Blockchain Papers

Abdulkadri Toyin Alabi, Abdullahi Ishola

The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and CO₂ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and CO₂ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.

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.

Michael Michaelides, Niraj Poudyal

Abstract Value‐at‐Risk (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 time‐heterogeneous Student's t autoregressive model outperforms standard models commonly used by practitioners.

Çağlar Sözen

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 cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—by 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 datasets—including MITRE ATT&CK® for Financial Services, the Global Cybersecurity Index (GCI), and the REKT.news exploit archive—the 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 24–28 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 oracles’ 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.

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: Metcalfe’s network law and bubble dynamics via Log-Periodic Power Law (LPPL) modeling. The findings suggest that, over the medium to long term, Metcalfe’s law—which posits that a network’s value scales with the square of its user base—serves 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 Metcalfe’s law holds true over longer time horizons, its reliability in short-term scenarios and under varying data regimes is considerably questionable.

Paul Cristian Donoiu

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 – especially with S&P 500 – 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 don’t 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 Commission’s approval of Bitcoin and Ethereum ETFs signaled wider mainstream adoption. Shortly thereafter, Donald Trump’s 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 asset’s 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, Răzvan Cătălin Dobrea, Mădălina 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.

Mateusz Skwarek

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 investors’ irrationality may be more pronounced during certain periods. Furthermore, investors’ 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

Abiola Idowu

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.

Aslan Aydoğdu, Hafize Meder Çakır

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.

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 market’s 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.

Arun Kuna

The decentralized finance ecosystem has fundamentally transformed traditional financial paradigms by eliminating intermediaries and enabling permissionless financial services through smart contracts deployed on blockchain networks. However, the explosive expansion has simultaneously exposed critical vulnerabilities in existing quality assurance methodologies, which were originally designed for centralized systems with predictable failure modes and controlled environments. Traditional quality assurance approaches rely heavily on static testing protocols, periodic audits, and human-mediated verification processes that prove fundamentally incompatible with the dynamic, autonomous nature of DeFi ecosystems. The inherent characteristics of DeFi platforms create a unique risk landscape that demands innovative approaches to quality assurance, particularly given the complex interconnected protocol dependencies across major DeFi applications. This article introduces a novel dynamic risk-adaptive quality assurance framework specifically engineered for DeFi platforms that transcends traditional static analysis by implementing a self-adjusting architecture capable of continuously monitoring, evaluating, and responding to emerging threats in real-time. The framework integrates artificial intelligence-driven risk prediction algorithms with behavioral analytics to create a comprehensive defense mechanism that evolves alongside the threat landscape. Through establishing dynamic risk thresholds and implementing automated response protocols, this system represents a paradigm shift toward autonomous, intelligent quality assurance in decentralized financial ecosystems, addressing critical security challenges through four interconnected layers, including data ingestion, AI-driven risk prediction, dynamic threshold management, and automated response mechanisms.

Abdullah A. Aljughaiman, Mosab I. Tabash, Suzan Sameer Issa, Abdulateif A. Almulhim

Most prior studies explain cross-country volatility interconnectedness without accounting for exogenous global uncertainty factors that influence equity returns. This study is the first to explore how major global uncertainty indicators such as U.S. and European financial market uncertainty indices (CBOE volatility index (VIX), VSTOXX-50), Global Financial Stress Indices (FSI) and Bitcoin Sentiment Indices (BSI) transmit shocks to the conditional volatility of Gulf Cooperation Council (GCC) stock markets. Using a novel ‘Extended Joint’ time-varying parameter vector autoregression (TVP-VAR) connectedness framework, the analysis addresses rolling-window limitations, enhances robustness to outliers, accommodates structural shifts and explains the shock transmission mechanism for the overall investment horizon. To capture transitory (short-term) and enduring (long-term) shock transmission channels from global uncertainty indicators toward the GCC financial system, a frequency-domain TVP-VAR is also employed. Furthermore, for the portfolio optimization, we also employ the hedge ratio and optimal portfolio weight strategy based on the DCC-GARCH-t copulas. Findings reveal that the conditional volatility of equity markets in Oman, Qatar, Saudi Arabia and the UAE is more sensitive to shocks from global uncertainty indicators such as VIX, VSTOXX-50 and the FSI, while Bahrain’s market shows relatively lower exposure. Kuwait’s equity market volatility exhibits the highest long-term sensitivity to FSI, VIX and VSTOXX-50, whereas the UAE demonstrates the highest sustained exposure to VIX and VSTOXX-50. Results from the DCC-GARCH-t copula model indicate that in stable periods (pre-COVID-19), optimized portfolio allocations significantly improved diversification, reducing risk by up to 83%. However, during financial stress events like COVID-19, hedge ratio strategies provided more effective risk mitigation, with reductions ranging from 3% to 43%.

Aviral Vaish

The rise of cryptocurrencies over the past decade has transformed the global financial landscape, introducing new paradigms in investment, value storage, and monetary exchange. This study investigates the role of cryptocurrencies—specifically Bitcoin (BTC) and Ethereum (ETH)—as alternative investment assets within modern portfolio frameworks. As digital currencies continue to gain legitimacy and acceptance among retail and institutional investors, it becomes imperative to examine their financial performance, volatility characteristics, and correlation with conventional asset classes such as equities, bonds, and commodities. This research adopts a hybrid methodological approach, combining rigorous quantitative analysis with qualitative review. Using historical market data from 2015 to 2024, it evaluates key performance indicators such as average returns, standard deviation, Sharpe and Sortino ratios, Value at Risk (VaR), and maximum drawdown. It further explores the utility of cryptocurrencies in enhancing portfolio efficiency through diversification benefits, while also considering risk mitigation through dynamic asset allocation and rebalancing. The study extends beyond price metrics to include macroeconomic factors, such as inflation trends and monetary policy shifts, which influence crypto markets. It also addresses behavioral finance phenomena—including herd behavior, market sentiment, and media impact—that contribute to the observed volatility and price surges. The emergence of decentralized finance (DeFi), stablecoins, and central bank digital currencies (CBDCs) are also discussed to contextualize the evolving ecosystem and its implications for future investment strategies. Key findings indicate that while cryptocurrencies have historically outperformed traditional assets in terms of absolute returns, they also exhibit significantly higher volatility and downside risk. Despite these risks, their low to moderate correlation with conventional financial instruments enhances their value as diversification tools in modern portfolios. However, the study cautions that this benefit may diminish during times of extreme market stress when cross-asset correlations tend to rise. Moreover, the research highlights critical regulatory, technological, and environmental challenges associated with crypto adoption, including inconsistent global regulations, concerns over energy-intensive proof-of-work systems, and vulnerabilities in smart contracts. These factors underscore the need for robust governance frameworks and investor education to support sustainable growth in the digital asset market. In conclusion, the paper asserts that cryptocurrencies can serve as high-risk, high-reward components of a diversified portfolio, particularly for investors with higher risk tolerance and a long-term investment horizon. The future integration of cryptocurrencies into mainstream finance will depend largely on regulatory clarity, technological innovation, and the maturation of supporting infrastructure such as custody services, derivative markets, and institutional-grade investment vehicles

João Pedro Malim Franco, Márcio Poletti Laurini

This study compares two approaches for measuring Conditional Value-atRisk (CoVaR), emphasizing the role of high-frequency intraday data in assessing systemic risk within financial systems. The first approach, AB CoVaR, estimates the risk of an asset Y conditional on another asset X being exactly at its Value-at-Risk (VaR) threshold. In contrast, the GE CoVaR refines this measure by capturing the risk of Y when X exceeds its VaR threshold, thereby accounting for more extreme scenarios and larger potential losses. To estimate these CoVaR measures, we employ high-frequency data sampled at five-minute intervals from major cryptocurrencies, including Bitcoin, Ethereum, Ripple, Solana, and Binance Coin. The results indicate that the GE CoVaR approach systematically yields higher risk estimates and exhibits superior predictive performance when applied to intraday data. Moreover, the analysis reveals strong interconnectedness among cryptocurrency returns. Bitcoin and Ethereum emerge as the primary sources of systemic risk, whereas Solana and Binance Coin are the most heavily affected assets. These findings underscore the granular risk dynamics captured through intraday analysis.

Chikashi Tsuji

Abstract Despite its importance, there has been little research on the relationship between Bitcoin’s risk and returns. Therefore, it is necessary to investigate the risk–return trade-off of Bitcoin. In the existing limited literature, a negative risk–return relationship in Bitcoin for high-frequency intraday time-series data has been reported. In this paper, we use lower time–frequency data and suitable models for the data frequency to examine the risk–return trade-off of Bitcoin. Specifically, this paper examines the time-series volatility risk–return trade-off of Bitcoin using standard Markov switching (MS) and MS–GARCH models with weekly Bitcoin data from 2010 to 2024. Consequently, the study reveals several new findings. Firstly, the volatility risk–return trade-off relationship is identified for Bitcoin’s log returns. Secondly, the risk–return trade-off is also found for Bitcoin’s simple returns. Thirdly, the risk–return trade-off is uncovered for Bitcoin’s risk premiums as well. Fourthly, the study shows that the risk–return trade-off relationships for Bitcoin’s log returns, simple returns, and risk premiums hold true for all business days from Monday to Friday, indicating the robustness of the results. Furthermore, the study presents significant interpretations, implications, and discussion. We emphasize that we have discovered positive weekly risk–return relationships for Bitcoin using Markov switching models for the first time. This demonstrates the novelty of our work.

Punit Anand, Anand M. Sharan

Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA.

Wajeeha Badar, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani · 6 authors

Predicting the price of Bitcoin is crucial, primarily because of the market’s rapid volatility and non-linear environment. For enhanced prediction of the price of Bitcoin, this research proposed a novel interpretable hybrid technique that combines long short-term memory (LSTM) networks with convolutional neural networks (CNN). Deep variational autoencoders (VAE) are used in the stage of preprocessing to determine noticeable patterns in datasets by learning features from historical Bitcoin price data. The CNN-LSTM model additionally implies Shapley additive explanations (SHAP) to promote interpretability and clarify the role of various features. For better performance, the methodology used data cleaning, preprocessing, and effective machine-learning techniques. The hybrid CNN + LSTM model, in collaboration with VAE, obtains a mean squared Error (MSE) of 0.0002, a mean absolute error (MAE) of 0.008, and an R-squared (R2) of 0.99, based on the experimental results. These results show that the proposed model is a good financial forecast method since it effectively reflects the complex dynamics of primary changes in the price of Bitcoin. The combination of deep learning and explainable artificial intelligence improves predictive accuracy as well as transparency, thus qualifying the model as highly useful for investors and analysts.

Matthew Brigida

A common assumption in cryptocurrency markets is a positive relationship between total-value-locked (TVL) and cryptocurrency returns. To test this hypothesis we examine whether the returns of TVL-sorted portfolios can be explained by common cryptocurrency factors. We find evidence that portfolios formed on TVL exhibit returns that are linear functions of aggregate crypto market returns, that is they can be replicated with appropriate weights on the crypto market portfolio. Thus, strategies based on TVL can be priced with standard asset pricing tools. This result holds true both for total TVL and a simple TVL measure that removes a number of ways TVL may be overstated.