Dissertations / Theses on the topic 'Stocks - Prices - Econometric models'

Following the important work on unit roots and cointegration which started in the mid-1980s, a great deal of econometric works has been devoted to the study of the subtleties and varieties of near nonstationarity and persistence that characterize so many economic and financial time series. In recent years research activity has gained importance with outstanding contributions made on estimation and testing of a wide variety of long memory processes, together with many interesting and imaginative applications over a wide variety of different fields of economics and finance. For these reasons, this study provides empirical evidence to an aspect of fractional differencing and long memory processes, or the long memory of volatility. Evidence of long memory persistence is explored using stock price indices for eight emerging economies in both Asian and Latin American markets. The concern with the presence of long memory in higher moments of return series was first drawn by Ding, Granger and Engle (1993), using asset returns. Baillie, Bollerslev and Mikkelsen (1996) developed the fractionally integrated GARCH, or FIGARCH, process to represent long memory in volatility. The measure of long-memory persistence in the volatility is employed either using the original rescaled range statistic by Hurst (1951) and its modified version proposed by Lo (1991). Further analysis of the presence of long memory persistence is conducted using autocorrelation analysis. All the findings point in the same direction, that is, the existence of long memory in volatility irrespective of the measure chosen. Estimation of different models of volatility is undertaken beginning with the ARCH specification and until the FIGARCH model. The results show the effects to be higher in Latin American countries than in the Asian ones. This result seems consistent with the degree of intervention in the Latin American markets, known to be much higher.<br>Other possible explanations for the occurrence of long term persistence are also pursued such as the Regime Switching modelisation proposed first by Hamilton and Susnel (1994) with the SWARCH approach. Results show that this approach can bring another possible explanation for persistence, specially in economies like Brazil that, have very different regimes for the period covered in this study.

This three year indepth study was prompted after a decade of working as a corporate advisor for numerous stockbroking firms' corporate advisory and listing divisions. An overwhelming lack of discernible pricing methodology for IPOs on the JSE's Main Board and failed Venture Capital and Development Capital Markets was transferred to the new Alternative Exchange (AltX). This prompted lengthly discussions with former head of JSE's AltX Noah Greenhill. Such discussions are set out in this dissertation and relate to pricing methodologies and the lack of guidance or legislation as set out in the JSE's schedule 21 of Listing requirements. The focus of this dissertation is thus centred on whether the current adopted methodologies to establish a fair and reasonable pre-IPO share price is effective. To achieve this, global pricing methodologies were assessed within the framework of various valuation techniques used by South African Designated Advisors.

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

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.

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

Stock return volatility has been shown to occasionally exhibit discrete structural shifts. These shifts are particularly evident in the transition from ‘normal’ to crisis periods, and tend to be more pronounced in developing markets. This study aims to establish whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the study, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. It is found that, even after accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important to capturing the heteroscedasticity evident in the data. However, those univariate models which include a GARCH term are shown to perform comparatively poorly when used for forecasting purposes. Additionally, in the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns when compared to portfolios that are constructed using the more conventional single-state models. While there is evidence that the use of some Markov-switching models can result in better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture the heteroscedasticity in the data remains problematic.

In 1993, Kenya liberalised its trade policy and allowed the Kenyan Shillings to freely float. This openness has left Kenya's domestic prices vulnerable to the effects of exchange rate fluctuations. One of the objectives of the Central Bank of Kenya is to maintain inflation levels at sustainable levels. Thus it has become necessary to determine the influence that exchange rate changes have on domestic prices given that one of the major determinants of inflation is exchange rate movements. For this reason, this thesis examines the magnitude and speed of exchange rate pass-through (ERPT) to domestic prices in Kenya. In addition, it takes into account the direction and size of changes in the exchange rates to determine whether the exchange rate fluctuations are symmetric or asymmetric. The thesis uses quarterly data ranging from 1993:Ql - 2008:Q4 as it takes into account the period when the process of liberalization occurred. The empirical estimation was done in two stages. The first stage was estimated using the Johansen (1991) and (1995) co integration techniques and a vector error correction model (VECM). The second stage entailed estimating the impulse response and variance decomposition functions as well as conducting block exogeneity Wald tests. In determining the asymmetric aspect of the analysis, the study followed Pollard and Coughlin (2004) and Webber (2000) frameworks in analysing asymmetry with respect to appreciation and depreciation and large and small changes in the exchange rate to import prices. The results obtained showed that ERPT to Kenya is incomplete but relatively low at about 36 percent in the long run. In terms of asymmetry, the results showed that ERPT is found to be higher in periods of appreciation than depreciation. This is in support of market share and binding quantity constraints theory. In relation to size changes, the results show that size changes have no significant impact on ERPT in Kenya.

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.

The aim of this thesis is to estimate the volatility function of continuoustime stochastic models. The estimation of the volatility of the following wellknown international stock market indexes is presented as an application: Dow Jones Industrial Average, Standard and Poor’s 500, NIKKEI 225, CAC 40, DAX 30, FTSE 100 and IBEX 35. This estimation is studied from two different perspectives: a) assuming that the volatility of the stock market indexes displays shortrange dependence (SRD), and b) extending the previous model for processes with longrange dependence (LRD), intermediaterange dependence (IRD) or SRD. Under the efficient market hypothesis (EMH), the compatibility of the Vasicek, the CIR, the Anh and Gao, and the CKLS models with the stock market indexes is being tested. Nonparametric techniques are presented to test the affinity of these parametric volatility functions with the volatility observed from the data. Under the assumption of possible statistical patterns in the volatility process, a new estimation procedure based on the Whittle estimation is proposed. This procedure is theoretically and empirically proven. In addition, its application to the stock market indexes provides interesting results.

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. <p><p>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<p>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. <p><p>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. <p><p>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<p>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. <p><p>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<p>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<p>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. <p><br>Doctorat en sciences économiques, Orientation économie<br>info:eu-repo/semantics/nonPublished

This dissertation first investigates the possible house price trend and the relationship with the mortgage market, from the perspective of risk management; then it chooses the angle from bond insurers and figures out possible methods to avoid capital procyclicality. In Chapter I, we apply vector auto regression models (VAR) and simultaneous equations models (SEM) to estimate the dynamic relations among house price returns, mortgage rates and mortgage default rates, using historical data during the time period of 1979 through second quarter 2008. We find that house prices would be better estimated and predicted with the consideration of the mortgage market. In Chapter II, following the methodology of co-integration, we first construct several succinct measures to display the possible intrinsic values of house prices. In the short run, house price return dynamics are investigated by dynamic adjustments following Capozza et al (2002) and error correction models. We examine the possible overshooting problem of house price returns. By analytical derivations and simulations, we demonstrate the effects of the coefficients on overshooting. In Chapter III, we adopt a structural model with time-varying correlations for bond insurers. We consider losses due to bond insurers’ downgrading and losses from both insurance contracts and investment portfolio. On that basis, we propose forward-looking smoothing rules of capital over a full business cycle, instead of only based on a short-term horizon, to avoid the procyclicality. With the smoothed capital, a bond insurer can actually establish some capital buffer in good times to support the potential losses in crisis.

An event study is an economic tool of analysis that has begun to gain popularity in recent empirical literature. It is a technique that gives a researcher the opportunity to map out the reaction of a firm's stock to an event, usually making use of daily or monthly data. However, up to this point, event study methodology has generally been applied to more traditional phenomena capable of affecting equity value, such as dividend and macroeconomic policy announcements, and there have only been a few exceptions to this. This study looks at what impact the tragic shootings at Lonmin mine in Marikana on August 16th 2012 had on the share prices of platinum mining firms based in South Africa using event study methodology. It makes use of the technique to investigate how the share prices responded to the tragedy over a number of trading days, including the day of the shootings. To be best of our knowledge, no attempt has been made to analyse the impact on share prices using events of this nature. For the investigation, daily returns data was used for each firm. The abnormal returns and cumulative abnormal returns to each were then calculated and compared with their respective expected returns in order to determine whether investors in the shares of that particular firm reacted positively, negatively or not at all. The evidence found suggests that tragedies of this nature are capable of influencing share prices in the same manner as more traditional economic phenomena. Overall, only one firm was found to have been negatively affected by the shootings in a persistent manner, while the shares of the other firms examined reacted in a manner that was positive overall, but varied according to individual firm characteristics such as size. These finding conformed to our a priori expectations. In addition, the results also confirm the benefits of applying event study methodology to a wide variety of phenomena that fall outside the boundaries usually associated with business.

It is observed that there are many different models about housing price. Yet, this is relatively smaller number of studies about housing starts. This thesis is an empirical study to work out the relationship between housing starts, housing price and other economic and policy instrumental factors. To achieve this objective, a Vector Autoregression (VAR) model is built since there is inter-relationship between housing starts and housing price. By applying previous models filled with the research gaps, a new VAR model about the housing starts and housing price in Hong Kong is built. Four hypotheses are tested in the thesis. The first and second hypotheses are if housing starts and housing price are affected by the given exogenous variables. The third hypothesis is if the past movement of economic variables reliable in predicting future values of that variable. The last hypothesis is to test if the “high-land-price” policy really pushes up the housing price. The empirical results found in this thesis are a little bit different to previous studies in Hong Kong and overseas. Factors which are frequently proved to be statistically significant are not significant in this study (e.g. interest rate and tender price index). Developers in Hong Kong are found to care more about the future market rather than the current market conditions. Many factors do not exert an influence directly on housing starts but indirectly through their impact to the change of the change of the housing price. It is interesting to know that housing starts react negatively to a change in housing price. An increase in the change of housing price is a bullish signal for the developers. They will hold the land for a while until they expect the peak is coming upon the completion of a project. Therefore, the empirical results suggest the government has to introduce some policies which will lead to a fall in housing price in case that she wants to increase the supply of new private residential housing. Developers will accelerate the applications to commence construction when they expect there will be a downward trend in the housing price (which is shown by a negative change of the housing price..<br>published_or_final_version<br>Real Estate and Construction<br>Master<br>Master of Philosophy

This dissertation is in the form of three essays on the topic of component and long memory GARCH models. The unifying feature of the thesis is the focus on investigating European index option evaluation using these models.<br>The first essay presents a new model for the valuation of European options. In this model, the volatility of returns consists of two components. One of these components is a long-run component that can be modeled as fully persistent. The other component is short-run and has zero mean. The model can be viewed as an affine version of Engle and Lee (1999), allowing for easy valuation of European options. The model substantially outperforms a benchmark single-component volatility model that is well established in the literature. It also fits options better than a model that combines conditional heteroskedasticity and Poisson normal jumps. While the improvement in the component model's performance is partly due to its improved ability to capture the structure of the smirk and the path of spot volatility, its most distinctive feature is its ability to model the term structure. This feature enables the component model to jointly model long-maturity and short-maturity options.<br>The second essay derives two new GARCH variance component models with non-normal innovations. One of these models has an affine structure and leads to a closed-form option valuation formula. The other model has a non-affine structure and hence, option valuation is carried out using Monte Carlo simulation. We provide an empirical comparison of these two new component models and the respective special cases with normal innovations. We also compare the four component models against GARCH(1,1) models which they nest. All eight models are estimated using MLE on S&P500 returns. The likelihood criterion strongly favors the component models as well as non-normal innovations. The properties of the non-affine models differ significantly from those of the affine models. Evaluating the performance of component variance specifications for option valuation using parameter estimates from returns data also provides strong support for component models. However, support for non-normal innovations and non-affine structure is less convincing for option valuation.<br>The third essay aims to investigate the impact of long memory in volatility on European option valuation. We mainly compare two groups of GARCH models that allow for long memory in volatility. They are the component Heston-Nandi GARCH model developed in the first essay, in which the volatility of returns consists of a long-run and a short-run component, and a fractionally integrated Heston-Nandi GARCH (FIHNGARCH) model based on Bollerslev and Mikkelsen (1999). We investigate the performance of the models using S&P500 index returns and cross-sections of European options data. The component GARCH model slightly outperforms the FIGARCH in fitting return data but significantly dominates the FIHNGARCH in capturing option prices. The findings are mainly due to the shorter memory of the FIHNGARCH model, which may be attributed to an artificially prolonged leverage effect that results from fractional integration and the limitations of the affine structure.

The Best PhD Thesis in the Faculties of Architecture, Arts, Business & Economics, Education, Law and Social Sciences (University of Hong Kong), Li Ka Shing Prize, 2006-2007.<br>published_or_final_version<br>abstract<br>Real Estate and Construction<br>Doctoral<br>Doctor of Philosophy

[Truncated abstract] This thesis examines competing asset-pricing models in Australia with the goal of establishing the model which best explains cross-sectional stock returns. The research employs Australian equity data over the period 1980-2001, with the major analyses covering the more recent period 1990-2001. The study first documents that existing asset-pricing models namely the capital asset pricing model (CAPM) and domestic Fama-French three-factor model fail to meet the widely applied Merton?s zero-intercept criterion for a well-specified pricing model. This study instead documents that the US three-factor model provides the best description of Australian stock returns. The three US Fama-French factors are statistically significant for the majority of portfolios consisting of large stocks. However, no significant coefficients are found for portfolios in the smallest size quintile. This result initially suggests that the largest firms in the Australian market are globally integrated with the US market while the smallest firms are not. Therefore, the evidence at this point implies domestic segmentation in the Australian market. This is an unsatisfying outcome, considering that the goal of this research is to establish the pricing model that best describes portfolio returns. Given pervasive evidence that liquidity is strongly related to stock returns, the second part of the major analyses derives and incorporates this potentially priced factor to the specified pricing models ... This study also introduces a methodology for individual security analysis, which implements the portfolio analysis, in this part of analyses. The technique makes use of visual impressions conveyed by the histogram plots of coefficients' p-values. A statistically significant coefficient will have its p-values concentrated at below a 5% level of significance; a histogram of p-values will not have a uniform distribution ... The final stage of this study employs daily return data as an examination of what is indeed the best pricing model as well as to provide a robustness check on monthly return results. The daily result indicates that all three US Fama-French factors, namely the US market, size and book-to-market factors as well as LIQT are statistically significant, while the Australian three-factor model only exhibits one significant market factor. This study has discovered that it is in fact the US three-factor model with LIQT and not the domestic model, which qualifies for the criterion of a well-specified asset-pricing model and that it best describes Australian stock returns.

This dissertation is in the form of one survey paper and three essays on the topic of volatility. The unifying feature that permeates the entire thesis is the focus on the measurement and use of conditional second moment of equities and currencies as a measure of risk for asset pricing and policy purposes in the context of international markets.<br>The survey examines selected papers from the international finance literature and from the volatility literature with a focus on the theoretical and empirical relationship between first and second unconditional and conditional moments of domestic and international asset returns. It then specifically proposes several areas for investigation related to international finance topics. The first essay investigates the importance of asymmetric volatility when computing the risk premium of international assets. The results indicate that conditional second moment asymmetry is significant and time-varying. They also show that, if the price of risk is time-varying, the world market and foreign exchange risk premia estimated without allowing for time-varying asymmetry are less consistent with the data. Furthermore, they imply that asymmetry is more pronounced when the business condition is such that investors require higher compensation to bear risk.<br>In the second essay we start from the consideration that financial decision makers often consider the information in currency option valuations when making assessments about future exchange rates. The purpose of this essay is then to systematically assess the quality of option based volatility, interval and density forecasts. We use a unique dataset consisting of over 10 years of daily data on over-the-counter currency option prices. We find that the implied volatilities explain a large share of the variation in realized volatility. Finally, we find that wide-range interval and density forecasts are often misspecified whereas narrow-range interval forecasts are well specified.<br>In the third essay we examine whether the information contained in various measures of correlation among exchange rates can be used to assess future currency co-movement. We compare option-implied correlation forecasts from a dataset consisting of over 10 years of daily data on over-the-counter currency option prices to a set of return-based correlation measures and assess the relative quality of the correlation forecasts. We find that while the predictive power of implied correlation is not always superior to that of returns based correlations measures, it tends to provide the most consistent results across currencies. Predictions that use both implied and returns-based correlations generate the highest adjusted R2's, explaining up to 42 per cent of the realized correlations.

Assignment (MAcc )--University of Stellenbosch, 2002.<br>ENGLISH ABSTRACT: The price-earnings ratio is one of a series of benchmarks developed after the Great Depression, to measure the fair value of shares on a relative basis. It originated from the idea that investors buy the earnings of a company and that the price-earnings ratio provides a consensus indication of the future growth potential of a company. Therefore, the price-earnings ratio is a rating of a company's future profitability. The price-earnings ratio developed, over the years, firstly, into an indicator of the relative risk associated with a company as the market anomalies associated with the ratio were investigated and clarified, and the theoretical background of the ratio integrated with the portfolio theory. It is now clear that the price-earnings ratio can be a useful indicator of the risk associated with an investment and the uncertainty associated with the duration of the growth phase of a company. Secondly, the price-earnings ratio is also a growth and valuation model with a theoretical background that can be linked to popular dividend discount models and the growth opportunities approach to investment valuation. With the use of the price-earnings ratio it is easy to visualise the relative profitability and the total investment required to raise a company's rating of future profitability. This simplicity allows one the opportunity to evaluate the reasonableness and likelihood of the investment reaching its projected potential profit targets. Lastly, as a result of accounting changes and the different accounting rules in force today, the price-earnings ratio also assists in the identification and elimination of the effects of accounting on investment decisions. It is apparent that the price-earnings ratio possesses the capabilities to assist investors significantly with the analysis of investment opportunities.<br>AFRIKAANSE OPSOMMING: Die prys-verdienste verhouding is een van 'n reeks relatiewe maatstawwe ontwikkel na die Groot Depressie om die redelike waarde van aandele te bepaal. Dit is gebaseer op die idee dat beleggers die winste van 'n maatskappy koop en dat die prys-verdienste verhouding 'n konsensus aanduiding verskaf van die toekomstige groeipotensiaal van 'n maatskappy. As gevolg hiervan is die prys-verdienste verhouding 'n aanduiding van die relatiewe toekomstige winsgewendheid van 'n maatskappy. Die prys-verdienste verhouding het oor die jare ontwikkel, eerstens as 'n aanwyser van die relatiewe risiko verbonde aan 'n maatskappy soos abnormaliteite wat daaraan verwant is ondersoek en verklaar is, en die teorieë onderliggend aan die verhouding ontwikkel het saam met die portefeulje teorie. Dit is nou duidelik dat die prys-verdienste verhouding 'n bruikbare aanduider is van die risiko wat geassosieer word met 'n belegging en die onsekerheid wat gepaard gaan met die duur van die groeifase van 'n maatskappy. Tweedens is die prys-verdienste verhouding ook 'n waardasie- en groeimodel met 'n teoretiese agtergrond wat verband hou met die populêre dividend verdiskonteringsmodelle en die groeigeleenthede-benadering tot waardasie. Met die gebruik van die prys-verdienste verhouding is dit maklik om die relatiewe winsgewendheid en die totale belegging wat benodig word om die waarde van die relatiewe winsgewendheid van 'n maatskappy te verhoog, tevisualiseer. Hierdie eenvoud verskaf die geleentheid om die redelikheid en die waarskynlikheid van 'n belegging om sy voorsiene winsgewendheidsdoelwitte te bereik, te evalueer. Laastens, as 'n resultaat van die rekeningkundige veranderinge, en die verskillende rekeningundige reëls huidiglik van toepassing in die wêreld, help die prys-verdienste verhouding ook met die identifikasie en die eliminasie van rekeningkundige komplikasies op beleggingsbesluite. Dit is duidelik dat die prys-verdienste verhouding die vermoë het om die belegger by te staan met die ontleding van beleggingsgeleenthede.

The effects of the March 1990 deregulation policy on the marketing of agricultural products are examined in terms of price levels, price variability and market integration for maize, tef, noug and sorghum for the Bako, Tibe and Shoboka markets of the Wollega and Shoa regions of Ethiopia. Weekly price data from 1986 to 1993 are used. The price level and price variability changes are tested using a T-test and F-test respectively while market integration is tested using traditional price correlation analysis and Granger's and Johansen's methods of cointegration analysis. Deregulation has resulted in an increase in real prices which has also, in most cases, been accompanied by an increase in price variability. The price correlation and Granger methods indicate improvement in market integration under deregulation while Johansen's method indicates similar levels of market integration for both regulated and deregulated marketing systems. Increased price variability might thwart the perceived benefits of deregulation and further research is needed to identify its causes and to provide appropriate policy recommendations.

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 dissertation explores different aspects of the interaction between money and asset prices. The first chapter investigates how a firm’s financing affects its decision to update prices: does linking interest rates to inflation alter the firm’s optimal price updating strategy? Building on the state dependent pricing models of Willis (2000) and the price indexing literature of Azariadis and Cooper (1985) and Freeman and Tabellini (1998), this model investigates the financing and price updating decisions of a representative firm facing state-dependent pricing and a cash-in-advance constraint. The model shows the circumstances under which a firm’s financing decision affects its price updating decision, and how the likelihood of changing prices affects the amount borrowed. It also illustrates how the use of nominal (as opposed to inflation-linked) interest rates leads to a lower frequency of price updating and higher profits overall for a firm facing menu costs and sticky prices. The second chapter extends the bank run literature to present a theoretical mechanism that explains how money supply can affect asset prices and asset price volatility. In a two period asset allocation model, agents faced with uncertainty cannot perfectly allocate assets ex-ante. After income shocks are revealed, they will be willing to pay a premium over the future fundamental value for an asset in order to consume in the current period. The size of this premium is directly affected by the supply of money relative to the asset. This paper explores the relationship between economy-wide monetary liquidity on the mean and variance of equity returns and in relation to market liquidity. At an index level, I test the impact of money-based liquidity measures against existing measures of market liquidity. I proceed to do a stock level analysis of liquidity following Pastor and Stambaugh (2003). The results indicated that measures of aggregate money supply are able to match several of the observed relationships in stock return data much better than market liquidity. At an individual stock level, monetary liquidity is a priced factor for individual stocks. Taken together, these papers support the idea that changes in the money supply have consequences for the real economy.<br>text

Cheung Ho Sang.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.<br>Includes bibliographical references (leaves 185-187).<br>Abstracts in English and Chinese.<br>Curriculum Vitae --- p.ii<br>Acknowledgments --- p.iii<br>Abstract --- p.v<br>Table of Contents --- p.viii<br>Chapter Chapter 1. --- Introduction --- p.1<br>Chapter Chapter 2. --- The behavior of income velocity of money --- p.3<br>Chapter 2.1 --- Introduction --- p.3<br>Chapter 2.2 --- Literature Review --- p.4<br>Chapter 2.3 --- Data Description --- p.9<br>Chapter 2.4 --- Methodology --- p.9<br>Chapter 2.5 --- Empirical Result --- p.16<br>Chapter 2.6 --- Conclusion --- p.26<br>Chapter Chapter 3. --- The behavior of equity premium --- p.106<br>Chapter 3.1 --- Introduction --- p.106<br>Chapter 3.1 --- Literature Review --- p.106<br>Chapter 3.2 --- Data Description --- p.112<br>Chapter 3.3 --- Methodology --- p.112<br>Chapter 3.4 --- Empirical Result --- p.120<br>Chapter 3.5 --- Conclusion --- p.130<br>Data Appendices --- p.182<br>Bibliography --- p.185

Chen Haiqiang.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 33-35).<br>Abstracts in English and Chinese.<br>Chapter 1. --- Introduction --- p.1<br>Chapter 2. --- The Model --- p.4<br>Chapter 3. --- Least Squares Estimations --- p.6<br>Chapter 4. --- Inference --- p.7<br>Chapter 4.1 --- Asymptotic Joint Distribution of the Threshold Estimators --- p.7<br>Chapter 4.2 --- Testing Threshold Effect: Model Selection Followed by Testing --- p.13<br>Chapter 5. --- Modeling --- p.16<br>Chapter 5.1 --- Generic Consistency of the Threshold Estimators under specification errors --- p.17<br>Chapter 5.2 --- Modeling Procedure --- p.20<br>Chapter 6. --- Monte Carlo Simulations --- p.21<br>Chapter 7. --- Empirical Application in the Financial Market --- p.24<br>Chapter 7.1 --- Data Description --- p.26<br>Chapter 7.2 --- Estimated Results --- p.26<br>Chapter 8. --- Conclusion --- p.30<br>References --- p.33<br>Appendix 1: Proof of theorem1 --- p.36<br>Appendix 2: Proof of theorem2 --- p.39<br>Appendix 3: Proof of theorem3 --- p.43<br>List of Graph --- p.49

Su Qian.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 66-70).<br>Abstracts in English and Chinese.<br>Abstract --- p."i,ii"<br>Acknowledgements --- p.iii<br>Table of Content --- p.iv<br>List of Tables and Figures --- p.v<br>List of Abbreviation --- p.vi<br>Chapter Chapter 1. --- Introduction --- p.1<br>Chapter Chapter 2. --- Literature Review --- p.4<br>Chapter 2.1 --- Benefits of Cross-listing --- p.4<br>Chapter 2.2 --- The Price-discovery process of cross-listed stocks --- p.8<br>Chapter 2.3 --- Previous studies on Chinese cross-listed stocks --- p.2<br>Chapter Chapter 3. --- China Overseas Listing --- p.15<br>Chapter 3.1 --- The history of overseas listing --- p.15<br>Chapter 3.2 --- Methods of overseas listing --- p.17<br>Chapter 3.3 --- The motivation for Chinese firms to list overseas --- p.18<br>Chapter 3.4 --- The prospects of China Overseas listing --- p.21<br>Chapter Chapter 4. --- Price-discovery contributions to China-backed stocks cross-listed on SEHK and NYSE --- p.23<br>Chapter 4.1 --- Data --- p.23<br>Chapter 4.2 --- Methodology --- p.25<br>Chapter 4.3 --- Empirical Results and Interpretation --- p.31<br>Chapter 4.4 --- Cross-Sectional analysis of NYSE contributions to the price-discovery process --- p.40<br>Chapter Chapter 5. --- Price-discovery contributions to the cross-listed H share and A share --- p.45<br>Chapter 5.1 --- Data and Sample details --- p.46<br>Chapter 5.2 --- Methodology --- p.49<br>Chapter 5.3 --- Empirical results and interpretation --- p.54<br>Chapter 5.4 --- A brief analysis of cointegration determinants --- p.57<br>Chapter 5.5 --- The cointegration between H share and A share- Daily analysis --- p.61<br>Chapter Chapter 6. --- Conclusion --- p.64<br>Reference --- p.66<br>Tables --- p.71

This Paper empirically investigates the behavior of Africa’s stock price volatility over time in ten African equity markets. It also attempts to establish the existence of a relationship between volatility and expected returns in the chosen equity markets. The effect of volatility on the stock prices is also investigated, together with establishing variations in the stock return volatility risk premia. Lastly, an investigation of whether volatility is transmitted from international markets to African markets is also undertaken. The sample period starts from November 1998 until December 2016. The preliminary empirical results show a mixed finding in the mean-variance tradeoff theory. Based on the GARCH-type models, the empirical results show that volatility of stock returns show the characteristics of volatility clustering, leptokurtic distribution and leverage effects over time for all the Africa equity markets. A weak relationship between volatility and expected returns is also found in all the African equity markets studied. The results also showed that as volatility increases, the returns correspondingly decrease by a factor of the coefficient for most of the equity markets. These results negate the theory of a positive risk premium on stock indices. It was also observed that stock return volatility risk premia have variations over time. The study also established that there was volatility transmission from the international markets into Africa equity markets.<br>MT2017

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016.<br>This paper builds upon Blanchard's (1981) model of asset prices, and provides an empirical evidence for good news cases (GNC) and/or bad news cases (BNC) as de ned in Blanchard's paper. We update Blanchard's model by introducing Taylor's rule of monetary policy and explicitly incorporate income distribution in a small, open economy. The ndings indicate that, the labour share is a strong and signi cant variable that should be considered in asset pricing models. The real exchange rate plays a signi cant role in the determination of asset prices in most of the selected countries, but the signi cance is stronger in the emerging markets economies. As the main objective of the paper, the study has found four of the selected countries to be bad news cases and eight of them are good news cases.<br>MT2016

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

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

by Lee Chi Kau.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 115-119).<br>Abstract also in Chinese.<br>ACKNOWLEDGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.vi<br>CHAPTER<br>Chapter ONE --- INTRODUCTION --- p.1<br>Chapter TWO --- THE LITERATURE REVIEW --- p.6<br>Parametric Models<br>Nonparametric Estimation Techniques<br>Chapter THREE --- ANALYTICAL FRAMEWORKS --- p.21<br>Parametric Models<br>Nonparametric Models<br>Chapter FOUR --- EMPIRICAL FINDINGS --- p.36<br>Data<br>Estimation Results<br>Evaluation of Model Performance<br>Out-of-Sample Forecast and Evaluation<br>Chapter FIVE --- CONCLUSION --- p.54<br>TABLES --- p.58<br>ILLUSTRATIONS --- p.76<br>BIBLIOGRAPHY --- p.115

Farm equipment is becoming an increasingly important financial asset for many farmers. Tractors probably represent the single largest component of equipment asset value. As such, changes in tractor values can have a dramatic effect on a farmer's financial situation. Changes in equipment value can be attributed to depreciation and the value of output produced. The general objective of this study was to identify a specific set of variables explaining changes in equipment value and to determine the relative importance of these variables. The Box-Cox power transformation technique was employed in estimating the depreciation patterns. The method was applied to two different sources of used tractor prices--auction and advertised. Remaining value (RV), defined as the real market price in time t divided by real purchase price, was regressed against several independent variables. These independent variables were age, usage per year, condition, horsepower, manufacturer, regions of the U.S., auction types, and net farm income. A number of these variables were found to have some important impact on RV. Depreciation patterns were found to differ between manufacturers. Significant differences in remaining values (RV) were found to exist for different regions of the U.S. and different auction types. For both auction and advertised data, an increase in usage produces a noticeable decrease in RV. For auction data, however, the level of usage tends to have greater influence on RV when the tractor is newer. The results did not closely approximate any clear depreciation pattern. The depreciation patterns are accelerated relative to straight-line method and are a combination of the geometric and sum-of-the-year's digits functions. The RV model was used to examine optimal replacement ages for farm tractors. Annual usage levels had the most influence on the age at which tractors were replaced. Expensing and some tax law changes had a less significant impact.<br>Graduation date: 1989

Lam Suet-man.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 111-117).<br>Abstracts in English and Chinese.<br>ACKNOWOLEDGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.vi<br>CHAPTER<br>Chapter ONE --- INTRODUCTION --- p.1<br>Chapter TWO --- REVIEW OF THE LITERATURE --- p.6<br>Parametric Models<br>Nonparametric Estimation Techniques<br>Chapter THREE --- METHODOLOGY --- p.21<br>Parametric Models<br>Nonparametric Models<br>Chapter FOUR --- DATA DESCRIPTION --- p.33<br>Chapter FIVE --- EMPIRICAL FINDINGS --- p.39<br>Estimation Results<br>Evaluation of Model Performance<br>Out-of-sample Forecast Evaluation<br>Chapter SIX --- CONCLUSION --- p.58<br>TABLES --- p.62<br>ILLUSTRATIONS --- p.97<br>APPENDIX --- p.107<br>BIBOGRAPHY --- p.111