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by Long Mei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 73-77). / Abstracts in English and Chinese. / Acknowledgements --- p.iv / Abstract --- p.v / Table of Contents --- p.vii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Nonlinear Factor Analysis Model --- p.1 / Chapter 1.2 --- Main Objectives --- p.2 / Chapter 1.2.1 --- Investigation of the performance of the ML approach with MCECM algorithm in NFA model --- p.2 / Chapter 1.2.2 --- Investigation of the Robustness of the ML approach with MCECM algorithm --- p.3 / Chapter 1.3 --- Structure of the Thesis --- p.3 / Chapter 2 --- Theoretical Background of the MCECM Algorithm --- p.5 / Chapter 2.1 --- Introduction of the EM algorithm --- p.5 / Chapter 2.2 --- Monte Carlo integration --- p.7 / Chapter 2.3 --- Markov Chains --- p.7 / Chapter 2.4 --- The Metropolis-Hastings algorithm --- p.8 / Chapter 3 --- Maximum Likelihood Estimation of a Nonlinear Factor Analysis Model --- p.10 / Chapter 3.1 --- MCECM Algorithm --- p.10 / Chapter 3.1.1 --- Motivation of Using MCECM algorithm --- p.11 / Chapter 3.1.2 --- Introduction of the Realization of the MCECM algorithm --- p.12 / Chapter 3.1.3 --- Implementation of the E-step via the MH Algorithm --- p.13 / Chapter 3.1.4 --- Maximization Step --- p.15 / Chapter 3.2 --- Monitoring Convergence of MCECM --- p.17 / Chapter 3.2.1 --- Bridge Sampling Method --- p.17 / Chapter 3.2.2 --- Average Batch Mean Method --- p.18 / Chapter 4 --- Simulation Studies --- p.20 / Chapter 4.1 --- The First Simulation Study with the Normal Distribution --- p.20 / Chapter 4.1.1 --- Model Specification --- p.20 / Chapter 4.1.2 --- The Selection of System Parameters --- p.22 / Chapter 4.1.3 --- Monitoring the Convergence --- p.22 / Chapter 4.1.4 --- Simulation Results for the ML Estimates --- p.25 / Chapter 4.2 --- The Second Simulation Study with the Normal Distribution --- p.34 / Chapter 4.2.1 --- Model Specification --- p.34 / Chapter 4.2.2 --- Monitoring the Convergence --- p.35 / Chapter 4.2.3 --- Simulation Results for the ML Estimates --- p.38 / Chapter 4.3 --- The Third Simulation Study on Robustness --- p.47 / Chapter 4.3.1 --- Model Specification --- p.47 / Chapter 4.3.2 --- Monitoring the Convergence --- p.48 / Chapter 4.3.3 --- Simulation Results for the ML Estimates --- p.51 / Chapter 4.4 --- The Fourth Simulation Study on Robustness --- p.59 / Chapter 4.4.1 --- Model Specification --- p.59 / Chapter 4.4.2 --- Monitoring the Convergence --- p.59 / Chapter 4.4.3 --- Simulation Results for the ML Estimates --- p.62 / Chapter 5 --- Conclusion --- p.71 / Bibliography --- p.73

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This thesis consists of three parts: a genetic fuzzy trading model for stock trading, incremental intraday information for financial time series forecasting, and intraday effects in conditional variance estimation. Part A investigates a genetic fuzzy trading model for stock trading. This part contributes to use a fuzzy trading model to eliminate undesirable discontinuities, incorporate vague trading rules into the trading model and use genetic algorithm to select an optimal trading ruleset. Technical indicators are used to monitor the stock price movement and assist practitioners to set up trading rules to make buy-sell decision. Although some trading rules have a clear buy-sell signal, the signals are always detected with 'hard' logical. These trigger the undesirable discontinuities due to the jumps of the Boolean variables that may occur for small changes of the technical indicator. Some trading rules are vague and conflicting. They are difficult to incorporate into the trading system while they possess significant market information. Various performance comparisons such as total return, maximum drawdown and profit-loss ratios among different trading strategies were examined. Genetic fuzzy trading model always gave moderate performance. Part B studies and contributes to the literature that focuses on the forecasting of daily financial time series using intraday information. Conventional daily forecast always focuses on the use of lagged daily information up to the last market close while neglecting intraday information from the last market close to current time. Such intraday information are referred to incremental intraday information. They can improve prediction accuracy not only at a particular instant but also with the intraday time when an appropriate predictor is derived from such information. These are demonstrated in two forecasting examples, predictions of daily high and range-based volatility, using linear regression and Neural Network forecasters. Neural Network forecaster possesses a stronger causal effect of incremental intraday information on the predictand. Predictability can be estimated by a correlation without conducting any forecast. Part C explores intraday effects in conditional variance estimation. This contributes to the literature that focuses on conditional variance estimation with the intraday effects. Conventional GARCH volatility is formulated with an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance. However, the intra-daily information doesn't include in the conditional variance while it should has implication on the daily variance. Using Engle's multiplicative-error model formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The impact of significant changes in intraday data is reflected in the MEM-GARCH variance. For some frameworks, it is possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance equation. / Ng, Hoi Shing Raymond. / Adviser: Kai-Pui Lam. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 107-114). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.