The inference of causal relationships between stock markets constitutes a major research topic in the field of financial time series analysis. A successful reconstruction of the underlying causality structure represents an important step towards the overall aim of improving stock market price forecasting. In this thesis, I utilise the concept of Granger-causality for the identification of causal relationships. One major challenge is the possible presence of latent variables that affect the measured components. An instantaneous interaction can arise in the inferred network of stock market relationships either spuriously due to the existence of a latent confounder or truly as a result of hidden agreements between market players. I investigate the implications of such a scenario; proposing a new method that allows for the first time to distinguish between instantaneous interactions caused by a latent confounder and those resulting from hidden agreements. Another challenge is the implicit assumption of existing Granger-causality analysis techniques that the interactions have a time delay either equal to or a multiple of the observed data. Two sub-cases of this scenario are discussed: (i) when the collected data is simultaneously recorded, (ii) when the collected data is non-simultaneously recorded. I propose two modified approaches based on time series shifting that provide correct inferences of the complete causal interaction structure. To investigate the performance of the above mentioned method improvements in predictions, I present a modified version of the building block model for modelling stock prices allowing causality structure between stock prices to be modelled. To assess the forecasting ability of the extended model, I compare predictions resulting from network reconstruction methods developed throughout this thesis to predictions made based on standard correlation analysis using stock market data. The findings show that predictions based on the developed methods provide more accurate forecasts than predictions resulting from correlation analysis.

Three empirical studies are conducted examining the efficiency of S&P 500 futures prices and the pricing of these futures contracts. In the first study, the ability of futures prices to predict the realized spot S&P 500 index prices on the expiration date is examined for near term contracts. The futures prices are found to be unbiased predictors of the realized spot index prices for the nineteen quarterly contracts from 1982 to 1986. Previous studies report significant deviations in S&P SOO futures prices from theoretically determined Cost of Carry Model (CCM) prices. In the second study, it is found that the CCM using the federal funds rate, a proxy for the overnight repurchase rate, provides relatively better estimates of the S&P S(x) futures prices over the 1984-1986 period. The futures mispricing also reflects the weekend effect anomaly: futures prices are "over-priced" relative to CCM prices on Mondays, whereas the opposite occurs on Fridays. The futures over-pricing (under-pricing) is characterized by "bull" ("bear") financial markets and the extent of price changes are relatively greater in the futures market. The futures under-pricing is supported by strong future market volume and open-interest positions. The basis and changes in it over the futures contract period are measures of how well integrated the futures market and the underlying spot market are. In the third study, based on daily closing prices for the S&P 500 index and index futures for the 1984-1986 period, it is found that the basis decreases over the contract period but the rate of decrease is independent of the time to expiration. The change in basis on Mondays is generally positive which also reflects the weekend effect anomaly. The daily basis is negative on 107 days, which generally occurs during strong futures market trading volume and open interest positions. It is doubtful whether the negative basis can be attributed to a negative net financing cost, where the dividend yield 0.1 the spot index exceeds the cost of financing the spot index forward.

Thesis (M.B.A.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-01

by Jor Chi Pang, So Kwok Kin. / Bibliography: leaves 87-90 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1986

by Cheng Wui-kei. / Title also in Chinese. / Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1980. / Bibliography: leaves 58-59.

by Patrick Chiu-Ying Ng. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1980. / Bibliography: leaves 32-33.

by Roger K.H. Luk, Robert M.K. Young. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1981. / Bibliography: leaves 101-103.

by Kwong Kok-shi, Mak Kai-kwong. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1981. / Bibliography: leaves 142-144.

by Liu Tat-luen. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 68-69. / ABSTRACT --- p.i / ACKNOWLEDGMENT --- p.ii / TABLE OF CONTENTS --- p.iii / LIST OF TABLES --- p.v / LIST OF FIGURES --- p.vi / Chapter / Chapter I. --- INTRODUCTION --- p.1 / The trend of international diversification in stock investment --- p.1 / Why diversify in international stock investment --- p.2 / Chapter II. --- STUDY OBJECTIVES --- p.8 / Hypothesis --- p.8 / Relevance of Hong Kong case --- p.9 / Research Methodology --- p.10 / Chapter III. --- HOW RISKS ARE MEASURED --- p.12 / Systematic risk and non-systematic risk --- p.12 / Measurement of systematic risk - concept of β --- p.13 / Chapter IV. --- RISK-RETURN RELATIONSHIP --- p.15 / The standard Capital Asset Pricing Model --- p.15 / B in CAPM defined --- p.17 / Chapter V. --- EXTENSION OF CAPM INTERNATIONALLY --- p.19 / Chapter VI. --- SYSTEMATIC AND NON-SYSTEMATIC RISKS IN INTERNATIONAL CAPM CONTEXT --- p.27 / Systematic Risk - International CAPM --- p.27 / Non-systematic risk - International CAPM --- p.29 / Chapter VII. --- THE DETERMINATION OF βHong Kong --- p.31 / Estimating of β by Regression Analysis --- p.31 / Analysis Procedure --- p.32 / Graphical presentation of the data set --- p.34 / The regression Output --- p.35 / Estimation of 3 from the definition --- p.38 / Findings --- p.42 / Chapter VIII. --- USE OF BETA FOR A COUNTRY STOCK MARKET --- p.48 / Chapter IX. --- CONCLUSION --- p.52 / Chapter Appendix 1 --- The Hang Seng Index : 1980-89 --- p.58 / Chapter Appendix 2 --- The Data Set - Hang Seng and World Index (1987-89) --- p.59 / Chapter Appendix 3 --- The Data Set - Hang Seng and World Index (1982-84) --- p.60 / Chapter Appendix 4 --- t - test for the correlation of 3-year regression data --- p.61 / Chapter Appendix 5 --- t - test for the correlation of yearly regression data --- p.63 / Chapter Appendix 6 --- Constraints to International Diversification --- p.66 / BIBLIOGRAPHY --- p.68

by Ho, Conrad Siu-chan, Wong, Nancy In-peng. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 46-50). / COVER --- p.i / ABSTRACT --- p.ii / ACKNOWLEDGEMENTS --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.vii / Chapter / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- The Theme of this Paper --- p.1 / Chapter 1.2 --- Literature Survey on Efficient Capital Market Hypothesis --- p.2 / Chapter 1.3 --- The Structure of this Paper --- p.4 / Chapter 2 --- THE EFFICIENT CAPITAL MARKET HYPOTHESIS --- p.5 / Chapter 2.1 --- The Three Levels of Efficiency --- p.6 / Chapter 2.2 --- Implications of the Three Levels of Efficiency --- p.10 / Chapter 2.3 --- Assumptions of ECMH --- p.11 / Chapter 3 --- METHODOLOGY --- p.14 / Chapter 3.1 --- The Statistical Tests --- p.14 / Chapter 3.1.1 --- Serial Correlation Test --- p.15 / Chapter 3.1.2 --- Regression Test --- p.17 / Chapter 3.1.3 --- Runs Test --- p.17 / Chapter 3.2 --- Sample Data --- p.19 / Chapter 3.3 --- The Limitation --- p.22 / Chapter 4 --- FINDINGS AND INTERPRETATIONS --- p.24 / Chapter 4.1 --- The Decision Criteria --- p.24 / Chapter 4.2 --- Interpretations --- p.24 / Chapter 4.2.1 --- SCC Test --- p.25 / Chapter 4.2.2 --- Regression Test --- p.31 / Chapter 4.2.3 --- Runs Test --- p.34 / Chapter 4.2.4 --- The overall interpretation --- p.38 / Chapter 5 --- CONCLUSIONS --- p.43 / REFERENCES --- p.46 / APPENDIX / Chapter 1 --- List of Selected Samples and their Market Capitalization --- p.51 / Chapter 2 --- "Adjusted Data, Adjustments and Raw Data" --- p.60 / Chapter 3 --- Price Adjustment Information --- p.96 / Chapter 4 --- Summary of Statistical Findings --- p.99 / Chapter 5 --- Findings of Serial Correlation Test --- p.129 / Chapter 6 --- Findings of Regression Test --- p.141 / Chapter 7 --- Findings of Runs Test --- p.153