[Truncated abstract] Given the theoretical and historical evidence that support the benefit of investing internationally. there is Iittle knowledge available of proper international portfolio construction in terms of how much should be invested in foreign countries, which countries should be targeted, and types of assets to be included in the portfolio. The prospects of these benefits depend on the market volatilities, cross-country correlations, and currency risks to change in the future. Another important issue in international portfolio diversification is the growth of newly emerging markets which have different characteristics from the developed ones. Addressing the issues, the thesis intends to investigate the nature of volatility, conditional correlations, and the impact of currency risks in international portfolio, both in developed and emerging markets. Chapter 2 provides literature review on volatility spillovers, conditional correlations, and forecasting both VaR and conditional correlations using GARCH-type models. Attention is made on the estimated models, type of assets, regions of markets, and tests of forecasts. Chapter 3 investigates the nature of volatility spillovers across intemational assets, which is important in determining the nature of portfolio's volatility when most assets are seems to be connected. ... The impacts of incorporating volatility spillovers and asymmetric effect on the forecast performance of conditional correlation will also be examined in this thesis. The VARMA-AGARCH of McAleer, Hoti and Chan (2008) and the VARMA-GARCH model of Ling and McAleer (2003) will be estimated to accommodate volatility spillovers and asymmetric effect. The CCC model of Bollerslev (1990) will also be estimated as benchmark as the model does not incorporate both volatility spillovers and asymmetric effects. Given the information about the nature of conditional correlations resulted from the forecasts using a rolling window technique, Section 2 of Chapter 4 investigates the nature of conditional correlations by estimating two multivariate GARCH models allowing for time-varying conditional correlations, namely the DCC model of Engle (2002) and the GARCC model of McAleer et al. (2008). Chapter 5 conducts VaR forecast considering the important role of VaR as a standard tool for risk management. Especially, the chapter investigates whether volatility spillovers and time-varying conditional correlations discussed in the previous two chapters are of helps in providing better VaR forecasts. The BEKK model of Engle and Kroner (1995) and the DCC model of Engle (2002) will be estimated to incorporate volatility spillovers and conditional correlations, respectively. The DVEC model of Bollerslev et al. (1998) and the CCC model of Bollerslev (1990) will be estimated to serve benchmarks, as both models do not incorporate both volatility spillovers and timevarying conditional correlations. Chapter 6 concludes the thesis and lists somc possible future research.

In this work, we propose and investigate a series of methods to predict stock market movements. These methods use stock market technical and macroeconomic indicators as inputs into different machine learning classifiers. The objective is to survey existing domain knowledge, and combine multiple techniques into one method to predict daily market movements for stocks. Approaches using nearest neighbor classification, support vector machine classification, K-means classification, principal component analysis and genetic algorithms for feature reduction and redefining the classification rule were explored. Ten stocks, 9 companies and 1 index, were used to evaluate each iteration of the trading method. The classification rate, modified Sharpe ratio and profit gained over the test period is used to evaluate each strategy. The findings showed nearest neighbor classification using genetic algorithm input feature reduction produced the best results, achieving higher profits than buy-and-hold for a majority of the companies.

by Wong Hiu Ming. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 74-79). / Abstracts in English and Chinese. / ACKNOWLEDGMENTS --- p.iii / LIST OF TABLES --- p.iv / LIST OF ILLUSTRATIONS --- p.v / CHAPTER / Chapter ONE --- INTRODUCTION --- p.1 / Chapter TWO --- THE LITERATURE REVIEW --- p.5 / ARCH/GARCH Models / Nonparametric Method / Chapter THREE --- METHODOLOGY --- p.14 / ARCH Modeling / Semiparametric GARCH Modeling / Causality Test / Local Polynomial Model / Chapter FOUR --- DATA AND EMPIRICAL RESULTS --- p.37 / Data / GARCH Modeling / Semiparametric GARCH Modeling / Causality Test / Local Polynomial Model / Chapter FIVE --- CONCLUSION --- p.52 / TABLES --- p.56 / ILLUSTRATIONS --- p.62 / APPENDIX --- p.71 / BIBLIOGRAPHY --- p.74

Wong Man Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 118-121). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Table of Contents --- p.v / List of Tables --- p.viii / List of Figures --- p.x / List of Appendices --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Performance of Value Strategy in Stock Markets over The World --- p.7 / Chapter 2.2 --- Possible Explanations for Superior Return of Value Stocks --- p.11 / Chapter 2.2.1 --- Sampling Biases --- p.11 / Chapter 2.2.2 --- Risk Factors --- p.13 / Chapter 2.2.3 --- Expectation Error Hypothesis --- p.15 / Chapter 2.3 --- Studies for Value Strategy in Hong Kong --- p.20 / Chapter Chapter 3 --- Data and Methodology --- p.23 / Chapter 3.1 --- Methodology of Expectation Error Hypothesis --- p.23 / Chapter 3.1.1 --- Earnings Announcement Returns --- p.23 / Chapter 3.1.2 --- Past and Future Earnings Growth Rates of Stocks --- p.26 / Chapter 3.2 --- Data Source --- p.29 / Chapter 3.3 --- Portfolio Formation --- p.30 / Chapter 3.4 --- Variable Calculation Method --- p.31 / Chapter 3.4.1 --- Annual Buy and Hold Returns --- p.31 / Chapter 3.4.2 --- Earnings Announcement Returns --- p.32 / Chapter 3.4.3 --- Earnings Growth Rate of Portfolios --- p.33 / Chapter Chapter 4 --- Interpretation of Results --- p.34 / Chapter 4.1 --- Annual Buy and Hold Returns of Portfolios --- p.36 / Chapter 4.1.1 --- Annual Returns of Portfolios Sorted by B/M Ratio --- p.36 / Chapter 4.1.2 --- Annual Returns of Portfolios Sorted by E/P Ratio --- p.37 / Chapter 4.1.3 --- Analysis of Performance on Return Differences between Two Ratios --- p.38 / Chapter 4.2 --- Earnings Announcement Returns for Value and Glamour Portfolios --- p.41 / Chapter 4.2.1 --- 3-day Event Returns --- p.41 / Chapter 4.2.2 --- "B/M Ratio: 5,7,9 & 11 Days Event Returns" --- p.43 / Chapter 4.2.3 --- "E/P Ratio: 5,7,9 & 11 Days Event Returns" --- p.46 / Chapter 4.3 --- Past and Future Earnings Growths of Portfolios --- p.49 / Chapter 4.3.1 --- "Fundamental Variables, Prior and Post Returns of Portfolios" --- p.50 / Chapter 4.3.2 --- Earnings Performance of Portfolios --- p.51 / Chapter 4.3.3 --- Factors Affect Investor Expectation --- p.56 / Chapter Chapter 5 --- Conclusion --- p.59 / Tables --- p.64 / Figures --- p.76 / Appendices --- p.82 / References --- p.118

Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.

This thesis explores various aspects of long memory behaviour in African stock markets (ASMs). First, we examine long memory in both equity returns and volatility using the weak-form version of the efficient market hypothesis (EMH) as a criterion. The results show that these markets (largely) display a predictable component in returns; while evidence of long memory in volatility is mixed. In general, these findings contradict the precepts of the EMH and a variety of remedial policies are suggested. Next, we re-examine evidence of volatility persistence and long memory in light of the potential existence of neglected breaks in the stock return volatility data. Our results indicate that a failure to account for time-variation in the unconditional mean variance can lead to spurious conclusions. Furthermore, a modification of the GARCH model to allow for mean variation is introduced, which, generates improved volatility forecasts for a selection of ASMs. To further evaluate the quality of volatility forecasts we compare the performance of a number of long memory models against a variety of alternatives. The results generally suggest that over short horizons simple statistical models and the short memory GARCH models provide superior forecasts of volatility; while, at longer horizons, we find some evidence in favour of long memory models. However, the various model rankings are shown to be sensitive to the choice of error statistic used to assess the accuracy of the forecasts. Finally, a wide range of volatility forecasting models are evaluated in order to ascertain which method delivers the most accurate value-at-risk (VaR) estimates in the context of Basle risk framework. The results show that both asymmetric and long memory attributes are important considerations in delivering accurate VaR measures.