Saudi Cultural Missions Theses & Dissertations
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Item Restricted Forecasting OPEC Basket Oil Price and Its Volatilities Using LSTM(University College London, 2024-09) Almazyad, Sulaiman; Hamadeh, LamaThe global economy is greatly affected by oil prices, which have an impact on everything from consumer goods prices to transportation expenses. Forecasting these prices accurately is crucial for energy security, company strategy, and economic planning. Traditional statistical models such as ARIMA and SARIMA have been used for such forecasts, but struggle with the non-linear patterns inherent in oil price movements. This research explores the use of Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Network (RNN) built to manage longterm dependencies, in predicting the OPEC reference basket oil price and its associated volatility, ultimately improving the accuracy of these forecasts. The model is built upon historical datasets of the OPEC Reference Basket (ORB), and its efficacy is assessed using a variety of performance indicators, including RMSE, MAE, and MAPE. The outcomes reveal that the LSTM model is15 0Item Restricted What Do Investors Care About in Cryptocurrency Markets? Evidence from ESG Ratings and NFTs(University of East Anglia, 2024-09) Alsultan, Sarah Abdulrahman; Markellos, Raphael; Kourtis, ApostolosWhile cryptocurrencies have seen limited adoption as a medium of exchange, they have been recognised as a new class of investment assets. A broader range of investors, including institutional investors, has shown growing interest in cryptocurrency and digital assets. Therefore, this thesis contributes to the literature by thoroughly examining digital technologies as investment assets through three empirical studies. The first study explores whether investors prefer blockchains with strong Environmental, Social, and Governance (ESG), using a novel dataset of blockchain ESG scores. The findings reveal a time-varying preference for high-ESG blockchains. The top-rated blockchains outperform lower-rated ones during favourable market conditions and optimistic market sentiment but underperform during times of negative sentiment. Our findings also indicate that high-ESG blockchains exhibit higher market volatility. Furthermore, governance and environmental factors have the strongest influence on investor preferences among the three ESG dimensions. The second study examines NFTs as a relatively new asset class that is not yet fully understood, particularly in terms of risk modelling. It evaluates and compares the forecasting performance of various GARCH models in estimating NFT market volatility across different time horizons. The selected models include GARCH(1,1), IGARCH(1,1), EGARCH(1,1), GJR- GARCH(1,1), and TGARCH(1,1). The dataset comprises three major NFT categories, six NFT token platforms, and major cryptocurrencies, including Bitcoin and Ethereum. Empirical evidence shows that different models perform better depending on the asset type and forecast horizon. The findings highlight the highly volatile nature of NFT markets. The third study assesses the impact of visual attractiveness on NFT market prices. Prior art literature has demonstrated the role of aesthetics in influencing art prices. Given the similarities between NFTs and traditional art, this study investigates whether aesthetics similarly impact NFT prices. The empirical analysis focuses on one of the largest NFT collections, CryptoPunks, by applying a hedonic pricing model. We employ quantitative aesthetic measures to capture aspects of NFT art, including colourfulness, brightness, colour intensity, and texture. Our results reveal a significant impact of visual aesthetics on determining NFT prices. The results indicate that more colourful and visually complex NFTs are associated with higher prices, while brighter and more saturated NFTs are associated with lower prices.23 0Item Restricted Growth and Volatility Relationships Reexamined: The Role of Aggregation(Southern Illinois University, 2024-05-21) Khan, Haya; Morshed, Akm MahbubAbstract: This dissertation studies the relationship between output growth rate and its volatility. This study sheds light on International, Regional, and Development Economics literature. In the first chapter, we revisit the relationship between output growth rate and its volatility using cross-section techniques for our panel data set from 60 countries from 1970 to 2019. In addition to the conventional volatility measurement of the standard deviation, we incorporate the higher moments, such as skewness and kurtosis, as volatility measures. Higher moments further sharpen our understanding of the volatility and growth rate relationship. We also examine the role of the irreversibility of investment, a purported proximate factor for increased volatility in theory but not applied to empirical models, on the growth rate. We find that a higher level of the irreversibility of investment tends to reduce the growth rate. In the second chapter, we examine the growth-volatility relationship covering manufacturing activities at the two-digit level in 32 countries. In particular, we conduct a comprehensive analysis to reveal the long-term relationship between output growth rate and volatility over 1970 – 2019 within countries and across sectors. We have data for each manufacturing subsector for each country over a long period. We have redefined the growth rate and volatility measures with alternative definitions such as cross-country and cross-sector across time. This offers additional advantages from an econometric perspective, as the large cross-sectional dimension is beneficial when estimating the determinants of growth rate. Moreover, our study assesses the evolution of the long-term relationship between economic sectoral growth rate and sectoral volatility over time. Overall, we find that growth rate and volatility are negatively related, with a few exceptions. The third chapter investigates the relationship between regional growth rate and volatility in U.S. state regions. We use disaggregated data for manufacturing activities over the period 1977 – 2021. We find a significant positive relationship between sectoral volatility and GDP per worker growth rate across the U.S. states regions, meaning that manufacturing volatile sectors for the U.S. are growing faster. This finding is also robust in including additional control variables in the analysis, thus confirming that volatility does not capture the effect of other potential determinants of GDP growth in the manufacturing sectors. We further examine how policy structure and geographical similarity affect regional growth rates, in which we distinguish between the Democrat and Republican Parties and Coastline and Non-Coastline states. We find that the growth rate and volatility relationship has been weaker for Democrat-leading states and geographically more open states (states with a coastline). This suggests that the growth rate and volatility relationship can be altered by having a supporting fiscal policy or having a more open economy.8 0Item Restricted 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