Guo, Min. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 66-69). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Notation and Model --- p.8 / Chapter 2.1 --- Notation --- p.9 / Chapter 2.2 --- Classical News vendor Model --- p.11 / Chapter 2.3 --- The Price of the Put Option --- p.12 / Chapter 2.4 --- Extended Models with the Option --- p.13 / Chapter 3 --- Literature Review --- p.16 / Chapter 4 --- Objective I ´ؤ Maximizing Expected Profit --- p.24 / Chapter 4.1 --- Single Decision Variable Case: K = Q --- p.24 / Chapter 4.2 --- Two Decision Variable Case: K ≤Q --- p.25 / Chapter 4.3 --- Summary of the Chapter --- p.28 / Chapter 5 --- Objective II ´ؤ Maximizing the Probability of Achieving A Target Profit --- p.30 / Chapter 5.1 --- Single Decision Variable Case: K = Q --- p.30 / Chapter 5.2 --- Two Decision Variable Case: K ≤ Q --- p.37 / Chapter 5.3 --- Numerical Examples --- p.38 / Chapter 5.4 --- Summary of the Chapter --- p.41 / Chapter 6 --- Objective III ´ؤ Minimizing Profit Variance --- p.43 / Chapter 6.1 --- Minimizing Profit Variance through R --- p.44 / Chapter 6.2 --- Minimizing Profit Variance through K --- p.51 / Chapter 6.2.1 --- Special Case R = s --- p.54 / Chapter 6.3 --- Summary of the Chapter --- p.60 / Chapter 7 --- Conclusion --- p.63 / Bibliography --- p.69

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The accurate estimation and forecasting of volatility is of utmost importance for anyone who participates in the financial market as it affects the whole financial system and, consequently, the whole economy. It has been a popular subject of research with no general conclusion as to which model provides the most accurate forecasts. This thesis enters the ongoing debate by assessing and comparing the forecasting performance of popular volatility models. Moreover, the role of key parameters of volatility is evaluated in improving the forecast accuracy of the models. For these purposes a number of US and European stock indices is used. The main contributions are four. First, I find that implied volatility can be per se forecasted and combining the information of implied volatility and GARCH models predict better the future volatility. Second, the GARCH class of models are superior to the stochastic volatility models in forecasting the one-, five- and twenty two-days ahead volatility. Third, when the realised volatility is modelled and forecast directly using time series, I find that the HAR model performs better than the ARFIMA. Finally, I find that the leverage effect and implied volatility significantly improve the fit and forecasting performance of all the models.

There have been a number of technological advances in the last ten years, which has resulted in the amount of data generated in organisations increasing by more than 200% during this period. This rapid increase in data means that if financial institutions are to derive significant value from this data, they need to identify new ways to analyse this data effectively. Due to the considerable size of the data, financial institutions also need to consider how to effectively visualise the data. Traditional tools such as relational database management systems have problems processing large amounts of data due to memory constraints, latency issues and the presence of both structured and unstructured data The aim of this research was to use data analysis and information visualisation techniques (IV) to support the effective analysis of large financial data sets. In order to visually analyse the data effectively, the underlying data model must produce results that are reliable. A large financial data set was identified, and used to demonstrate that IV techniques can be used to support the effective analysis of large financial data sets. A review of the literature on large financial data sets, visual analytics, existing data management and data visualisation tools identified the shortcomings of existing tools. This resulted in the determination of the requirements for the data management tool, and the IV tool. The data management tool identified was a data warehouse and the IV toolkit identified was Tableau. The IV techniques identified included the Overview, Dashboards and Colour Blending. The IV tool was implemented and published online and can be accessed through a web browser interface. The data warehouse and the IV tool were evaluated to determine their accuracy and effectiveness in supporting the effective analysis of the large financial data set. The experiment used to evaluate the data warehouse yielded positive results, showing that only about 4% of the records had incorrect data. The results of the user study were positive and no major usability issues were identified. The participants found the IV techniques effective for analysing the large financial data set.

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This dissertation is a collection of papers investigating objectives and incentives in financial markets. The first essay (Chapter 2) deals with the endogenous determination of credit history, credit-worthiness, loans and efforts by borrowers over time. A financial market with adverse selection and moral hazard is analyzed. Facing the adverse selection, lenders are not able to offer separate contracts to different types of borrowers. However, knowing borrowers' credit histories, lenders are able to assign different credit worthiness to borrowers that have different credit histories, and offer different contracts to different groups. It is shown that if borrowers' credit rating is too low, they make low effort to repay their debts. As a borrower acquires a good credit history and has his credit-rating upgraded above a certain point, it becomes worthwhile for him to choose high effort. A low quality borrower may make high effort in early periods in order to build up a good credit history and obtain better terms in the future contracts then shift back to the low effort even though his project continues to succeed when he approaches the end of his life. / Ph. D.

Law, Tak Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 38-39). / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Literature Review --- p.4 / Chapter 2.1 --- Brief Overview --- p.4 / Chapter 2.2 --- Determinants of the Target Debt Ratio --- p.7 / Chapter 2.3 --- Adjustments Toward the Target Ratio --- p.9 / Chapter Chapter 3. --- Methodology --- p.12 / Chapter 3.1 --- Estimation of the Target Leverage Ratio --- p.12 / Chapter 3.2 --- Factors affecting the Long-term Adjustment --- p.16 / Chapter 3.3 --- Persistence and Reversal of Effects --- p.19 / Chapter Chapter 4. --- Expected Signs of Variables --- p.22 / Chapter 4.1 --- Estimation of the Target Ratio --- p.22 / Chapter 4.2 --- Effects of Firm Histories --- p.24 / Chapter 4.2.1 --- Pecking order theory --- p.24 / Chapter 4.2.2 --- Tradeoff theory --- p.25 / Chapter 4.2.3 --- Market Timing Theory --- p.25 / Chapter Chapter 5. --- Data --- p.27 / Chapter Chapter 6. --- Empirical Findings --- p.29 / Chapter 6.1 --- Estimation of the Target Ratio --- p.29 / Chapter 6.2 --- Factors Affecting the Long-term Adjustment --- p.31 / Chapter 6.3 --- Persistence of the Effects --- p.33 / Chapter 6.4 --- Reversal of the Effects --- p.35 / Chapter Chapter 7. --- Concluding Remarks --- p.37 / References --- p.38 / Tables --- p.40

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2017 / The study examined the relationship between oil price shocks, volatilities and stock indices in the African emerging markets. The ARDL and Bivariate BEKK GARCH models are used in this study. The countries examined are Botswana, Egypt, Mauritius, Morocco, Namibia, Nigeria, South Africa, Tanzania, Kenya, Ghana, Tunisia, and the MSCI’s World Index. The study shows a bidirectional relationship between oil price shocks for Nigeria and the MSCI, but unidirectional flow from oil price shocks to Botswana, Egypt, Mauritius, Morocco, Namibia, South Africa, Tanzania, Kenya, Ghana, and Tunisia. In addition, there is evidence of unidirectional volatility spill over from oil returns to Botswana, Namibia, Tanzania, Mauritius and Kenyan, Nigeria, Tanzania, Kenya and Ghana. Finally, the study found bidirectional volatility between oil and index returns in MSCI, South Africa, and Tunisia. / MT2017