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

[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.

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 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.

Thesis (MBA) -- University of New Brunswick, Faculty of Administration, 2003.
Typescript. Bibliography: leaves 83-84. Also available online through University of New Brunswick, UNB Electronic Theses & Dissertations.

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 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.

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.

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.

Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications.

In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation

function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used.

The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario.

The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear

to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability.

The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It

focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the

two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Doctorat en sciences économiques, Orientation économie
info:eu-repo/semantics/nonPublished

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.

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 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

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

Thesis (Ph. D.)--Florida State University, 2006.
Advisor: Kai-Sheng Song, Florida State University,College of Arts and Sciences, Dept. of Statistics; David R. Peterson, Florida State University, College of Business, Dept. of Finance. Title and description from dissertation home page (viewed Sept. 27, 2006). Document formatted into pages; contains ix, 147 pages. Includes bibliographical references.

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)