Saudi Cultural Missions Theses & Dissertations
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Item Restricted Essays on Blockchain Technology(Saudi Digital Library, 2025-05-15) Alkhars, Kazem Abdulkarim; Radhakrishnan, AbiramiThis dissertation consists of two essays on blockchain technology adoption and application. The first essay empirically examines the factors affecting the adoption of cryptocurrency at the individual level using the theoretical lenses of the Theory of Technological, Personal, and Environmental factors and Diffusion of Innovation Theory. The existing literature emphasizes the need for broader adoption by individuals. Therefore, in this large-scale, theory-driven study, I investigate the factors influencing the intention to adopt cryptocurrencies at an individual level. The findings show that the main technological factors impacting the intention to adopt cryptocurrency are complexity, perceived benefits, and trialability. The main personal factors are compatibility, individual risk propensity, and trust. Finally, it reveals the role of positive market sentiments as the main environmental factor. The second essay uses the Dynamic Capabilities View as a theoretical lens to empirically investigate the impact of blockchain technology usage on a firm’s resilience to supply chain disruptions and operational performance. Witnessing the negative impacts of disruptions such as COVID-19 on supply chain performance, this research addresses this gap. While prior studies present blockchain chain technology as a potential solution a firm can use to enhance resilience, not one study has shed light on how blockchain usage can impact firms' resilience and operational performance. The findings provide empirical evidence of the relationship between blockchain technology usage and supply chain capabilities. Second, it also provides empirical evidence of the indirect relationship between blockchain technology usage and a firm's resilience and operational performance in the face of environmental uncertainty.11 0Item Unknown A FRAMEWORK FOR BEHAVIOURAL INTENTIONS TO ADOPT CRYPTOCURRENCY AMONG PUBLIC UNIVERSITY STUDENTS IN THE KINGDOM OF SAUDI ARABIA(Saudi Digital Library, 2024-11-28) ALOMARI, ALI SAEED A; Lee Abdullah, NasuhaMany individuals believe that the cryptocurrencies have the potential to disrupt the traditional financial system. This is evidenced in several commercial and scholarly studies that show a significant investment made by individuals in different cryptocurrencies like Bitcoin and Ethereum despite the associated risks such as volatility in value and a lack of regulations. Although, cryptocurrency is a rapidly growing digital asset and its investment potential remain uncertain. Both pros and cons of cryptocurrencies can cause problems for individuals. Therefore, it is essential to understand individuals’ intention toward cryptocurrency to prevent them from any financial loss or for the authorities to make informed decision in policymaking, strategies for adoption of cryptocurrency. By examining individual intentions, this study seeks to provide valuable insights into the behavioural drivers and barriers affecting cryptocurrency adoption. The study used a quantitative research method with developing a survey questionnaire. Using a purposive sampling technique, the study targeted the students in the Saudi Arabian universities. The study used SPSS software for demographic and descriptive statistics and Smart PLS for testing validity, reliability and research hypotheses. To achieve this, past research studies have identified several factors that influence behavioural intentions of individuals to adopt cryptocurrency. However, those studies are conducted either in a specific context e.g., country or are conceptual. Despite progress in understanding cryptocurrency adoption, financial literacy and technology readiness's moderating roles in culturally unique contexts like Saudi Arabia still needs to be explored. Studies have focused on direct factors like awareness, effort expectancy, and security without adequately exploring how individual capabilities and preparedness affect these relationships. By studying the relationship between financial literacy, technology readiness, and cryptocurrency adoption, this study seeks to fill these gaps and better understand Saudi Arabian user intentions. To fill this gap, this study aims to understand the factors influencing behavioural intentions of public university students to adopt cryptocurrencies in the Saudi Arabia. Public university students are chosen as the population for this study because they represent the youth in Saudi Arabia who are early adopters of innovation like cryptocurrency. The study uses an extended version of the existing Unified Theory for Acceptance and Use of Technology (UTAUT) as a foundational theory. The results of the study show that awareness, performance expectancy, effort expectancy, social influence, and security significantly influence the behavioural intentions of public university students to adopt cryptocurrency in Saudi Arabia. The results also show that the technology readiness and financial literacy moderate the relationship of influencing factors and behavioural intention. The study contributes theoretically and practically into the existing body of knowledge. The study not only replicates the findings of the past studies on individuals’ intention to adopt cryptocurrency, but it also extends and confirms the UTAUT model in the context of Saudi Arabia. The results of the study would benefit the policy makers, practitioners, regulators, and government authorities in developing better policies, and strategies for the adoption of cryptocurrency in Saudi Arabia.6 0Item Unknown Spillover-Based Portfolio Management: Bayesian Diebold & Yilmaz Spillover Applications in Cryptocurrency Market(University of Tokyo, 2025) Bukhary , Husam; Gento, MOGIThis study evaluates the performance of various Bayesian priors in modeling and assessing financial and economic Diebold and Yilmaz spillover networks through simulations using the posterior distribution of spillover effects across multiple priors. A key contribution of this research is the introduction and validation of graph similarity analysis within spillover networks, demonstrating that even when the overall fit of the spillover is suboptimal, the interconnections between variables of interest are accurately captured in terms of directionality, albeit with slight discrepancies in magnitude. Building on this insight, we apply the Diebold and Yilmaz spillover approach to develop a novel portfolio optimization strategy that integrates Hierarchical Risk Parity (HRP) with the Louvain method, utilizing the optimized spillover values. This innovative method outperforms traditional HRP techniques when applied to both synthetic data and real cryptocurrency market data, providing a robust and efficient framework for managing interconnected financial assets.11 0Item Unknown Forecasting the Volatility of Bitcoin and Ethereum(University of Sussex, 2024) AlSulami, Rahaf Abbad; Zhao, YuqianCryptocurrencies, particularly Bitcoin and Ethereum, have introduced new dynamics to global financial markets, most notably through their extreme price volatility. As a result, the accurate forecasting of cryptocurrency volatility has become critical for traders, investors, and regulators. This study examines the forecasting performance of two prominent time series models—the Heterogeneous Autoregressive (HAR) model and the Autoregressive Moving Average (ARMA) model—by applying them to high-frequency data from 2022. The results indicate that while the ARMA model performs reasonably well in stable market conditions, it struggles to account for the sharp volatility spikes that are common in cryptocurrency markets. In contrast, the HAR model demonstrates stronger predictive accuracy, particularly during periods of heightened volatility, as it captures the persistent and multi-scale nature of cryptocurrency price movements. These findings suggest that the HAR model is a more effective tool for forecasting volatility in highly volatile environments like those seen in the cryptocurrency market, offering valuable insights for risk management and strategic decision-making.16 0Item Unknown Comparative Analysis of Stablecoin Market Capitalization(University of Sussex, 2024) Alshammari, Naif; Alexander, CarolThis paper takes a closer look at the market capitalization trends for the five most popular stablecoins: Tether, USD Coin, Dai, TrueUSD, and PAX Gold. Stablecoins are digital currencies whose price volatility is minimized since their value is pegged to an underlying asset, such as a fiat currency or commodity. Despite the ever-growing importance, very little research has been done to explain the factors affecting their market capitalization and what this means for the general financial market. The design of the research applies an empirical study with a quantitative approach in analyzing historical data of the market capitalization for these stablecoins over a defined period. In the analysis, it identifies major determinants of the market capitalization: regulatory developments, rates of adoption, technological advancements, and macroeconomic conditions. The study further shows comparative stability and growth patterns among the chosen stablecoins and gives insight into which performs better relatively in the cryptocurrency marketplace. The results indicate there were major differences in the trends of market capitalization for studied stablecoins, thus evidence of the impact from exogenous variables such as regulatory change and changes in market demand. In the light of this, the study finds that it is very important to stakeholders, including investors, policy makers, and financial analysts, that these factors are understood well to make informed decisions within the fast-changing landscape of digital currencies. This work thus adds value to the literature base with meaningful insights into the dynamics of stablecoin market capitalization, with implications for the future of digital financial assets.44 0Item Unknown Risk and Uncertainty in Cryptocurrency Markets(University of East Anglia, 2024-04-23) Alsamaani, Abdulrahman; Kourtis, Apostolos; Markellos, RaphaelThis dissertation consists of three kinds of research. Each one has its purpose and aim to achieve. The first research tries to discover the most effective approach for forecasting the volatility of cryptocurrency returns utilising high-frequency data that can predict the volatility of dominant and less notable cryptocurrencies. The GARCH, IGARCH, EGARCH, GJR-GARCH, HAR, and LRE models were investigated, and univariate and comprehensive regression were used. Regarding univariate regression results, the HAR model beat the other models when forecasting one day ahead, while the EGARCH model outperformed the other models when forecasting seven and thirty days ahead. In addition, the HAR + EGARCH duo beat the other model couples when forecasting one, seven, and thirty days. Aside from the primary study, the out-of-sample analysis yielded conflicting results. These results will benefit investors, portfolio managers, and other financial professionals. The second study seeks to investigate the relationship between cryptocurrency returns and uncertainty indices along with assessing the impact of the Covid-19 pandemic period on both indices and cryptocurrency returns, determining which index has the most significant influence on cryptocurrency market results, and determining which indices pair has the most significant influence on cryptocurrency market returns. Ten cryptocurrency returns, as well as eight uncertainty indices, were investigated. The Quantile Regression, Multivariate-Quantile Regression, and Granger Causality tests were used. According to the Quantile Regression results, the Cryptocurrency Policy Uncertainty index and the Cryptocurrency Price Uncertainty index considerably impact cryptocurrency returns. On the other hand, the other indices have no influence on cryptocurrency returns. The Multivariate-Quantile Regression findings demonstrated that when the cryptocurrency market experiences a bull wave, the UCRY Policy Index + Central Bank Digital Currency Attention Index combination strongly impacts cryptocurrency returns. Nonetheless, when the cryptocurrency market has a bull run, the UCRY Policy Index and the Cryptocurrency Environmental Attention (ICEA) index combination considerably impact cryptocurrency gains. During the crisis, most of the overall sample findings were verified. These insights will benefit investors, portfolio managers, and policymakers. The third research strives to find the best model for forecasting the covariance matrix of cryptocurrency returns. To achieve this purpose, five models were thoroughly examined: BEKK, Diagonal BEKK, DCC, Asymmetric DCC, and LRE are all examples of BEKK. To assess prediction accuracy and capacity, three essential criteria were used: Euclidean distance (LE), Frobenius distance (LF), and the multivariate quasi-likelihood loss function (LQ). The LRE model outperformed the other models, predicting daily and weekly frequencies more accurately. Furthermore, the Mean Squared Error (MSE) and Mean Absolute Error (MAE) loss functions were used for validation. Except for LQ, the findings were in line with the forecasting criteria. These findings have significant implications for investors and portfolio managers aiming to enhance their risk management techniques. By utilizing the knowledge provided, they may be able to make better-informed decisions to lower portfolio risk.42 0