market indexes. That is, the higher the investors’ interest in trading cryptocurrencies, the lower the volatility of stock market indexes as investors trade stocks less frequently, (b) cryptocurrencies can provide hedge and diversification benefits, and (c) the relationship between volatilities of cryptocurrencies and stock market indexes varies across indexes, therefore, contingent.