Blockchain Papers

Ziyad Baali

No abstract is available for this record.

Carmine Russo

No abstract is available for this record.

Cleave Otieno

No abstract is available for this record.

Eman Abdullah I Alghufaili

The cryptocurrency market has undergone unprecedented growth and transformation, driven by technological advancements, global crises, and shifts in financial paradigms. This thesis comprises three empirical studies that collectively enhance our understanding of diverse cryptocurrency types, namely Shariah-compliant cryptocurrencies, green cryptocurrencies, stablecoins (fiat-backed, gold-backed, crypto-backed), and traditional cryptocurrencies, during major global shocks (the COVID-19 pandemic, the Russia-Ukraine war, and the FTX exchange collapse), with a focus on their resilience, safe-haven properties, market connectedness, portfolio performance and stability.The first empirical chapter investigates the resilience and safe-haven characteristics of Shariah-compliant cryptocurrencies relative to conventional ones. Using wavelet coherence and DCC-GARCH, quantile, and threshold regressions as robustness, it reveals that Shariah-compliant cryptocurrencies offer superior short- and medium-term safe-haven properties, particularly during geopolitical turmoil. Portfolio optimisation demonstrates that these assets deliver higher risk-adjusted returns during shocks, underscoring their potential in crisis-resilient portfolio construction.The second empirical chapter analyses the dynamic connectedness of green and non-green cryptocurrencies with traditional and environmental assets during the COVID-19 pandemic and the Russia-Ukraine war. Using a TVP-VAR framework and three portfolio strategies, namely minimum variance, correlation, and connectedness, the study shows that green cryptocurrencies, despite heightened volatility during crises, enhance hedging effectiveness when combined with strategic assets such as carbon futures, gold, and energy commodities.The third empirical chapter evaluates the impact of the FTX collapse on the return and volatility of stablecoins and traditional cryptocurrencies using Difference-in-Differences and event study methods. The findings reveal that while stablecoins were more resilient than traditional cryptocurrencies, they were not immune to systemic shocks. Notably, gold- and crypto-backed stablecoins did not outperform fiat-backed ones during crises, underscoring the importance of liquidity and transparency over collateral type.Overall, this thesis provides novel empirical insights into the evolving cryptocurrency landscape, offering actionable implications for investors, regulators, and policymakers navigating digital assets in times of systemic stress.

Burcu Kapar, Mabruk Billah, Mohammad Talha

No abstract is available for this record.

Pablo Azar, Sergio Olivas, Nish D. Sinha

This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack’s transactions are recorded—becoming public information—while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices.

manyu kong, Shunqi Zhang

No abstract is available for this record.

Nicolin Decker

No abstract is available for this record.

Imran Hussain Shah

No abstract is available for this record.

Arzé Karam

No abstract is available for this record.

Kaston Chen

This paper introduces a model for forecasting daily Bitcoin returns using data sourced from Yahoo Finance. Additionally, I propose a simple trading strategy that leverages the model's forecasts for Bitcoin trading.

Ethan Flowerday, Neil Gandal, Hanna Hałaburda, Eric Olson · 5 authors

No abstract is available for this record.

Mirzat Ullah, Kazi Sohag

This study examines the connectedness among Bitcoin, gold, equity, bonds, and dollar to Ruble exchange rate volatility in the context of new developments during Russia Ukraine conflict using daily data from January 1, 2018, to May 30, 2023. Three GARCH estimation models are utilised to capture the volatility spillover effect among the underlined assets, and assess for the hedging, diversification, and safe haven properties of assets in the context of Russian financial market. The results indicate that the Bitcoin exhibits hedging ability that enables investors to diversify the risk among the underline financial assets. In addition, VaR and CVaR estimations are employed to estimate potential losses in the portfolio during the crisis, where we observe significant increase in Bitcoin investments during crisis, where negative news has a stronger impact compared to positive news, which underscores the importance of prudent asset allocation for risk mitigation. The study provides notable policy implications within the context of the ongoing crisis between Russia and Ukraine.

Abdullah Amberkhani, Harshitha Bolisetty, Ranjith Narasimhaiah, Ghulam Jilani · 9 authors

No abstract is available for this record.

Po‐Sheng Ko, Kuo‐Shing Chen

In the AI era, we contribute to the literature by uncovering that the price dynamics of most AI tokens could be fully characterized by the processes driven by fractal Brownian motion, which robustly supports the principles of the fractal markets hypothesis. Using rescaled range (R/S, i.e., Fractal) analysis and the wavelet coherence technique, we analyzed daily log-returns from seven major AI tokens and Bitcoin over the period 2020–2024. Our empirical results rejected the weak form of the Efficient Market Hypothesis (EMH), supporting the Fractal Market Hypothesis (FMH) as a better explanation for the dynamics of AI crypto tokens. More importantly, the log-returns of all analyzed AI tokens, each exhibiting a Hurst exponent exceeding 0.58, provided evidence of persistent behavior and an inherent tendency toward positive price trajectories. These results implied that Fractal analysis can enhance investors' ability to model return dynamics and identify potential appreciation in AI tokens, particularly as short-term trading activity intensifies during episodes of elevated market turbulence. Finally, this work reveals that AI tokens exhibit strong coherence patterns with Bitcoin, varying across time and frequency domains, suggesting Bitcoin's limited role as a hedge against AI tokens. Crucially, this study highlights the significant role of AI tokens as potential safe-haven assets during market turmoil, offering valuable insights for portfolio diversification for crypto investors with intuitive and plausible results that carry strong policy implications.

Naji Mansour Nomran, Razali Haron, Abdelkader Laallam, Ali Ateeq · 7 authors

Cryptocurrencies have emerged as a transformative force across various sectors of the global economy, particularly in financial markets, where they influence asset classes and market dynamics. In this context, Asia's leadership in both cryptocurrency adoption and Islamic finance provides a unique opportunity to assess whether Islamic stock markets outperform their conventional counterparts amid cryptocurrency volatility. This study employs advanced econometric techniques, including panel unit root tests, Johansen-Fisher cointegration, pairwise Granger causality tests, and regression analysis, to empirically examine the influence of cryptocurrencies on the performance of Islamic and conventional stocks. Weekly data from 13 Asian countries spanning 2016–2019 are analyzed, with a focus on two distinct periods: before and after the 2017–2018 cryptocurrency crash. The findings reveal bidirectional significant causality between conventional stock returns and cryptocurrency returns. In contrast, Islamic stock returns exhibit a unidirectional influence on cryptocurrency prices, with no reciprocal effect observed across all panels. The findings indicate that during both overall and pre-crash periods, cryptocurrency returns positively affect Islamic and conventional stock markets, with Islamic indices experiencing a stronger impact. However, post-crash, both conventional and Islamic stocks suffer negative consequences from cryptocurrency fluctuations, with conventional stocks experiencing more pronounced losses, while Islamic stocks display greater resilience. This suggests that investor sentiment and risk appetite in Islamic markets differ from those in conventional markets, particularly during periods of cryptocurrency instability. Overall, our findings indicate that rising cryptocurrency returns, especially post-crash, may divert investors from stock markets across Asia, with conventional markets being more affected than Islamic markets. The study offers valuable insights for investors, policymakers, and regulators, emphasizing that conventional stock market investors face greater exposure to cryptocurrency risks. It advocates for the implementation of robust policies to mitigate these risks and recommends expanding future research to encompass other regions and incorporate additional control variables.

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.

Shaista Peerzada, Sangeeta Verma

No abstract is available for this record.

Cathy W. S. Chen, Po‐Hui Chen, Ying‐Lin Hsu

ABSTRACT Cryptocurrencies exhibit high volatility, emphasizing the importance of accurately measuring tail risk in their markets. This research incorporates a threshold‐switching mechanism into Taylor's ES‐CAViaR models that unveil features such as asymmetry and jump phenomena. These enhancements effectively capture the diverse tail risks of cryptocurrencies while enabling the simultaneous forecasting of both Value‐at‐Risk (VaR) and Expected Shortfall (ES). The proposed models incorporate two types of functions to address the VaR and ES nexus with the option to use the rolling standard deviation of returns as a short‐term volatility proxy as a regressor. We estimate the parameters and forecast tail risk within a Bayesian framework. Taking the two largest cryptocurrencies by market capitalization, Bitcoin and Ethereum, we assess the one‐step‐ahead forecasting performance over a four‐year out‐of‐sample period using a rolling window approach. The comparative results from backtests and five scoring functions among eight competing models support the conclusion that models with a threshold mechanism capture the tail risk of cryptocurrencies more accurately than other risk models.

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.