Blockchain Papers

Samar S. Alharbi, Shoaib Ali, Muhammad Shahid Rasheed, Mina Sami

No abstract is available for this record.

Richard Beainy, Cesar Kamel

No abstract is available for this record.

Sumin Li, Rentao Wang, Yudong Wan, Jincheng Hu

The accurate prediction of cryptocurrency prices remains challenging due to their high volatility, which is driven by complex factors including market dynamics, macroeconomic conditions, and investor sentiment. Traditional econometric models, standalone machine learning methods, and deep learning architectures have shown limited effectiveness in capturing both short-term variations and long-range dependencies. To address these limitations, a hybrid deep learning model, L-FED, is proposed by integrating long-short term memory (LSTM) network with the FEDformer architecture, augmented by sentiment analysis. A parallel framework is adopted to enable bidirectional information interaction through local-global collaborative learning. A comprehensive feature engineering approach is also introduced, incorporating historical trading data, technical indicators, sentiment features, and LSTM-derived short-term guiding prices. The experimental results demonstrate that L-FED outperforms the existing baseline models in terms of prediction accuracy. On the Bitcoin and Ethereum datasets, L-FED achieves improvements of 16% and 12.8% in RMSE and MAPE, respectively, for Bitcoin, and 11.6% and 6.4% for Ethereum. Furthermore, sentiment analysis using the CryptoBERT model enhances price prediction accuracy by 19% and 2.9%, respectively, attributable to its pre-training on a large, domain-specific cryptocurrency corpus. Our code and datasets are publicly available at https://github.com/lsm-2024/L-FED.

Steven Msomi, Andile Nyandeni

The study analyses the spillover effects of cryptocurrencies to establish if cryptocurrencies possess any hedging abilities for South African markets. Different and ZAR/USD exchange rate, Gold and Johannesburg All Share Index (JSE-ALSI) were studies between the period 01/01/2016 to 31/12/2020. The study employed the Baba, Engle, Kraft and Kroner (BEKK) and multiplicative dynamic conditional correlation (MDCC) multivariate generalised autoregressive conditional heteroscedasticity (GARCH) models. The results of the study indicate the presence of volatility spillovers from the cryptocurrencies to the South African markets through the JSE market and the Rand. A bidirectional shock transmission between the JSE market and Bitcoin and a unidirectional spillovers from Dogecoin and Litecoin to JSE was found. The study also proved that cryptocurrencies are not yet at a stage where they can replace Gold as a hedge tool. The results show predominantly low correlations between the South African market (JSE and The Rand) and cryptocurrencies. Suggesting the presence of diversification and hedging abilities of cryptocurrencies.

Veronika Vinogradova, Mariya Gubareva

No abstract is available for this record.

Jacek Karasiński

Abstract The objective of this study was to examine the level and behaviour of the weak-form efficiency of the 16 most capitalised cryptocurrencies using intraday data. The study employed martingale difference hypothesis tests utilising the rolling window method. The predictability of high frequency returns varied over time. For most of the time, the cryptocurrencies were unpredictable. Nevertheless, their weak-form efficiency appeared to decrease along with an increase in frequency. In general, most cryptocurrencies were marked by high levels of unpredictability. However, there were some significant differences between the most and least efficient ones. To exploit market inefficiencies, investors should focus on higher frequencies. Higher frequencies should also be a concern to regulators when it comes to ensuring market efficiency.

Francisco Jareño, María de la O González, José M. Almansa

BackgroundThis study examines the impact of interest rate fluctuations on the returns of traditional, 'green', and 'stable' cryptocurrencies from April 2019 to April 2023. Bitcoin, Cardano, and Tether represent these categories due to their market significance.MethodsUsing quantile regression (QR), the study analyzes the impact of interest rate shocks on cryptocurrency returns during bullish and bearish market periods. It also decomposes nominal interest rates into real interest rates and inflation expectations. The sample period is divided into stable and rising interest rate sub-periods for robustness.ResultsThe results show that cryptocurrency returns are more sensitive to interest rate fluctuations in both bullish and bearish periods. The sensitivity varies across cryptocurrency types, with Cardano acting as a hedge against inflation risk during bearish periods.ConclusionsThe results support the research hypotheses and provide insights into the behavior of cryptocurrencies under different market conditions. These findings help portfolio managers and policymakers to make informed decisions in a digital financial environment. Future research should explore the interactions between cryptocurrencies and other financial markets.

Rubaiyat Ahsan Bhuiyan, Tanusree Chakravarty Mukherjee, Kazi Md. Tarique, Ch. Zhang

In order to provide hedging strategies on the financial risks involved in such crises and also taking into consideration that two cryptocurrency prices have been impacted by Russia-Ukraine war uncertainties apart from the COVID-19 pandemic, we applied wavelet analysis along with the multivariate DCC-GARCH process to scrutinize the return–volatility causal relationship among gold price and six stock market indices, including three well-established emerging economy (EE) ones. We achieved a more balanced and complete picture by considering data for the time period July 28, 2016 to December 30, 2022. The events of analysis were crises in the Chinese market, a trade war between the USA and China), caused by the COVID-19 pandemic, after which came global recession Ⅲ (a Russia-Ukraine war); next, part Ⅳ — the peak of the global energy crisis. The findings generally indicated that when a sudden shock sometimes like this happens (or in a pandemic), there is no one other than Ethereum for all investors in emerging and developed markets to find a safe haven or protect themselves, while Bitcoin acts as less safe. We also showed Gold as a hedge in Global Crises and as a Hedge and Weak Safe Haven Against Geopolitical Tension. Last, investors in the paired joint oil stock have a greater benefit but can gain only if they hold shorter-term investments. As for volatility, arguably, only bitcoin is to be observed as the least volatile among all other variables. Our findings suggested that stock markets are the source of volatility spillover to all others while prior work has established mixed evidence during the pandemic, the most crucial and recent periods, respectively.

Reza Roshanpour, Aliakbar Khosravinejad, Gholamreza Abbasi, Amirreza Keyghobadi

We propose a GA-optimized self-attention LSTM (SAG-LSTM) for multi-asset price forecasting and evaluate it on daily series of crude oil, gold, and Bitcoin, augmented with trading volumes (01-Apr-2021 to 30-Dec-2024). The model marries LSTM sequence learning with a multi-head self-attention layer and a post-attention gating block; a genetic algorithm tunes key hyperparameters (learning rate, hidden size, epochs). Using a 30-day horizon and standard preprocessing with lagged features, we benchmark SAG-LSTM against SA-LSTM and vanilla LSTM on MSE, RMSE, MAE, andR2, supplemented by error-trend and residual diagnostics, a forecast coherence score, and inter-asset dynamic/cross-correlation analyses. SAG-LSTM consistently dominates the baselines across assets: out-of-sampleR2rises to 0.90 for oil, 0.94 for gold, and 0.88 for Bitcoin, with visibly flatter error profiles and tighter, near-zero residuals. Inter-asset analyses show time-varying contemporaneous correlations but weak lead–lag effects, clarifying when co-movement is episodic rather than persistent. The largest gains occur in oil, reflecting more structured fundamentals; improvements for gold and Bitcoin are material but tempered by regime shifts and sentiment-driven jumps. Training time is higher due to GA search (≈2,121 s), but inference is fast (≈0.40 s), making the approach suitable for infrequent retraining with near-real-time scoring. Our findings highlight the value of hybrid, optimization-aware deep architectures for medium-horizon forecasting while underscoring the limits of price-volume inputs in sentiment-sensitive markets. These results offer actionable guidance for practitioners and a roadmap for future research and policy.

Delia Elena Diaconaşu, Riadh Benammar

No abstract is available for this record.

Seyedeh Fatemeh Mottaghi, Bertram I. Steininger

No abstract is available for this record.

Mohammadhossein Lashkaripour, Seyed Mehdi Hosseini, Rizwan Ahmed

Bitcoin contributes to global carbon emissions on a scale comparable to entire countries in order to secure its decentralized network. This exposes Bitcoin to climate policies aimed at reducing emissions. This paper develops a general equilibrium framework to examine how the stringency of climate policy affects Bitcoin’s valuation and its relationship with the equity market. Our theoretical analysis delivers a key insight: a transition from a lenient to a stringent climate policy increases the conditional correlation between Bitcoin and equity returns, thereby compromising Bitcoin’s appeal as a hedge or diversifier against equity market volatility. Empirical evidence supports this theoretical prediction.

Josué Thélissaint

No abstract is available for this record.

Jobaer Hossain

No abstract is available for this record.

nasir khan khattak, Khaled Guesmi, Tong Su, Brian M. Lucey

No abstract is available for this record.

Prashant Joshi

In this study, we examined the regime-dependent dynamics and interrelationships among major cryptocurrencies, Bitcoin (BTC), Ethereum (ETH), and Monero (XMR), using high-frequency one-minute data from January 2020 to April 2025. To capture the presence of latent structural shifts without assuming Markovian transitions, we employed a Gaussian Mixture Model (GMM), which flexibly clustered distributions into two, empirically distinct regimes. Regime-specific Vector Autoregressive (VAR) models were then estimated to analyze interdependencies, spillovers, and shock transmission mechanisms across these digital assets. In the calm regime, the return dynamics were primarily self-driven, with limited cross-asset responses. Conversely, the volatile regime exhibited stronger and more persistent interlinkages, with BTC consistently acting as the principal transmitter of shocks to ETH and XMR, while ETH acts as a secondary transmitter, whereas XMR remains largely a risk recipient, absorbing external shocks with limited feedback into the system. These findings were corroborated through impulse response functions and forecast error variance decompositions, which consistently revealed asymmetric interdependence structures across the regimes. The Granger causality indicated more stable and statistically significant causal relationships in the calm regime than in the volatile regime. Furthermore, the Bai-Perron structural break tests confirmed the absence of significant deterministic breaks in the return series, reinforcing the validity of the GMM-based regime identification. These findings have practical implications for investors, regulators, and risk managers when modeling contagion and developing risk management strategies in cryptocurrency markets, especially during periods of heightened volatility.

Rajbeer Kaur, Parveen Kumar, Magdalena Radulescu, Sharif Mohd · 5 authors

Abstract In recent decades, the rising challenges posed by climate change have prompted investors to take a keen interest in green assets and incorporate them into their portfolios to achieve optimal returns. Therefore, this article explores the static and dynamic connectedness between renewable energy stocks (solar, wind, and geothermal), green cryptocurrencies (Stellar, Nano, Cardona, and IOTA), and agricultural commodities (wheat, cocoa, coffee, corn, cotton, sugar, and soybean) using the TVP-VAR (time-varying parameter vector autoregression) framework offering novel empirical evidence for investors and portfolio managers. The connectedness is examined across two distinct sub-samples: during COVID-19 and post-COVID-19 times. Because the relevant connectedness can have implications for diversification benefits, we proceed with the computation of optimal weights, hedge ratios, and hedge effectiveness using the DCC-GARCH model. The main findings are as follows: We first find that green cryptocurrencies particularly Cardona and Stellar exhibit the highest spillovers to the network and wind energy stock has the least connectedness with the other markets. Second, the dynamic NET spillover indices reveal that cotton, cocoa, and coffee are consistently net receivers over the entire period except in the beginning of the pandemic. Third, renewable energy stocks exhibit diverse positions implying that the impact of the pandemic has varied significantly across the sectors. Finally, agricultural commodity depicts greater weights in the pandemic period under scoring the benefit of a diversified portfolio consisting of agriculture and green assets.

Renhong Wu, Yuantao Fang, Md. Alamgir Hossain

This study aims to explore the dynamic relationships among economic policy uncertainty (EPU), Bitcoin trading activity, and the NASDAQ index over the period from January 2, 2014, to March 21, 2023. Employing the dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) model, this research reveals significant time‐varying interdependencies between EPU and financial markets, with a specific focus on the Bitcoin sector. This paper extends the literature by examining EPU’s influence on trading volume and volatility spillovers during different market regimes and crisis events, including the COVID‐19 pandemic. The results demonstrate that the correlations between EPU and both Bitcoin and the NASDAQ index are dynamic and sensitive to market phases, with stronger effects observed during bull markets and heightened volatility during the pandemic. The findings provide new empirical evidence on the evolving role of EPU in shaping investor behavior and financial asset comovement. This study offers practical implications for investors, regulators, and policymakers, especially in designing risk management strategies under uncertainty.

Luís Costa, Elisabete Vieira, Mara Madaleno

Purpose: This work analyzes whether cryptocurrencies significantly influence Euronext stock returns. Design/methodology/approach: To this end, this quantitative research analyzes companies from 4 Euronext financial markets between 2017 and 2022 using the panel data methodology. The Generalized Method of Moments (GMM) methodology was also used to make the analysis more robust. Findings: This study concluded that Bitcoin and Ethereum positively and statistically significantly influence Euronext stock returns. Their notoriety caused them to lose the safe haven characteristics they displayed in a more embryonic phase and led them to be influenced by the same systemic factors that affect the stock market. Originality/value: The results of this study are immensely important for private and institutional investors investing in Euronext stocks and looking to diversify their portfolios. Keywords: Stock Returns; Euronext; Bitcoin; Ethereum; Cryptocurrencies DOI: https://doi.org/10.58869/EJABM11(1)/06

Gabriel Borrego Rold aacute n

No abstract is available for this record.

J M de Almeida, Tiago Gonçalves

• Cryptocurrencies serve as alternative safe havens during geopolitical conflicts. • Cryptocurrencies exhibit increased liquidity and interconnectedness during war periods. • Crypto assets gain significance as strategic tools amid global economic sanctions. This study examines the role of cryptocurrencies in modern War, specifically during the Russia-Ukraine conflict. Utilizing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model, the research assesses the dynamic financial behaviors of cryptocurrencies, focusing on changes in liquidity, safe haven status, and their use in circumventing economic sanctions. The analysis distinguishes financial behaviors across three distinct phases: Pre-Conflict, Conflict, and financial sanctions periods, highlighting the interaction between cryptocurrencies and traditional financial markets. The findings indicate shifts in the role of cryptocurrencies from net transmitters to net receivers of spillovers in both returns and volatility, particularly during the financial sanctions phase. This study provides insights into the integration of cryptocurrencies with traditional financial assets and their potential impact on local economies during military conflicts. The results document the increased liquidity and interconnectedness of cryptocurrencies during military conflict periods and explore their potential use in evading sanctions and supporting War efforts.

Taegyum Kim, Hyeontae Jo, Woohyuk Choi, Bong‐Gyu Jang

ABSTRACT This paper aims to improve Bitcoin price direction prediction using a CNN‐LSTM model that incorporates various relevant indicators, such as stock market indices, commodity indices, and interest rates. Separate models are trained for predicting price up and down direction and combined to enhance prediction accuracy. We utilize binary classification models to independently analyze the impact of different features, verified through explainable artificial intelligence techniques. Additionally, an investment strategy based on our model is proposed and compared with traditional strategies, specifically focusing on maximum drawdown relative to the S&P500 buy‐and‐hold strategy. Results suggest that our strategy offers potential for stable investment in Bitcoin, showcasing its value as a financial asset. This study demonstrates the role of deep learning in Bitcoin price direction prediction and investment strategy development and contributes to future research on cryptocurrency forecasting and investment approaches.

Murray A. Rudd, Dennis Porter

We refine a bottom-up, quantity-clearing framework of Bitcoin price formation that couples its fixed 21-million-coin cap with plausible demand growth and execution behavior. This approach relies on first-principles economic supply-and-demand dynamics rather than assumptions about anticipated Bitcoin price appreciation, its price history, or its potential effectiveness in demonetizing other asset classes. We considered five key high-level factors that may affect price determination: level of market demand; intertemporal investment preferences; fiat-denominated withdrawal sensitivity; initial liquid supply; and daily withdrawal levels from liquid supply. With a goal of both increasing understanding of the impacts of price drivers and developing probabilistic forecasts, we show two models: (1) a baseline to assess the impacts of parameter changes, alone and in combination, on Bitcoin price trajectories and liquid supply over time and (2) a Monte Carlo simulation that incorporates uncertainty across a range of uncertain parameterizations and presents probabilistic price and liquid supply forecasts to 2036. Our baseline model highlighted the importance of liquid supply and withdrawal sensitivity in price impacts. The Monte Carlo simulation results suggest a 50% likelihood that Bitcoin price will exceed USD 5.17 M by April 2036. Generally, prices from the low single millions to the low tens of millions per Bitcoin by 2036 emerge under broad parameter sets; hyperbolic paths to higher price levels are relatively rare and concentrate when liquid supply falls near or below BTC 2 M and withdrawal sensitivity is low. Our results help locate where right-tail risk and disorderly market outcomes concentrate and suggest that policy tools are available to help guide trajectories.

Filip Stefaniuk, Robert Ślepaczuk

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL, and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.