E. I. Agbedo, R. O. Osanakpa, Salami M. O, C. O. Kayoh ¡ 5 authors
This study explores the intricate dynamics of digital asset engagement, employing a Markov chain model to examine peer-influenced adoption (θ) and event-triggered abandonment (γ) across diverse network structures. The study gives hindsight into mixing time (time to stationarity) analysis, which represents the duration required to achieve a stationary distribution, and investigates its upper bound along with a revised linear programming proof. Simulations reveal the significant impact of network architecture on the spread of adoption and abandonment behaviors over time. Random networks typically demonstrate faster mixing, facilitating rapid information dissemination and market stabilization. In contrast, structured networks like small-world and scale-free exhibit more complex and often slower mixing patterns, showing distinct vulnerabilities or resilience based on the prevailing dynamic. Phase diagrams outline areas of sustainable adoption, critical decline, and swift abandonment, showcasing the long-term viability of various digital asset categories (such as Bitcoin-like, Meme coin-like, and NFT-like) within these network landscapes. The research underscores the crucial influence of network structure on market efficiency, information flow, and the enduring sustainability of digital assets. Additionally, this study aims to provide practical insights for Web3 project teams striving to cultivate sustainable asset ecosystems.
Abstract ValueâatâRisk (VaR), the primary measure of downside risk in market risk management, relies heavily on the accuracy of volatility forecasts produced by risk models. This paper shows that, for forecasting the VaR of cryptocurrencies, the timeâheterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.
Abstract Cryptocurrency markets have evolved into a vital segment of the global financial ecosystem, drawing considerable interest from both investors and regulatory bodies. Yet, their extreme price instability demands innovative strategies for risk mitigation and investment that diverge from conventional financial practices. This research focuses on analyzing the volatility patterns of leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)âby employing GARCH-family models such as GARCH, EGARCH, TGARCH, and CGARCH. Through a comparative evaluation of these models, the study identifies the optimal framework for characterizing cryptocurrency market volatility. Utilizing daily closing prices from Yahoo Finance (January 1, 2019, to January 8, 2025), the analysis reveals that TGARCH outperforms others for BTC, EGARCH for ETH, and CGARCH for BNB, underscoring the critical role of asymmetric volatility in these markets. This work advances existing research by offering a detailed comparison of GARCH-based approaches and practical insights for risk evaluation and portfolio optimization.
Senior Financial Markets Dealer, Nassau, The Bahamas, Vladyslav Yakymashko
This article investigates the phenomenon of volatility clustering in the cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH), through empirical time-series analysis. The study employs quantitative methods, including GARCH modeling, to identify persistent patterns in the price fluctuations of the two leading digital assets. The analysis is based on trading data over an extended period, encompassing both phases of high market turbulence and periods of relative stability. Adopting an interdisciplinary approach that integrates behavioral finance, econometrics, and financial market theory, particular attention is given to identifying autocorrelation, memory effects, and the structure of market shocks. The findings demonstrate that volatility clustering in BTC and ETH significantly differs from similar phenomena in traditional financial markets, largely due to their speculative nature, asset novelty, and the influence of both institutional and retail participants. The identified patterns enhance risk profiling for crypto assets and may be applied in hedging strategies, automated trading algorithm development, and investment portfolio optimization. Additionally, the study highlights the importance of accounting for both micro- and macroeconomic factors influencing market behavior. The article is intended for researchers in digital finance, risk managers, analysts, investors, and anyone examining unstable assets in conditions of high uncertainty and a rapidly changing informational landscape.
Frederic Haase, Tom Celig, Oliver Rath, Detlef Schoder
Abstract The emergence of cryptocurrencies and decentralized finance (DeFi) applications brings unique challenges, including high volatility, limited fundamental valuation methods, and significant informational reliance on social media. Consequently, traditional trading algorithms and decision support systems (DSS) often fall short in effectively capturing these dynamics, underscoring the need for tailored solutions. Recent research on sentiment analysis in cryptocurrency trading has provided mixed evidence regarding its predictive power, highlighting limitations in generalizability and reliability due to the inherent noise of social media content. Addressing these limitations, this study explores crowd-based trading signals, explicit buy and sell recommendations shared by users on social media platforms including X (formerly Twitter), Reddit, Stocktwits, and Telegram. We apply an event study methodology to analyze over 28,000 trading signals extracted using natural language processing (NLP) techniques based on large language models (LLMs). Our findings demonstrate that these explicit crowd-based signals significantly predict short-term cryptocurrency price movements, particularly for assets with lower market capitalization and recent negative returns. An out-of-sample trading strategy using these signals achieves superior risk-adjusted returns, outperforming both a standard cryptocurrency index (CCI30) and the S&P 500. Additionally, we uncover the role of automated accounts (signal bots) actively disseminating trading recommendations. This research advances literature by introducing a precise alternative to sentiment analysis, contributing to the understanding of social media as a distributed financial information environment, and raising theoretical considerations about algorithmic agency and trust. Practical implications span investors, social media platforms, and regulators.
Akinde Michael Ogunmolu, Emonena Patrick Obrik-Uloho, Oluwaseun Oladeji Olaniyi, Aisha Temitope Arigbabu ¡ 5 authors
This study investigates the systemic propagation of cyber risks between traditional financial institutions (TradFi) and decentralized finance (DeFi) infrastructures, focusing on oracles as critical conduits for contagion. Using publicly available datasetsâincluding MITRE ATT&CKÂŽ for Financial Services, the Global Cybersecurity Index (GCI), and the REKT.news exploit archiveâthe study applies frequency analysis, logistic regression, time-series event studies, and Principal Component Analysis with cluster modeling to quantify institutional vulnerabilities, model breach likelihood, and evaluate governance impacts. Empirical findings show that API interconnectivity and DeFi exposure increase breach probabilities by up to 3.7 times, while countries in Cluster 0, such as Singapore and Estonia, exhibit governance indices 24â28 points above average, correlating with lower systemic risks. Oracle-related incidents triggered over 150% volatility surges in TradFi-linked tokens like USDC and DAI, demonstrating oraclesâ role in cross-domain cyber risk transmission. The study recommends harmonizing cybersecurity governance frameworks across centralized and decentralized sectors, mandating periodic audits of oracle infrastructures, and developing integrated real-time threat monitoring systems to contain spillovers. These policy measures, alongside expanded cybersecurity workforce development, are essential to mitigate evolving cross-sector vulnerabilities. By combining rigorous empirical modeling with actionable recommendations, this research offers practical insights for policymakers, regulators, and cybersecurity professionals to strengthen resilience in the increasingly interconnected global financial ecosystem.
Central Bank Digital Currencies (CBDCs) represent a critical innovation in the era of Industry 4.0, combining the technological advancements of digital currencies with the regulatory oversight of central banks. Despite increasing interest, gaps remain in understanding how technical design choices influence CBDC integration into financial systems. This study addresses this gap by examining key technical characteristics of CBDCs across three critical dimensions through a systematic literature review: Infrastructure and Functionality, Access and Transfer Mechanisms, and Cross-Border Payments. The Infrastructure and Functionality dimension examines architectural models (one-tier vs. two-tier), and the integration of blockchain and Distributed Ledger Technology (DLT), non-DLT, and hybrid systems, with a focus on how these frameworks impact CBDC performance. The Access and Transfer Mechanisms dimension focuses on access models (token-based vs. account-based) and transfer methods (online vs. offline). The Cross-Border Payments dimension explores interoperability through three potential models: Compatible CBDC Systems, Linking Multiple CBDC Systems, and Single Multi-Currency Systems. By synthesizing insights from ongoing global CBDC projects such as Project Garuda, Project Jura, and e-CNY, this research develops a refined taxonomy that categorizes and maps technical design elements of CBDCs. The findings provide a comprehensive transformative mapping of CBDCâs technical aspects, supporting policymakers, regulators, and developers to navigate implementation challenges and achieve the goals of Industry 4.0. Future studies could further investigate specific use cases to optimize CBDC frameworks.
Andrei-Theodor Ginavar, Alexandra Conda, Daniel Traian Pele, Miruna Mazurencu-Marinescu-Pele ¡ 5 authors
Abstract This study examines the statistical characteristics of Bitcoin and the CRIX index through a dual analytical framework: Metcalfeâs network law and bubble dynamics via Log-Periodic Power Law (LPPL) modeling. The findings suggest that, over the medium to long term, Metcalfeâs lawâwhich posits that a networkâs value scales with the square of its user baseâserves as a valid approach for assessing cryptocurrency value. However, its applicability to Bitcoin in the short term remains uncertain. To analyse price dynamics during speculative bubbles, the DS LPPLS method was employed, enabling the identification of bubble phases and the estimation of potential regime shifts. Ultimately, the research concludes that while Metcalfeâs law holds true over longer time horizons, its reliability in short-term scenarios and under varying data regimes is considerably questionable.
Abstract The approval of Bitcoin ETFs by the Securities and Exchange Commission (SEC) on 01/11/2024 was an essential event for both the cryptocurrency market and the traditional financial system. Bitcoin ETFs work as a bridge between digital assets and traditional financial instruments, contributing to increased liquidity and attracting new institutional investors who were reluctant before due to regulatory and security concerns. This study assesses the impact of the approval of Bitcoin ETFs on the stability of the financial system, focusing on the correlations and the volatility spillover effects of Bitcoin and three major financial indices (S&P 500, Dow Jones Industrial Average, and Nasdaq-100). Using Pearson Correlation, Time-Varying Parameter Vector Autoregression (TVP-VAR) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, this research offers a comprehensive analysis of the influence of Bitcoin on the dynamics of market. The results show that, although the correlations between Bitcoin and stock market indices reached a peak in 2021, they dropped later, suggesting a gradual decoupling from traditional financial markets. However, after the launch of Bitcoin ETFs in 2024, the correlations with financial indices â especially with S&P 500 â started to rise again, suggesting a reintegration of Bitcoin into the traditional financial system. Contrary to initial expectations, the results obtained from data covering 90 days before and after the launch of Bitcoin ETFs donât show a significant increase in short-term correlations, which suggest a smooth adaptation of the market to these new financial instruments. In addition, although Bitcoin ETFs contribute to the stabilization of cryptocurrency volatility, they introduced new types of intra-day fluctuations, highlighting the need for an advanced strategy of risk management. The study concludes that, while Bitcoin ETFs contribute to the stability of financial markets, they introduce systemic risks which require continuous surveillance from the regulatory authorities. Long-term implications of the approval of Bitcoin ETFs remain uncertain, hence more research is needed in order to comprehensively assess the impact of these new financial instruments on the global financial stability.
Siang-Li Jheng, Alexandra Conda, Daniel Traian Pele, Wolfgang Karl Härdle
Abstract 2024 marks a significant milestone in integrating digital finance into the global financial landscape. The U.S. Securities and Exchange Commissionâs approval of Bitcoin and Ethereum ETFs signaled wider mainstream adoption. Shortly thereafter, Donald Trumpâs return to the presidency drove Bitcoin prices beyond $100,000. In light of these developments, we observe the rapid changes in cryptocurrency market prices, trends, and regulatory policies, which drive us to conduct a comprehensive review of cryptocurrencies assetâs literature and examine its robustness. Our study covers several themes: how cryptocurrencies fit into broader asset allocation strategies, techniques to create crypto-based indexes, current debates over speculative bubbles, and the evolution of valuation models to highlight the dual aspects of market opportunities and risks. Throughout our review, we compare previous studies with the latest data, seeking to determine which arguments continue to hold up and which require adjustment. Although digital assets have experienced multiple crashes, they often rebound more strongly than expected, making them a topic of intense debate among academics, regulators, and investors. We aim to assemble an organized summary of research findings, providing a comprehensive framework that unites historical evolution with recent shifts and future perspectives.
Cristina Dima, RÄzvan CÄtÄlin Dobrea, MÄdÄlina Ioana Moncea, Eduard Laurentiu Ion
Abstract For a long time, among the most controversial topics revolves around technology, which encompasses the financial landscape and changes the way we perceive and interact with money. The cause of this transformation is cryptocurrency - a revolutionary innovation that has captured the imagination of individuals and institutions around the world. For the less informed, investing in cryptocurrencies may seem like a game of chance, while for the younger ones, it represents a promising source of income for the future. The reasons for choosing the theme about cryptocurrencies can be motivated by several current factors such as: the topicality and relevance of cryptocurrencies, technological innovation, financial opportunities, regulations and public policies, social and cultural impact, but the main reason is the monetary future, which can become a significant part of the global monetary system.
The popularity of cryptocurrencies as alternative investments has grown in recent years. However, it remains unclear whether cryptocurrency investors behave irrationally in a similar way to emerging market investors. Using a systematic literature review, this study aims to compare the factors related to the presence of behavioural biases in the cryptocurrency and emerging stock markets. This study highlights similarities and differences between cryptocurrency and emerging stock market investor behaviour. Thus, the study's novelty arises from comparing the role of behavioural inclinations in cryptocurrency and emerging stock markets. The findings indicate that the small amount or lack of available information about small-cap emerging stocks or cryptocurrencies may reinforce investor sentiment and herding behaviour. The herding behaviour among investors in both markets may stem from following the most popular investment trends. Investors in cryptocurrency and emerging stock markets also tend to overreact to market sentiment and changes in market conditions. Extreme market conditions may affect the strength of herding behaviour, disposition effect, price clustering, anomalous behaviour, investor sentiment and uncertainty. Thus, cryptocurrency and emerging stock markets are informationally inefficient most of the time, whilst investorsâ irrationality may be more pronounced during certain periods. Furthermore, investorsâ behaviour in the cryptocurrency and emerging stock markets is more consistent with the adaptive market hypothesis than the efficient market hypothesis. This research suggests that cryptocurrency and emerging stock market investors should actively manage investment portfolios. Policymakers should be more concerned about information accessibility and quality, especially in the case of small-cap investment assets. JEL codes: G14;G15;G41
For any meaningful instructional delivery to take place, the teacher must clearly understand who the learners are: their strengths, weaknesses, environment, the goal of instruction, the pace to mention but a few.This process is better referred to as instructional analysis.This paper posits that instructional analysis, the foundational phase of instructional design, serves as the indispensable basis for achieving high-quality and impactful instructional delivery.It explores the multifaceted components of instructional analysis, including learner analysis, context analysis, content/task analysis, and performance analysis, demonstrating how insights derived from these processes directly inform strategic decisions regarding instructional strategies, media selection, and assessment design.Drawing upon established instructional design models and contemporary research, this paper highlights the benefits of thorough instructional analysis in optimizing learning outcomes, enhancing engagement, and ensuring the relevance and efficiency of educational interventions.It also addresses practical challenges in conducting instructional analysis, offering considerations for educators and designers in diverse learning environments, particularly within the evolving nature of education in the 21st century.
In this study, the fractal structure, efficiency, and long memory features of Bitcoin are investigated according to different investment horizons. The study utilized daily returns from 01.01.2017 to 22.11.2023, applying the maximum overlap discrete wavelet transform, Rescaled Range (R/S) analysis, and volatility models. The analysis results revealed a deviation of Bitcoin returns from the average and a negative correlation, indicating a lack of permanent behaviour in the series. The analysis demonstrates the rejection of the efficient market hypothesis and reveals a chaotic structure in the Bitcoin market. Furthermore, we observed a hyperbolic rate of decrease in returns at long-term investment horizons due to information shocks. This indicates that past returns can predict future returns. This suggests that instead of the efficient market hypothesis, the fractal market hypothesis is valid due to the existence of recurring trends. Finally, we determined the most appropriate volatility models for Bitcoin. The analysis shows that information shocks in Bitcoin returns at medium- and long-term investment horizons decrease over time, and past returns can predict future returns. However, volatility and information shocks are transitory at short- and medium-term investment horizons but can vary. All analysis methods yield consistent and compatible results, suggesting their potential extension to other cryptocurrency markets beyond the Bitcoin market.
Dimitris Kastoris, Dimitris Papadopoulos, Konstantinos C. Giotopoulos
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka-Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka-Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge-Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Moreover, our method outperforms ARIMA models in terms of accuracy, showcasing its effectiveness for crypto market forecasting. The entire framework, including neural network training and Runge-Kutta integration, was implemented in MATLAB.
Farrukh Nawaz, Mirzat Ullah, Ohannes George Paskelian, Umar Nawaz Kayani ¡ 5 authors
Abstract This study delves into the substantial fluctuations in returns of leading cryptocurrenciesâBitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)âalongside major global stock indices, including the NASDAQ Composite, S&P 500, and Euronext ENX. Utilizing the swap variance (SwV) analysis estimation approach, the research examines these entities based on their respective market capitalizations, assessing market jumps, integrated volatility, and realized volatility as key metrics for evaluating abnormal returns. The findings reveal that economic crises trigger an increased occurrence of market jumps in both cryptocurrency and stock markets, contributing to heightened volatility. This phenomenon underscores market inefficiencies and challenges the Efficient Market Hypothesis (EMH) framework. While positive jumps are more frequent, negative jumps are significantly larger in magnitude, supporting theories such as asymmetric volatility, the leverage effect, prospect theory, and loss aversion. Notably, the cryptocurrency market exhibits greater volatility compared to traditional stock markets, particularly during periods of heightened economic policy uncertainty. These insights hold profound implications for investors, portfolio managers, and policyâmakers, offering a nuanced understanding of the intricate dynamics within these financial ecosystems. By uncovering the interplay between market jumps, volatility, and economic uncertainty, the study provides valuable guidance for navigating the complexities of modern financial markets.
Susanna Levantesi, Gabriella Piscopo, Alba Roviello
Accurate estimation of cryptocurrency market volatility is crucial for investors. The Crypto Volatility Index (CVI) was developed to measure the marketâs expectations for the 30-day implied volatility of Bitcoin and Ethereum to address the growing demand for reliable predictions. This study explores the relationship between the CVI and the volatility of traditional financial markets, including the Gold Volatility Index (GVZ), the Crude Oil Volatility Index (OVX), and the S&P500 Volatility Index (VIX). Three other variables are also analyzed: the USD to EUR exchange rate (USDEUR), the Federal Reserve interest rate (FED), and the NASDAQ index. The aim of the research is explanatory: the input variables and the CVI are observed contemporaneously to catch the complex relation between them. Using Pearson correlation, distance correlation, and mutual information, we demonstrate the presence of non-linear relationships between some variables in the dataset. Explanatory analysis is conducted using machine learning techniques, specifically the Random Forest (RF) algorithm and Gradient Boosting Machines (GBM) to account for these potential non-linear interactions. These methods are better suited than standard linear models for identifying complex relationships. In particular, the RF algorithm reaches a better level of accuracy than GBM and avoids overfitting.