by Wai Chi-kin. / Abstract also in Chinese / Bibliography: leaves 174-175 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1982

Tang Him John. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 76-80). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 1.1.1 --- Linear Time Series --- p.2 / Chapter 1.1.2 --- Mixture Models --- p.3 / Chapter 1.1.3 --- EM algorithm --- p.6 / Chapter 1.1.4 --- Model Selection --- p.6 / Chapter 1.2 --- Main Objectives --- p.7 / Chapter 1.3 --- Outline of this thesis --- p.7 / Chapter 2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.9 / Chapter 2.1 --- Introduction --- p.9 / Chapter 2.1.1 --- "AR, MA, and ARMA" --- p.10 / Chapter 2.1.2 --- Stationarity --- p.11 / Chapter 2.1.3 --- ARCH and GARCH --- p.12 / Chapter 2.1.4 --- Gaussian mixture --- p.13 / Chapter 2.1.5 --- EM and GEM algorithms --- p.14 / Chapter 2.2 --- Finite Gaussian Mixture ARMA-GARCH Model --- p.16 / Chapter 2.3 --- Estimation of Gaussian mixture ARMA-GARCH model --- p.17 / Chapter 2.3.1 --- Autocorrelation and Stationarity --- p.20 / Chapter 2.3.2 --- Model Selection --- p.24 / Chapter 2.4 --- Experiments: First Step Prediction --- p.26 / Chapter 2.5 --- Chapter Summary --- p.28 / Chapter 2.6 --- Notations and Terminologies --- p.30 / Chapter 2.6.1 --- White Noise Time Series --- p.30 / Chapter 2.6.2 --- Lag Operator --- p.30 / Chapter 2.6.3 --- Covariance Stationarity --- p.31 / Chapter 2.6.4 --- Wold's Theorem --- p.31 / Chapter 2.6.5 --- Multivariate Gaussian Density function --- p.32 / Chapter 3 --- Finite Mixture-of-Expert ARMA-GARCH Model --- p.33 / Chapter 3.1 --- Introduction --- p.33 / Chapter 3.1.1 --- Mixture-of-Expert --- p.34 / Chapter 3.1.2 --- Alternative Mixture-of-Expert --- p.35 / Chapter 3.2 --- ARMA-GARCH Finite Mixture-of-Expert Model --- p.36 / Chapter 3.3 --- Estimation of Mixture-of-Expert ARMA-GARCH Model --- p.37 / Chapter 3.3.1 --- Model Selection --- p.38 / Chapter 3.4 --- Experiments: First Step Prediction --- p.41 / Chapter 3.5 --- Second Step and Third Step Prediction --- p.44 / Chapter 3.5.1 --- Calculating Second Step Prediction --- p.44 / Chapter 3.5.2 --- Calculating Third Step Prediction --- p.45 / Chapter 3.5.3 --- Experiments: Second Step and Third Step Prediction . --- p.46 / Chapter 3.6 --- Comparison with Other Models --- p.50 / Chapter 3.7 --- Chapter Summary --- p.57 / Chapter 4 --- Stable Estimation Algorithms --- p.58 / Chapter 4.1 --- Stable AR(1) estimation algorithm --- p.59 / Chapter 4.2 --- Stable AR(2) Estimation Algorithm --- p.60 / Chapter 4.2.1 --- Real p1 and p2 --- p.61 / Chapter 4.2.2 --- Complex p1 and p2 --- p.61 / Chapter 4.2.3 --- Experiments for AR(2) --- p.63 / Chapter 4.3 --- Experiment with Real Data --- p.64 / Chapter 4.4 --- Chapter Summary --- p.65 / Chapter 5 --- Conclusion --- p.66 / Chapter 5.1 --- Further Research --- p.69 / Chapter A --- Equation Derivation --- p.70 / Chapter A.1 --- First Derivatives for Gaussian Mixture ARMA-GARCH Esti- mation --- p.70 / Chapter A.2 --- First Derivatives for Mixture-of-Expert ARMA-GARCH Esti- mation --- p.71 / Chapter A.3 --- First Derivatives for BYY Harmony Function --- p.72 / Chapter A.4 --- First Derivatives for stable estimation algorithms --- p.73 / Chapter A.4.1 --- AR(1) --- p.74 / Chapter A.4.2 --- AR(2) --- p.74 / Bibliography --- p.80

The main objective of the thesis is to carry out a rigorous hydrodynamic analysis of ocean current turbines and determine power for a range of flow and geometric parameters. For the purpose, a computational tool based on the vortex lattice method (VLM) is developed. Velocity of the flow on the turbine blades, in relation to the freestream velocity, is determined through induction factors. The geometry of trailing vortices is taken to be helicoidal. The VLM code is validated by comparing its results with other theoretical and experimental data corresponding to flows about finite-aspect ratio foils, swept wings and a marine current turbine. The validated code is then used to study the performance of the prototype gulfstream turbine for a range of parameters. Power and thrust coefficients are calculated for a range of tip speed ratios and pitch angles. Of all the cases studied, the one corresponding to tip speed ratio of 8 and uniform pitch angle 20 produced the maximum power of 41.3 [kW] in a current of 1.73 [m/s]. The corresponding power coefficient is 0.45 which is slightly less than the Betz limit power coefficient of 0.5926. The VLM computational tool developed for the research is found to be quite efficient in that it takes only a fraction of a minute on a regular laptop PC to complete a run. The tool can therefore be efficiently used or integrated into software for design optimization. / by Aneesh Goly. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

Fung Pui Cheong Gabriel. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 88-93). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Abstract (Chinese) --- p.ii / Acknowledgement --- p.iii / Contents --- p.v / List of Figures --- p.ix / List of Tables --- p.x / Chapter Part I --- The Very Beginning --- p.1 / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Contributions --- p.3 / Chapter 1.2 --- Dissertation Organization --- p.4 / Chapter 2 --- Problem Formulation --- p.6 / Chapter 2.1 --- Defining the Prediction Task --- p.6 / Chapter 2.2 --- Overview of the System Architecture --- p.8 / Chapter Part II --- Literatures Review --- p.11 / Chapter 3 --- The Social Dynamics of Financial Markets --- p.12 / Chapter 3.1 --- The Collective Behavior of Groups --- p.13 / Chapter 3.2 --- Prediction Based on Publicity Information --- p.16 / Chapter 4 --- Time Series Representation --- p.20 / Chapter 4.1 --- Technical Analysis --- p.20 / Chapter 4.2 --- Piecewise Linear Approximation --- p.23 / Chapter 5 --- Text Classification --- p.27 / Chapter 5.1 --- Document Representation --- p.28 / Chapter 5.2 --- Document Pre-processing --- p.30 / Chapter 5.3 --- Classifier Construction --- p.31 / Chapter 5.3.1 --- Naive Bayes (NB) --- p.31 / Chapter 5.3.2 --- Support Vectors Machine (SVM) --- p.33 / Chapter Part III --- Mining Financial Time Series and Textual Doc- uments Concurrently --- p.36 / Chapter 6 --- Time Series Representation --- p.37 / Chapter 6.1 --- Discovering Trends on the Time Series --- p.37 / Chapter 6.2 --- t-test Based Split and Merge Segmentation Algorithm ´ؤ Splitting Phrase --- p.39 / Chapter 6.3 --- t-test Based Split and Merge Segmentation Algorithm - Merging Phrase --- p.41 / Chapter 7 --- Article Alignment and Pre-processing --- p.43 / Chapter 7.1 --- Aligning News Articles to the Stock Trends --- p.44 / Chapter 7.2 --- Selecting Positive Training Examples --- p.46 / Chapter 7.3 --- Selecting Negative Training Examples --- p.48 / Chapter 8 --- System Learning --- p.52 / Chapter 8.1 --- Similarity Based Classification Approach --- p.53 / Chapter 8.2 --- Category Sketch Generation --- p.55 / Chapter 8.2.1 --- Within-Category Coefficient --- p.55 / Chapter 8.2.2 --- Cross-Category Coefficient --- p.56 / Chapter 8.2.3 --- Average-Importance Coefficient --- p.57 / Chapter 8.3 --- Document Sketch Generation --- p.58 / Chapter 9 --- System Operation --- p.60 / Chapter 9.1 --- System Operation --- p.60 / Chapter Part IV --- Results and Discussions --- p.62 / Chapter 10 --- Evaluations --- p.63 / Chapter 10.1 --- Time Series Evaluations --- p.64 / Chapter 10.2 --- Classifier Evaluations --- p.64 / Chapter 10.2.1 --- Batch Classification Evaluation --- p.69 / Chapter 10.2.2 --- Online Classification Evaluation --- p.71 / Chapter 10.2.3 --- Components Analysis --- p.74 / Chapter 10.2.4 --- Document Sketch Analysis --- p.75 / Chapter 10.3 --- Prediction Evaluations --- p.75 / Chapter 10.3.1 --- Simulation Results --- p.77 / Chapter 10.3.2 --- Hit Rate Analysis --- p.78 / Chapter Part V --- The Final Words --- p.80 / Chapter 11 --- Conclusion and Future Work --- p.81 / Appendix --- p.84 / Chapter A --- Hong Kong Stocks Categorization Powered by Reuters --- p.84 / Chapter B --- Morgan Stanley Capital International (MSCI) Classification --- p.85 / Chapter C --- "Precision, Recall and F1 measure" --- p.86 / Bibliography --- p.88

Abstract not available

Not available / text

published_or_final_version / abstract / toc / Economics and Finance / Master / Master of Economics

published_or_final_version / Statistics / Master / Master of Philosophy

An explicit solution is given for the value of a risk neutral firm with stochastic revenue facing the possibility of bankruptcy. The analysis is conducted in continuous time. Uncertainty is modeled using an Ito process and bankruptcy is modeled as an absorbing boundary. The analysis yields an ordinary differential equation with a closed form solution. The value function is used to calculate the firm's demand for high interest rate loans, showing a positive demand at interest rates which appear intuitively to be excessive. A value function is also derived for a risk neutral lender advancing funds to the firm. The borrowing and lending value functions are then used to examine various aspects of lender-borrower transactions under different bargaining structures. In a competitive lending market, the model shows that credit rationing occurs inevitably. In a monopoly lending market, the lender sets interest rates and maximum loan levels which reduce the borrower to zero profit. When a second borrower is introduced, the lender must allocate limited funds between two borrowers. A lender is shown to squeeze the smaller "riskier" borrower out of the market when the lender's overall credit constraint is tight. Under each bargaining structure, the model is also used to examine changes in the respective "salvage" recoveries of the lender and borrower on bankruptcy. Accepted:

No description available.