Дисертації з теми "Stock exchanges Forecasting Econometric models"
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Hakim, Abdul. "Modelling the interactions across international stock, bond and foreign exchange markets." UWA Business School, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0202.
Повний текст джерелаLi, Heng. "New econometrics models with applications." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1165.
Повний текст джерелаLin, Gang. "Nesting regime-switching GARCH models and stock market volatility, returns and the business cycle /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9906497.
Повний текст джерелаClayton, Maya. "Econometric forecasting of financial assets using non-linear smooth transition autoregressive models." Thesis, University of St Andrews, 2011. http://hdl.handle.net/10023/1898.
Повний текст джерелаHumpe, Andreas. "Macroeconomic variables and the stock market : an empirical comparison of the US and Japan." Thesis, St Andrews, 2008. http://hdl.handle.net/10023/464.
Повний текст джерелаMilunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.
Повний текст джерелаYoldas, Emre. "Essays on multivariate modeling in financial econometrics." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663051691&SrchMode=2&sid=2&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1265225972&clientId=48051.
Повний текст джерелаIncludes 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.
Fodor, Bryan D. "The effect of macroeconomic variables on the pricing of common stock under trending market conditions." Thesis, Department of Business Administration, University of New Brunswick, 2003. http://hdl.handle.net/1882/49.
Повний текст джерелаTypescript. Bibliography: leaves 83-84. Also available online through University of New Brunswick, UNB Electronic Theses & Dissertations.
Yang, Wenling. "M-GARCH Hedge Ratios And Hedging Effectiveness In Australian Futures Markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2000. https://ro.ecu.edu.au/theses/1530.
Повний текст джерелаO'Grady, Thomas A. "The profitability of technical analysis and stock returns from a traditional and bootstrap perspective : evidence from Australia, Hong Kong, Malaysia and Thailand." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/506.
Повний текст джерелаThupayagale, Pako. "Essays in long memory : evidence from African stock markets." Thesis, St Andrews, 2010. http://hdl.handle.net/10023/883.
Повний текст джерелаCaley, Jeffrey Allan. "A Survey of Systems for Predicting Stock Market Movements, Combining Market Indicators and Machine Learning Classifiers." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/2001.
Повний текст джерелаChimhini, Joseline. "International portfolio diversification with special reference to emerging markets." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2001. https://ro.ecu.edu.au/theses/1076.
Повний текст джерелаStarkey, Randall Ashley. "Financial system development and economic growth in selected African countries: evidence from a panel cointegration analysis." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002713.
Повний текст джерелаD'Agostino, Antonello. "Understanding co-movements in macro and financial variables." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210597.
Повний текст джерелаIn 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
Tongo, Yanga. "Financial sector development and sectoral output growth evidence from South Africa." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1002739.
Повний текст джерелаAjagbe, Stephen Mayowa. "An analysis of the long run comovements between financial system development and mining production in South Africa." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002689.
Повний текст джерела"Hybrid VAR, neural network, and evolutionary computation for predicting Asian Pacific market lead-lag dynamics." 2003. http://library.cuhk.edu.hk/record=b5891593.
Повний текст джерелаThesis (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
"Long run diversification potential in Asian stock markets: a test of cointegration." 1997. http://library.cuhk.edu.hk/record=b5889149.
Повний текст джерелаThesis (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
"Stock return volatility of emerging markets." 1998. http://library.cuhk.edu.hk/record=b5896256.
Повний текст джерелаThesis (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
Chandrashekar, Satyajit. "Three new perspectives for testing stock market efficiency." Thesis, 2006. http://hdl.handle.net/2152/3757.
Повний текст джерелаJin, Hua. "A comparative study of industry factors in emerging and developed stock markets." Master's thesis, 2005. http://hdl.handle.net/1885/146420.
Повний текст джерела"Modeling and forecasting Hong Kong stock market return." 1999. http://library.cuhk.edu.hk/record=b5889916.
Повний текст джерелаThesis (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
"Market effects of changes in the composition of the Hang Seng Index." 1998. http://library.cuhk.edu.hk/record=b5889419.
Повний текст джерелаThesis (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
Turk, George Watson Song Kaisheng Peterson David R. "Scale mixture modeling and shape parameter estimation of security returns new theories and analyses /." 2006. http://etd.lib.fsu.edu/theses/available/etd-07102006-171906.
Повний текст джерелаAdvisor: 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.
Nyasha, Sheilla. "Financial development and economic growth : new evidence from six countries." Thesis, 2014. http://hdl.handle.net/10500/18576.
Повний текст джерелаEconomics
DCOM (Economics)