About the intraday volatility modeling, the limitations and potential problems of using Andersen & Bollerslev's approach are addressed and distinct modifications are proposed to tackle the corresponding issues. The first suggestion is about the utilization of the interaction between the intraday periodicity and the heteroskedasticity while the second is about the modified normalization for the estimation of the intraday periodicity. / Furthermore, it is demonstrated that the inclusion of overnight variance can improve the prediction accuracy of the Chicago Board of options Exchange (CBOE) volatility indexes (VIX and VXD) under specific weight combinations. The findings contradict the common perception that overnight return does not contain useful information for daily volatility modeling. / On the other hand, the third suggestion is about the inclusion of overnight information for the estimation of daily volatility. This study explores the possibility of incorporating the overnight variance indirectly through the use of linearly combined daily volatility estimators. The empirical results demonstrate that the inclusion of overnight variance can produce substantial influence when the minimum-variance constraints are relaxed. Besides, the influence is revealed to be not monotonic as an increase of the overnight proportion does not necessarily produce a larger influence. / The proposed modifications are tested with different ARCH structures, including GARCH(1,1), FIGARCH(1,d,1) and HYGARCH(1,d,1), by using simulated data and market data. Apart from studying the 1-step-ahead out-of-sample performance, several multiple-step-ahead forecasting results are also addressed. Under the same level of model flexibility (parameterized portions), our proposed modifications always outperform the original method in both in-sample fitness and out-of-sample performance on various forecasting horizons. / This research study investigates three new considerations for improving the performance of volatility modeling of financial returns. Two of them are related to the intraday volatility modeling and the other one is about the use of overnight information for daily volatility modeling. / Chu, Chun Fai Carlin. / Adviser: Kai Pui Lam. / Source: Dissertation Abstracts International, Volume: 73-04, Section: A, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 180-186). / 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, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

The thesis analyzed the performance of 5,987 mutual funds using a database called Steele Mutual Fund Experts and compared the predicting ability of various measures of performance. The measures discussed in the thesis are Treynor Ratio, Sharpe Ratio, Jensen's Alpha, Graham-Harvey-1 (GH-1), and Graham-Harvey-2 (GH-2). The performance measures are mostly used by professional money managers and scholars for literary purposes.

by Lee Yat Fai, Frederick, Pang Fai. / Thesis (M.B.A.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaf 34). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.2 / Chapter 2. --- Methodology --- p.4 / Chapter 2.1 --- Approach --- p.5 / Chapter 2.2 --- Model Automation Tools --- p.7 / Chapter 2.2.1 --- Data --- p.7 / Chapter 2.2.2 --- Trend Identification by Regression --- p.8 / Chapter 2.2.3 --- Programming variables --- p.13 / Chapter 2.2.4 --- Execution --- p.13 / Chapter 3. --- Literature Review --- p.16 / Chapter 4. --- Trading Models --- p.19 / Chapter 4.1 --- 2Premises --- p.19 / Chapter 4.2 --- Trading rules --- p.20 / Chapter 4.3 --- THE IMPLEMENTATION OF THE TRADING MODEL AND ITS TESTING --- p.20 / Chapter 4.4 --- The Test --- p.23 / Chapter 4.5 --- Some Arbitrary Inputs and Limitations --- p.24 / Chapter 4.6 --- Preliminary Testing and the Grand Trend --- p.25 / Chapter 5. --- RESULT & ANALYSIS --- p.26 / Chapter 5.1 --- Deals made along Trends Identified --- p.27 / Chapter 5.2 --- Pseudo Trends Identified during Corrections of Trends --- p.30 / Chapter 5.3 --- Deals made during Corrections of Trends --- p.30 / Chapter 6. --- CONCLUSIONS --- p.33 / Chapter 6.1 --- Further Studies Recommended --- p.33 / Bibliography --- p.34 / Appendices / Chapter a. --- Table1 --- p.35 / Chapter b. --- Table2 --- p.36

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Economics, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Includes bibliographies. / by Jean Tirole. / Ph.D.

Each of the three essays in this dissertation deals with asset timing or allocation using technical techniques and pattern recognition. The first essay uses a technical indicator, the stochastic oscillator, for market timing in the bond market. The trading strategy using this technical indicator is optimized using a genetic algorithm The second essay finds that a measure of market chaos improves the performance of a simple trend-following technique in the stock market. The last essay uses technical analysis for asset allocation. A neural network with technical indicator inputs outperforms both a passive asset mix strategy and a neural network with economic data as inputs.

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