Dissertations / Theses on the topic 'Stock exchanges Australia Econometric models'

This research questions whether technical trading rules can help predict stock price movements for a sample of stocks selected from four equity markets from the Asia-Pacific region: Australia, Malaysia, Hong Kong and Thailand for the period 1989-2008. The research is split into two stages. Stage-1 of the research tests the predictability of technical trading rules against a buyand- hold strategy. The variable moving average (VMA), fixed moving average (FMA) and the trading range break (TRB) trading rules are applied to this research. Economic predictability of these rules is examined by comparing returns conditional on a trading rule buy (sell) signal against an unconditional buy-and-hold return. Any existence of excess returns can thus be established. This follows with a statistical analysis of returns using a traditional t-test methodology. Traditional statistical tests assume normally distributed returns with independent observations and a non-changing distribution across time. In Stage-2 of this research a bootstrap checks whether features such as non-normality, time-varying moments and serial correlation bias test statistics. The bootstrap involves assumptions regarding the underlying returns generating process (RGP) and allows returns conditional on a trading rule buy (sell) signal from the original stock price series to be compared with conditional returns simulated from four common null models: RW, AR (1), GARCH-M and E-GARCH models. Simulated p-values are calculated in conjunction with simulated distributions and are applied in lieu of the theoretical normal distribution. Given this process it is possible to infer as to whether non-linear dependencies in returns can be captured by any of the three trading rules. Given the null model output standard t-test outcomes of predictability of technical trading rules may be diminished and/or eliminated. Conclusions are drawn as to the predictability and profitability of the VMA, FMA and TRB trading rules when applied to the chosen stock samples. Findings of this research indicate returns conditional on technical trading rules exceed unconditional buy-and-hold returns for all stocks. Thai sample output indicates strong support in favour of the predictability of standard test results supporting the use of technical trading rules. Output for Australia, Hong Kong and Malaysia indicates that previous standard t-test outcomes of predictability may be diminished and/or eliminated. This implies that the underlying RGP may be characterised by underlying features of some/all of the stochastic models.

This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this

Thesis (Ph. D.)--University of California, Riverside, 2008. Thesis (Ph. D.)--University of California, Riverside, 2009.
Includes abstract. Title from first page of PDF file (viewed February 3, 2009). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 135-137). Includes bibliographical references (leaves ). Also issued in print.

This study deals with the estimation of the optimal hedge ratios using various econometric models. Most of the recent papers have demonstrated that the conventional ordinary least squares (OLS) method of estimating constant hedge ratios is inappropriate, other more complicated models however seem to produce no more efficient hedge ratios. Using daily AOIs and SPI futures on the Australian market, optimal hedge ratios are calculated from four different models: the OLS regression model, the bivariate vector autoaggressive model (BVAR), the error-correction model (ECM) and the multivariate diagonal Vcc GARCH Model. The performance of each hedge ratio is then compared. The hedging effectiveness is measured in terms of ex-post and ex-ante risk-return traHe-off at various forcasting horizons. It is generally found that the GARCH time varying hedge ratios provide the greatest portfolio risk reduction, particularly for longer hedging horizons, but hey so not generate the highest portfolio return.

This study evaluates the potential benefits that investors obtain from diversifying their portfolios into emerging markets when the time varying behavior of assets is considered. It also tests whether the existing asset-pricing model developed in the context of developed markets, which assumes complete integration, can explain the expected returns in emerging markets and determines the risk of investing in these markets using cross section and time series data. An international capital asset pricing model (ICAPM) with time varying moments developed by Harvey (1991) is adopted. The conditional asset-pricing model, which takes into account prevailing world economic factors, was used. The Generalized Methods of Moments (GMM) is used to test the model. Results indicate that some markets have become more integrated to the world markets than they were in the 1980s and other which failed to open their economies fully have become more segmented. The thesis looks at regional markets of Latin America, Africa Sub-Sahara, Middle East and North Africa, East Europe and Asia. A number of authors have looked at the emerging markets of Asia and Latin America but little is known about the African, Middle East and East Europe markets. The innovation of this research is it looked at the behavior of assets in all regional global markets and sees if they behave differently.

Financial systems (i.e. banking systems and stock markets) can influence economic growth by performing the five key financial functions, namely: mobilising savings, allocating capital, easing of exchange, monitoring and exerting corporate governance, as well as ameliorating risk. The level of development of the financial system is a key determinant of how effectively and efficiently these functions are performed. This study examines the short-run and long-run relationships between financial system development and economic growth for a panel of seven African countries (namely: Egypt, Ivory Coast, Kenya, Morocco, Nigeria, South Africa and Tunisia) covering the period 1988 to 2008. While numerous empirical studies have researched this topic, none of the previous African empirical literature have investigated thjs by using three groups of financial development measures (i.e. overall financial development, banking system development and stock market development measures) as well as employing panel cointegration analyses. The investigation of the long-run finance-growth relationship is conducted using two methods; the Pedroni panel cointegration approach and the Kao panel cointegration technique. The Pedroni panel cointegracion approach is more often applied in empirical research as it has less restrictive deterministic trend assumptions, while the Kao panel cointegration technique is employed in this study for comparison purposes. Furthermore, the short-run linkages bet\veen financial development and economic growth are analysed using the Holtz-Eakin d of (1989) panel Granger causality test. The results of the Pedroni cointegration tests show that there are long-run relationships between overall financial development (measured by LOFD and OFD2) and economic growth, banking system development (measured by LPSC) and economic growth, as well as stock marker development (measured by LMCP and LVLT) and economic growth. In contrast, the Kao test fails to find any cointegration between finance and growth. However, on the balance, findings largely support a conclusion of cointegration between financial development and economic growth since the Pedroni approach is more appropriate for examining cointegration in heterogeneous panels. Estimates of these long-run cointegrating relationships show that all five financial development measures have the expected positive linkages with growth. However, only four of the five financial development measures were found to have significant long-run linkages with growth, as the relationship between LOFD and growth was not found to be significant in the long-run. The panel Granger causality results show that economic growth Granger causes banking system development in the short-run (i.e. there is demand-following finance), irrespective of the measure of banking development used. While there is bi-directional, reciprocal causality between economic growth and both of the measures of overall financial development and one measure of srock market development (i.e. LVLT). Thus, pulicy makers should focus on formulating policy which promotes faster paced economic growth so as to stimulate financial development, while at the same time encourage policy that promotes the balanced expansion of the banking systems and srock markets in ordet to augment economic growth.

The goal of the study is to examine the relationship between financial sector development and output growth in the agricultural, mining and manufacturing sectors in South Africa. The analysis is based on the hypothesis that financial development is essential for promoting production growth in an economy. To test the hypothesis, in the South African context, the vector autoregressive model (VAR) framework and Granger causality test are applied to a quarterly data set starting from 1970 quarter one to 2009 quarter four. The results suggest that financial intermediary development (bank based measure) and stock market development (market based measure) have a positive impact on output growth in the agriculture, mining and manufacturing sectors in South Africa. There is evidence of a one way causal relationship between financial sector development and sectoral output growth. Particularly, there is evidence that financial intermediary development and stock market development causes output growth in the agriculture, mining and manufacturing sectors in South Africa. However, there is no evidence showing causality running from sectoral output growth to financial sector development. The results provide evidence supporting the theory which states that financial development is essential to promote output growth in a country i.e. in our case South Africa. Thus an efficient financial system which promotes efficient channeling of resources towards the agricultural, mining and manufacturing sectors should be built.

This study examines the nature of the relationship which exists between mining sector production and development of the financial systems in South Africa. This is particularly important in that the mining sector is considered to be one of the major contributors to the country’s overall economic growth. South Africa is also considered to have a very well developed financial system, to the point where the dominance of one over the other is difficult to identify. Therefore offering insight into the nature of this relationship will assist policy makers in identifying the most effective policies in order to ensure that the developments within the financial systems impact appropriately on the mining sector, and ultimately on the economy. In addition to using the conventional proxies of financial system development, this study utilises the principal component analysis (PCA) to construct an index for the entire financial system. The multivariate cointegration approach as proposed by Johansen (1988) and Johansen and Juselius (1990) was then used to estimate the relationship between the development of the financial systems and the mining sector production for the period 1988-2008. The study reveals mixed results for different measures of financial system development. Those involving the banking system show that a negative relationship exists between total mining production and total credit extended to the private sector, while liquid liabilities has a positive relationship. Similarly, with the stock market system, mixed results are also obtained which reveal a negative relationship between total mining production and stock market capitalisation, while a positive relationship is found with secondary market turnover. Of all the financial system variables, only that of stock market capitalisation was found to be significant. The result with the financial development index reveals that a significant negative relationship exists between financial system development and total mining sector production. Results on the other variables controlled in the estimation show that positive and significant relationships exist between total mining production and both nominal exchange rate and political stability respectively. Increased mining production therefore takes place in periods of appreciating exchange rates, and similarly in the post-apartheid era. On the other hand, negative relationships were found for both trade openness and inflation control variables. The impulse response and variance decomposition analyses showed that total mining production explains the largest amount of shocks within itself. Overall, the study reveals that the mining sector might not have benefited much from the development in the South African financial system.

Using 1980 - 2012 annual data, the study empirically investigates the dynamic relationship between financial development and economic growth in three developing countries (South Africa, Brazil and Kenya) and three developed countries (United States of America, United Kingdom and Australia). The study was motivated by the current debate regarding the role of financial development in the economic growth process, and their causal relationship. The debate centres on whether financial development impacts positively or negatively on economic growth and whether it Granger-causes economic growth or vice versa. To this end, two models have been used. In Model 1 the impact of bank- and market-based financial development on economic growth is examined, while in Model 2 it is the causality between the two that is explored. Using the autoregressive distributed lag (ARDL) bounds testing approach to cointegration and error-correction based causality test, the results were found to differ from country to country and over time. These results were also found to be sensitive to the financial development proxy used. Based on Model 1, the study found that the impact of bank-based financial development on economic growth is positive in South Africa and the USA, but negative in the U.K – and neither positive nor negative in Kenya. Elsewhere the results were inconclusive. Market-based financial development was found to impact positively in Kenya, USA and the UK but not in the remaining countries. Based on Model 2, the study found that bank-based financial development Granger-causes economic growth in the UK, while in Brazil they Granger-cause each other. However, in South Africa, Kenya and USA no causal relationship was found. In Australia the results were inconclusive. The study also found that in the short run, market-based financial development Granger-causes economic growth in the USA but that in South Africa and Brazil, the reverse applies. On the other hand bidirectional causality was found to prevail in Kenya in the same period.
Economics
DCOM (Economics)

by Ao, Sio Iong.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references.
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Overview --- p.2
Chapter 1.2 --- Topics of this Study --- p.3
Chapter 1.3 --- Econometric Analysis --- p.3
Chapter 1.4 --- Computational Intelligence --- p.4
Chapter 1.4.1 --- Overview --- p.4
Chapter 1.4.2 --- Successful Cases of Applying CI in Time Series Analysis --- p.4
Chapter 2 --- Background --- p.6
Chapter 2.1 --- Market Descriptions --- p.6
Chapter 2.1.1 --- Overview of the Markets --- p.6
Chapter 2.2 --- VAR method --- p.10
Chapter 2.2.1 --- Introduction --- p.11
Chapter 2.2.2 --- Implementation of VAR by RATS --- p.12
Chapter 2.2.3 --- Impulse Response Functions --- p.12
Chapter 2.3 --- Neural Network --- p.14
Chapter 2.3.1 --- Introduction --- p.14
Chapter 2.3.2 --- Supervised vs Unsupervised learning --- p.15
Chapter 2.3.3 --- Back-Propagation network --- p.15
Chapter 2.4 --- Evolutionary Computation --- p.19
Chapter 2.4.1 --- Motivation of Employing Evolutionary Computation --- p.19
Chapter 2.4.2 --- Brief Description --- p.21
Chapter 2.4.3 --- Genetic Algorithm --- p.21
Chapter 3 --- Analysis of their Interdependence and SD --- p.23
Chapter 3.1 --- Interdependence of the Asian Indices --- p.23
Chapter 3.2 --- Forecasting Index Price with the Help of Neural Network --- p.26
Chapter 3.3 --- Interdependence of the Standard Deviations of the Stock Indices --- p.28
Chapter 3.4 --- Using the Neural Network to Make Forecasting of the Stan- dard Deviations --- p.29
Chapter 3.5 --- Summary --- p.33
Chapter 4 --- Forecasting Opening Prices --- p.34
Chapter 4.1 --- Step 1: Identificating of the Interdependence of the Opening Price on Different Stock Indices by VAR --- p.36
Chapter 4.2 --- Step 2: Using the Neural Network to Make Forecasting of the Opening Prices --- p.38
Chapter 4.3 --- Summary --- p.39
Chapter 5 --- Incorporating Correlated Markets --- p.41
Chapter 5.1 --- Overview of the Markets from the Prespectives of VAR --- p.43
Chapter 5.2 --- Investigation of the Correlations by VAR Method --- p.43
Chapter 5.3 --- Prediction of the Market by Neural Network --- p.46
Chapter 5.4 --- Hypothesis: the Correlations of the Markets Are Time-Dependent --- p.46
Chapter 5.5 --- Testing this Hypothesis with Predictions by Neural Network . --- p.48
Chapter 5.6 --- Summary --- p.51
Chapter 5.7 --- F-tests Results on Different Periods of HK Markets --- p.51
Chapter 6 --- Hybrid VAR-NN-EC System --- p.53
Chapter 6.1 --- Introduction --- p.53
Chapter 6.1.1 --- Overview of the Econometric Analysis of the Lead-Lag Relationship of Stock Markets --- p.54
Chapter 6.1.2 --- Previous Results of Employing the Stand-alone Neural Network --- p.55
Chapter 6.2 --- Working Mechanism of the Hybrid VAR-NN-EC --- p.56
Chapter 6.3 --- Comparing Results from the VAR-NN-EC System --- p.58
Chapter 6.4 --- Summary --- p.60
Chapter 7 --- Hybrid System for Dual-Listing Indices --- p.61
Chapter 7.1 --- Introduction --- p.61
Chapter 7.2 --- HSI vs HSLRI --- p.62
Chapter 7.2.1 --- HSI's Selection Criteria --- p.62
Chapter 7.2.2 --- Hang Seng London Reference Index --- p.63
Chapter 7.2.3 --- Motivation for the Study --- p.63
Chapter 7.3 --- Data Descriptions --- p.64
Chapter 7.4 --- Overviews of this Analysis System --- p.64
Chapter 7.5 --- Results from the Simplified AR-NN System --- p.65
Chapter 7.5.1 --- Regression Results --- p.66
Chapter 7.5.2 --- NN Results --- p.67
Chapter 7.6 --- Summary --- p.68
Chapter 8 --- Using EC for Selecting Stock Experts --- p.70
Chapter 8.1 --- Example of Evolutionary Computation --- p.71
Chapter 8.2 --- Comparison of Results from the VAR-NN-EC System --- p.72
Chapter 8.3 --- Summary --- p.73
Chapter 9 --- Conclusion --- p.74
Bibliography --- p.i

by Lam Cham.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.
Includes bibliographical references (leaves 75-79).
ACKNOWLEDGMENTS --- p.i
ABSTRACT --- p.ii
LIST OF TABLES --- p.iii
LIST OF FIGURES --- p.iv
Chapter CHAPTER 1: --- INTRODUCTION --- p.1
Chapter CHAPTER 2: --- HISTORICAL BACKGROUND --- p.8
Chapter 2.1 --- Financial Liberalization in Nine Asian Countries --- p.8
Chapter 2.1.1 --- Hong Kong --- p.8
Chapter 2.1.2 --- Korea --- p.12
Chapter 2.1.3 --- "Indonesia, Malaysia, Singapore and Thailand - the ASEAN-4" --- p.15
Chapter 2.1.4 --- Taiwan --- p.18
Chapter 2.1.5 --- Japan --- p.19
Chapter 2.1.6 --- The Philippines --- p.20
Chapter 2.2 --- Stock Market Trend --- p.21
Chapter CHAPTER 3: --- LITERATURE REVIEW --- p.28
Chapter 3.1 --- Gain from International Diversification --- p.28
Chapter 3.2 --- International Transmission Effects --- p.30
Chapter 3.3 --- Integration of World Stock Markets --- p.31
Chapter CHAPTER 4: --- METHODOLOGY --- p.38
Chapter 4.1 --- Cointegration and Diversification --- p.38
Chapter 4.2 --- Testing for Cointegration --- p.45
Chapter CHAPTER 5: --- DATA --- p.50
Chapter 5.1 --- MSCI Index --- p.50
Chapter 5.2 --- Asian Funds --- p.51
Chapter CHAPTER 6: --- EMPIRICAL RESULTS --- p.52
Chapter 6.1 --- Unit Root Test --- p.52
Chapter 6.1.1 --- ADF and Phillips-Perron Unit Root Test --- p.52
Chapter 6.1.2 --- Unit Root Test with Structural Break --- p.55
Chapter 6.2 --- Cointegration Test on Stock Markets --- p.57
Chapter 6.2.1 --- Regional Factor Vs World Factor --- p.57
Chapter 6.2.2 --- Integration of the Asian Markets --- p.61
Chapter 6.3 --- Cointegration Test on the Asian Funds --- p.63
Chapter 6.3.1 --- Weekly Results --- p.65
Chapter 6.3.2 --- Monthly Results --- p.66
Chapter CHAPTER 7: --- CONCLUSIONS --- p.72
REFERENCES --- p.75

by Poon Yeuk Wan, Tsang Fei.
Thesis (M.B.A.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 54-55).
Acknowledgements --- p.i
Abstract --- p.iii
Table of Contents --- p.iv
List of Tables --- p.vi
List of Appendix --- p.vii
Chapter Chapter1 --- Introduction --- p.1
Chapter 1.1 --- Project Objective --- p.1
Chapter 1.2 --- Project Structure --- p.2
Chapter 1.3 --- Data --- p.3
Chapter Chapter 2 --- Emerging Markets´ؤ-An Overview --- p.5
Chapter 2.1 --- Latin America --- p.5
Argentina --- p.5
Brazil --- p.7
Chile --- p.7
Colombia --- p.8
Mexico --- p.8
Peru --- p.9
Venezuela --- p.9
Chapter 2.2 --- Eastern Europe --- p.10
Czech Republic --- p.10
Poland --- p.10
Slovakia --- p.11
Hungary --- p.11
Russia --- p.11
Chapter 2.3 --- Middle East --- p.12
Israel --- p.12
Jordan --- p.12
Chapter 2.4 --- Implication For Further Analysis --- p.13
Chapter Chapter 3 --- Analysis and Findings I: Descriptive Statistics Analysis --- p.14
Chapter 3.1 --- Objective of Descriptive Statistic Analysis --- p.14
Chapter 3.2 --- Findings --- p.16
Eastern Europe --- p.16
Latin America --- p.16
Middle East --- p.17
Chapter 3.3 --- Conclusion --- p.18
Chapter Chapter 4 --- Analysis and Findings II: Day-of-the- Week (Monday effect) Test --- p.19
Chapter 4.1 --- Objective --- p.19
Chapter 4.2 --- Literature Review --- p.19
Chapter 4.3 --- Methodology --- p.21
Chapter 4.4 --- Data --- p.23
Chapter 4.5 --- Analysis --- p.24
Chapter 4.6 --- Empirical findings --- p.25
Chapter I. --- The equality of return test --- p.25
Eastern Europe --- p.26
Latin America --- p.26
Middle East --- p.26
Overall --- p.27
Local currency versus US currency --- p.27
Chapter II. --- Comparison of Monday return with returns of other days within the week --- p.27
Chapter l. --- Without exchange rate effect --- p.28
Chapter 4.7 --- Monday effect一-an overview --- p.31
Comparison by region --- p.31
Eastern Europe --- p.31
Latin America --- p.31
Middle East --- p.32
The effect of exchange rate --- p.32
Chapter Chapter 5 --- Analysis And Findings III: Correlation Analysis --- p.33
Chapter 5.1 --- Literature Review --- p.33
Chapter 5.2 --- Objective --- p.35
Chapter 5.3 --- Methodology --- p.35
Chapter 5.4 --- Findings --- p.38
Chapter I --- Correlations Within Regions --- p.38
Eastern Europe --- p.33
Latin America --- p.40
Middle East --- p.42
Chapter II. --- Correlation Among Regions --- p.43
Eastern Europe vs. Latin America --- p.43
Latin America vs. Middle East --- p.44
Eastern Europe vs. Middle East --- p.45
Chapter III. --- Correlations with the United States --- p.46
US vs. Eastern Europe --- p.46
US vs. Latin America --- p.46
US vs. Middle East --- p.47
Chapter 5.5 --- Conclusion --- p.43
Chapter Chapter 6 --- Conclusions and Implications --- p.49
Implications on market integration --- p.52
BIBLIOGRAPHY --- p.54
APPENDIX --- p.56

by Chiu Mei-Yee, Pamela, Pong Kwok-Hung, Patrick.
Thesis (M.B.A.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaf 52).
ABSTRACT --- p.ii
TABLE OF CONTENT --- p.iii
LIST OF ILLUSTRATIONS --- p.iv
LIST OF TABLES --- p.v
ACKNOWLEGEMENTS --- p.vi
Chapter
Chapter I. --- INTRODUCTION --- p.1
Chapter II. --- OBJECTIVES --- p.3
Chapter III. --- LITERATURE REVIEW --- p.4
Chapter IV. --- THE SAMPLE --- p.9
Chapter V. --- METHODOLOGY --- p.14
The Market Model --- p.15
Methods to Estimate the Excess Returns --- p.16
Chapter VI. --- RESULTS AND ANALYSIS --- p.19
Price Effects on Inclusion in HSI --- p.19
Price Effects on Exclusion from HSI --- p.33
Comparison between Inclusion and Exclusion --- p.41
Chapter VII. --- IMPLICATIONS --- p.42
Chapter VIII. --- CONCLUSION --- p.45
APPENDIX --- p.47
BIBLIOGRAPHY --- p.52