/library/oar/handle/123456789/118824 2025-10-26T21:52:21Z /library/oar/handle/123456789/140268 Title: The impact of an increase in foreigners on the waiting times in healthcare in Malta Abstract: The increasing presence of foreign residents in Malta has raised questions about its impact on essential public services, including healthcare. As Malta’s only major public hospital, Mater Dei Hospital’s Emergency Department (ED) is critical to meeting the healthcare needs of both locals and foreigners. With the continuous growth of the foreign population, concerns have emerged regarding potential strains on the system, particularly in terms of increased waiting times in emergency care. This study investigates the impact of foreign residents on waiting times in Malta’s healthcare system, specifically at Mater Dei Hospital’s Emergency Department (ED). The objective is to assess whether foreign residents experience longer waiting times than Maltese residents and to identify the main factors contributing to these delays. Using data spanning from 2017 to 2023, the research employs an Ordinary Least Squares (OLS) regression model to examine various determinants of ED waiting times, including nationality, age, gender, arrival type, triage level, and the need for admission. The methodology involved comprehensive data cleaning and preparation of over 600,000 anonymous observations from Mater Dei’s records to fully adhere to ethical considerations. Separate regressions were run for the years 2017, 2020, and 2023 to account for temporal variations, particularly the effects of the COVID-19 pandemic. The results showed that although nationality has a statistically significant impact on waiting times, its influence is relatively low compared to other variables. These findings underscore the need for policy measures to manage ED demand, particularly for non-native populations, and improve the overall efficiency of Malta’s healthcare system. Description: M.Sc.(Melit.) 2024-01-01T00:00:00Z /library/oar/handle/123456789/138256 Title: Statistical arbitrage in commodity markets through PCA and OPTICS clustering Abstract: This thesis explored the application of statistical arbitrage strategies on commodity related assets. The asset universe consisted of a diversified basket of 55 assets spanning three asset classes: commodity futures, commodity-linked equities, and commodity currencies. Two strategies were employed: a traditional PCA-based approach and a method that additionally involved clustering the assets using OPTICS. Over the period from 2014 to 2024, both strategies generated slight yet consistent returns. Notably, the strategy incorporating OPTICS clustering outperformed, both in absolute returns and also risk adjusted performance, suggesting that the inclusion of a clustering step may provide additional benefits in such strategies. Moreover, when tested on a post COVID-19 period, the PCA approach failed to generate returns, while the OPTICS strategy remained slightly profitable. Additional results are presented on the characteristics of the residual parametrisation, as well as a insights into which asset clusters and sectors performed the best. Any returns attributable to both strategies proved to be uncorrelated both with a broad based commodity index and also the S&P500. Description: M.A.(Melit.) 2024-01-01T00:00:00Z /library/oar/handle/123456789/138255 Title: A study on whether quantitative trading strategies can continuously outperform humans using different strategies Abstract: Gone are the days when traders on Wall Street chaotically shouted bids and offers across trading floors, giving way to advanced trading systems that execute trades almost instantly as exponential technological growth has reshaped the financial industry. This study conducts a comprehensive comparative analysis of modern quantitative, rule-based autonomous investment strategies against more traditional, fund manager-led strategies within ETFs across three distinct markets: equities, fixed-income, and foreign exchange. Quantitative strategies utilise algorithms to make data-driven decisions, minimising human biases and emotions. In contrast, discretionary strategies rely on fund managers’ proprietary research, market insights, and intuition from experience. The two strategies were tested over a five-year period encompassing both stable and volatile markets arising from the unpredictability of the COVID-19 pandemic, ensuring a robust analysis of varied market conditions. While discretionary ETFs did, at times, achieve higher returns, they did so with higher volatility. This indicates that the prospect of higher returns is significantly influenced by the fund manager's skill, making consistent performance less predictable compared to algorithmic approaches. Quantitative strategies consistently outperformed their discretionary counterparts in a multifaceted series of tests focusing on risk-adjusted returns. The findings support the growing popularity of quantitative strategies in an increasingly automated environment, offering insights to investors and investment managers alike. Challenges to more traditional financial concepts also emerge, suggesting that markets might not be as efficient as previously believed, contributing to ongoing academic debates. Despite being limited by a five-year time frame since the legislative changes took place, this study lays a foundation for future research as the longer-term effects become observable. To conclude, the balance between algorithmic efficiency and human insight will shape the future of fund management, and the level of optimal oversight over the models remains to be determined. This work illustrates the benefits and potential provided by quantitative strategies and suggests a need for a fresh perspective on traditional investment approaches, hinting at a well-developed hybrid of the two. The era of traders shouting orders across trading floors may be over, but the drive to secure the best investment strategies has only grown more intense, propelled by technology’s continuous evolution. Description: M.A.(Melit.) 2024-01-01T00:00:00Z /library/oar/handle/123456789/138253 Title: Empirical analysis of cryptocurrency price movements and their relationship to other assets : a VAR & rolling VaR approach Abstract: Cryptocurrency is a relatively new digital instrument often referred to "digital gold," yet our understanding of its behaviour remains limited. The scarcity of both data and research makes it difficult to study its dynamics and implications which poses challenges for this emerging asset class, especially when compared to gold, which benefits from extensive scholarly attention. Existing studies in circulation delve into the interconnectedness of Bitcoin with various asset classes such as commodities, currencies, and Equities focusing on themes such as volatility spillovers, returns, and hedging strategies. These papers utilize modelling techniques such as DY models, GARCH models, regression analysis, and VAR-based models in which most papers do not consider the efficiency of different cryptocurrencies along with the dynamic relationships in a post- recession period & economic expansion period, resulting in literature gaps. A blend of methodologies was implemented within this research to enhance the analysis effectiveness. This incorporates a mixed methodology approach of VAR, Rolling VaR, the Diebold-Yilmaz Connectedness Framework, Rolling VAR, GFEVD, Granger Causality, IRF’s and Correlation Analysis with respect to volatility spillovers, dynamic correlation patterns, and responses to economic shocks across various periods. The findings confirm Hypothesis 1, showing volatility spillovers between cryptocurrencies, blockchain equities are high with the DY suggesting a higher spillover effect in periods of systematic events such as COVID 19 & lower spillover during post-recession periods. Hypothesis 2 is partially accepted, as dynamic correlation being lower post-recession with Granger causality results showing Blockchain Equities holding predictive power of cryptos for 3-4 Year periods & Other Cryptos holding predictive power for LT (7 Years). Hypothesis 3 is partially accepted, indicating cryptocurrencies shock reaction are most impactful with other sectoral-adjacent assets & recessionary periods containing more instability and volatility. Impacts in post- Recession periods for non-crypto adjacent was identified to be lower. These results hold regulatory importance in terms of highlighting the potential for volatility mitigation strategies in cryptocurrencies. Policymakers may consider promoting legislation in favour of stablecoins, regulating specific cryptocurrency disclosures & regulating Defi Finance as if they were banks. Additionally, the research could serve as a valuable guide for individual and institutional investors in managing their cryptocurrency based on economic cycles & interpreting them as a hedge or a diversifier accordingly. Description: M.A.(Melit.) 2024-01-01T00:00:00Z