Κωνσταντίνος Γκίλλας, Maria Tantoula, Manolis Tzagarakis
Abstract We analyze properties identified in the price volatility of Bitcoin and some of the leading cryptocurrencies namely Litecoin, Ripple, and Ethereum. We employ Heterogeneous Autoregressive models (HAR) in both a univariate and multivariate level of analysis. First, the significance of heterogeneity and jumps is examined, considering the ability of several univariate HAR models, to predict realized volatility of cryptocurrencies. Second, we examine the relevance of realized volatility jumps and covariances in the transmission of volatility spillovers among cryptocurrencies. We perform a comparative spillover analysis of the multivariate HAR models in two versions, considering variances only and covariances as well. Our results indicate that covariances and jumps inclusion lead to an increase in spillovers. The time-varying spillover analysis indicates higher dependency between Bitcoin and the other cryptocurrencies mostly at short frequencies.
This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.
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.
In this article, network analysis has been employed to study the dynamic evolution of the cryptocurrency market from 1 January 2020 to 1 January 2024. This approach facilitates an in-depth exploration of the market’s response to several major events during this period, including the coronavirus disease of 2019 (COVID-19) pandemic and the bankruptcy of FTX, one of the largest cryptocurrency exchanges. The study focuses on analysing key network characteristics of the cryptocurrency market, namely: (a) degree centrality, (b) betweenness centrality, (c) clustering coefficient and (d) average path length. Additionally, we explore the co-movements within the market, categorising cryptocurrencies into functional groups for a comparative analysis. This approach enables us to examine shifts in the cryptocurrency network topology, providing insights into how different groups of cryptocurrencies interact with and influence each other. Through this network analysis, we aim to shed light on the intricate interrelationships among cryptocurrencies. The findings of this study are intended to provide investors with valuable insights, potentially guiding the development of more informed and strategic diversification strategies in the dynamic and evolving landscape of the cryptocurrency market. JEL Codes: G11, G12, D85
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.
This study aims to calculate the Value at Risk (VaR) in the cryptocurrency assets portfolio using the Monte Carlo simulation method. Analysts mostly use value at risk to measure the potential loss from many financial assets. This study analyzed four cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), BNB (BNB), and Solana (SOL), from March 1, 2020 to March 31, 2024. The analysis results provide an overview of the expected performance, standard deviation, VaR, and Monte Carlo simulation results. According to the VaR analysis, SOL is the most risky asset, while Bitcoin is the least risky asset Investors should consider VaR before investing in any asset. Investors should also consider this information and their risk profile before making an investment decision.
Purpose The purpose of this study is to investigate how the price delay of cryptocurrencies to market news affects the herding behavior of investors, particularly during turbulent events such as the COVID-19 period. Design/methodology/approach The paper investigates the presence of herding behavior by using Cross-Sectional Absolute Deviation (CSAD) measures. We also investigate the herding activity in the crypto traders’ behavior during up and down-market movements periods and under investor extreme sentiment conditions. The speed of cryptocurrencies’ price response to the information embedded in the market is assessed based on the price delay measure proposed by Hou and Moskowitz (2005). Findings Our findings suggest that cryptocurrencies characterized by high price delays exhibit more herding among investors, thereby highlighting higher degrees of market inefficiencies. This is also apparent during periods of extreme investor sentiment. We also document an asymmetric herding behavior across cryptocurrencies that present different levels of price speed adjustments to market news during bullish and bearish market conditions. Our results are consistent and robust across different sub-periods, various market return estimations and different price delay frequencies. Practical implications The study provides crucial guidelines for investors’ asset allocation and risk management strategies. This study is also valuable to regulators and policymakers, particularly in light of the increasing importance of financial reforms aimed at mitigating market distortions and enhancing the resilience of the cryptocurrency market. More specifically, regulations that improve the market’s information efficiency should be prioritized to speed up the response time of cryptocurrency prices to market information, which can help reduce the investors' herding behavior. Originality/value This paper makes a novel contribution to the academic literature by investigating the unexplored relationship between cryptocurrency price delays and the presence of herding behavior among investors, especially in times of uncertainty such as the COVID-19 pandemic.
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.
분산 금융(DeFi)과 블록체인 기술의 결합은 금융 환경에서 획기적인 변화를 예고하며, 비교할 수 없는 투명성, 포괄성, 효율성을 약속한다. 그럼에도, 이 DeFi 분야는 주로 거래 비용 최적화와 다양한 이해 관계자의 인센티브 조정과 같은 중대한 도전에 직면하고 있다. 본 연구는 DeFi 풀이 내쉬 균형상태라는 새로운 검증 방식을 도입하여, DeFi 풀이 본질적으로 자체 규제하고 균형 상태를 유지할 수 있다는 가설을 검증하고자 한다. 수학적 표현과 시뮬레이션의 구현 방법을 통해, DeFi 프로토콜이 게임 이론의 원리를 활용할 수 있는 가능성을 밝혀내며, DeFi 생태계 내에서 프로토콜 설계의 안정성, 효율성, 공정성을 향상시키기 위한 방안을 제시한다.
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.