Taiwan is a crowded island with a large population but limited soil resource. The government makes the most effective use of the land by making various integrative developments in order to create the maximum use of social welfare. The idea of land reorganization thus came out. Take the metropolis of Kaohsiung for example, the implementation of land readjustment not only led to economic prosperity, but also created a spillover-effect like the growth of land utilization, construction, population and industries inside and outside the reorganization areas. It also brought enormous benefits to the government, land owners and the whole citizens as well. The fluctuation of real estate market price in Taiwan is easily affected by its economic situation, people's fear of the expectation on the increase of price index, the domestic idle capital flood, and the dramatic rising of the stock market, which lead to the enormous growth of land value, so there should be a long-term balance among economic factors (GDP, interest rate, and exchange rate) and land value. As for the researches of the interrelationship between the analysis of the land value change of real estate and economic factors, most scholars chose cities in northern Taiwan as an example, fewer researches had been made for Kaohsiung City in southern Taiwan. This paper, different from others, analyzes the public tender data of lands in every reorganization area in Kaohsiung City from the past few years. This paper aims at the long-term relationship of cointegration between the public tender data over the years of lands of readjustment area in Kaohsiung City and economic factors. The sample date is a long-term relationship from the year of 1962 to 2004 on such four parameters as Land value, GDP, interest rate, and exchange rate, which are the objects of this study, adopting the unit root test and Johansen¡¦s Maximum Likelihood Estimation (MLE) as studying tools. As this study finds out, the phenomenon of cointegration really exists among these four parameters. The land value has negative correlation with interest rate, and positive correlation with the GDP, and exchange rate.

Diss. Uppsala : Univ., 2003.

The grain and oil seed industry plays a major role in the South African economy; therefore, having access to market information is vital for this market to remain efficient and competitive. A shortage in market information causes many inefficiencies and uncertainties. Having market information allows the playing field to be level for all role players and reduces opportunities for manipulating prices. South Africa, just like most developing countries, needs to strengthen information flows, as well as institutions governing the grain and oil seed industry. In view of the major grain producing countries in the world and the amount of money and effort spent on releasing planting progress reports, the South Africa grain and oilseed sector should to take heed. This paper considers the importance of market information and how the South African grain and oil seed industry can benefit from that, grain planting progress reports are considered to be of importance as they fill a significant gap in the production season. Taking an institutional perspective into the economics of information, the study found that actors having little financial and social resources or political influence faced high costs in accessing information and that this prevents both market development and access to existing ones. The point of discussion is on weak information flows, as well as transaction costs that come with them, and the impact they have on prices and profitability. We therefore use New Institutional Economics to emphasise the importance of information in the market and the impact thereof in the absence of perfect information. The main underlying issue for imperfect information is that the lack of perfect and freely available information leads to risk and uncertainty in transactions. When trying to analyse the importance of information in the grain and oilseed industry, it was established that accuracy, value and market effect of information for public consumption were important. In particular, information communication technology was examined as a means of information dissemination in agriculture, especially in developing countries like South Africa. The study found that the major grain and oilseed producing countries that generate planting progress reports are the USA, Brazil, Argentina and Australia. The study looked at the methods used by these countries to compile such reports. Although they have varying methodologies, the key point is timely and frequent information which is readily available for public consumption. After analysing developments and methodologies globally, the focus shifted to South Africa where current information sources in the South African grain and oilseed industry, and the kind of information provided, were analysed. A pilot study was conducted in the summer grain production area of NWK Ltd to gain some insight and experience. The source of communication comprised mobile phones and farmers were able to respond on their progress, as well as receive feedback using the same communication media. Lastly in order to re-emphasis the benefits of a planting progress report, we review the impact of price volatility and how information in the market can help stabilise it. / Dissertation (MSc (Agric))--University of Pretoria, 2017. / Agricultural Economics, Extension and Rural Development / MSc (Agric) / Unrestricted

This study discusses about the relationships between the qualities of collateralized shares by the broad of directors and the bank performance. In this study, we focus on the quantitative indicators of collateralized shares. Base on individual collateralization data, we build up the sets of the loans permitted by banks. In additon, this study is based on the multiple regression model to find out the relationships between the qualities of the collateral loans and the bank performance. By the conclusion, this study tries to give some advice to the banks about measuring the loans with collateralized shares. There are few conclusion of this study: 1. The stocks with higher price volatility are not good collaterals. The banks which have the loan with the collaterals with higher price volatility usually have bad proformance. The banks should pay attension to this indicator. 2. The collaterals are better with high ¡¥market price-to-book value¡¦. 3. By literature review, the higher proportion of collateralized shares by the broad of directors, the shares seem to be the worse collaterals for the banks. However, in this study, we find out some trade-off relationship between the profit and the risk in measuring this indicator.

This diploma thesis brings theory, current knowledge and empirical analysis. It focuses on the methamphetamine market in the Czech Republic. In its theoretical part it follows individual schools of Economics with focus on the differences in expected behavior of the participants in the illegal market. The research is primarily oriented on finding the effects of the enforced prohibition on users, producers and sellers of pervitin. The main benefit of the diploma is the definition of as yet not published specifics about not only the whole segment, but also the comparison of characteristics of both an open and closed pervitin scene, where surprisingly there exists not only a difference in the quality of the product but also in the approach of sellers to customers. From a comparison with the heroin market one can see a trend of mutual exchanges of pervetin for heroin and also differences in competitive environments. The result of the economic analysis is the filling of gaps in the current understanding and elicitation of conclusions, which are compared to the economical studies and official overviews of the Czech drugs market.

Electricity price forecasting is an important task for electricity market participants since the very beginning of the deregulation. Accurate forecasting is essential for designing bidding strategy, risk management, and market operation. However, due to the compli-cated factors affecting electricity prices, there are more uncertainties in electricity price forecasting and hence more complex than demand forecasting. This makes accurate price forecasting very difficult. In the last decade, several methods have been developed in order to fully capture the peculiarities of electricity price dynamics, from classic econometric time series models, e.g., autoregressive moving average (ARMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model to modern machine learning based techniques such as artificial neural networks (ANN) and sup-port vector machine (SVM). In spite of all models proposed in the literature, there is still no clear consensus about which model is substantively outperforming others. Therefore, when a single method is used, decision-makers are facing the risk of not choosing the best one. On the other hand, the prediction of electricity market prices still involves large errors. If decision-makers take the prediction result on faith, prediction errors could exposure them to serious financial risks. Based on these findings, it can conclude that (1) systematic methodologies and implementations which can efficiently address model selection uncertainty in price forecasting require an investigation; (2) more powerful and robust price forecasting models are still needed to reduce the fore-cast errors; and (3) In addition, the emphasis of price forecasting should shift away from point forecast to uncertainty around the forecast. Unfortunately, most researches in this area have been devoted to finding the single “best” estimates rather than dealing with the uncertainty in model selection and quantifying the predictive uncertainty. In this thesis the research focus is on: (1) finding methodologies and efficient imple-mentations to deal with the uncertainty in model selection; (2) developing more power-ful machine learning based approaches to model electricity spot prices and further im-proving the accuracy of electricity market price forecast; and (3) incorporating uncer-tainty estimation into the application of price forecasting. The thesis makes three main contributions to the study of this topic. Firstly, it proposes linear, nonlinear forecast combination frameworks to deal with model selection prob-lem; secondly, it introduces two novel models: support vector machine based nonlinear generalized autoregressive conditional heteroscedasticity model (SVM-GARCH) and extreme learning machine (ELM) to the price forecasting and furthermore gives a series of bootstrap-based interval construction procedures to quantify the prediction uncer-tainty. Finally, it proposes a more robust interval forecasting approach which is based on quantile regression to electricity price forecasting literature. The effectiveness and efficiency of the proposed approaches have been tested based on real market data of Australian National Electricity Market (NEM).

Electricity price forecasting is an important task for electricity market participants since the very beginning of the deregulation. Accurate forecasting is essential for designing bidding strategy, risk management, and market operation. However, due to the compli-cated factors affecting electricity prices, there are more uncertainties in electricity price forecasting and hence more complex than demand forecasting. This makes accurate price forecasting very difficult. In the last decade, several methods have been developed in order to fully capture the peculiarities of electricity price dynamics, from classic econometric time series models, e.g., autoregressive moving average (ARMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model to modern machine learning based techniques such as artificial neural networks (ANN) and sup-port vector machine (SVM). In spite of all models proposed in the literature, there is still no clear consensus about which model is substantively outperforming others. Therefore, when a single method is used, decision-makers are facing the risk of not choosing the best one. On the other hand, the prediction of electricity market prices still involves large errors. If decision-makers take the prediction result on faith, prediction errors could exposure them to serious financial risks. Based on these findings, it can conclude that (1) systematic methodologies and implementations which can efficiently address model selection uncertainty in price forecasting require an investigation; (2) more powerful and robust price forecasting models are still needed to reduce the fore-cast errors; and (3) In addition, the emphasis of price forecasting should shift away from point forecast to uncertainty around the forecast. Unfortunately, most researches in this area have been devoted to finding the single “best” estimates rather than dealing with the uncertainty in model selection and quantifying the predictive uncertainty. In this thesis the research focus is on: (1) finding methodologies and efficient imple-mentations to deal with the uncertainty in model selection; (2) developing more power-ful machine learning based approaches to model electricity spot prices and further im-proving the accuracy of electricity market price forecast; and (3) incorporating uncer-tainty estimation into the application of price forecasting. The thesis makes three main contributions to the study of this topic. Firstly, it proposes linear, nonlinear forecast combination frameworks to deal with model selection prob-lem; secondly, it introduces two novel models: support vector machine based nonlinear generalized autoregressive conditional heteroscedasticity model (SVM-GARCH) and extreme learning machine (ELM) to the price forecasting and furthermore gives a series of bootstrap-based interval construction procedures to quantify the prediction uncer-tainty. Finally, it proposes a more robust interval forecasting approach which is based on quantile regression to electricity price forecasting literature. The effectiveness and efficiency of the proposed approaches have been tested based on real market data of Australian National Electricity Market (NEM).

The diploma thesis deals with the influence of spatial planning on the market price of land in Vyškov. The first part deals with the theoretical background needed to understand the issue. The following chapter describes the problems, sets out the hypotheses and goals of the solution of this work. Furthermore, the methods used to fulfill the goal are described and justified. At the end of the first part there is a chapter where the solved locality is described together with selected plots. The following is a part of the solution itself, which contains the valuation of selected plots in three stages of spatial planning documentation. An analysis of the market of the city Vyškov is used here and subsequently a comparative database for land valuation using a comparative direct method is created. Finally, the achieved results of the solution are summarized and the effects of spatial planning affecting the market price of land in Vyškov are interpreted.

The diploma thesis is focused on price discrimination of a selected company operating in an international environment. The work is divided into three main parts. The theoretical part of the thesis describes the key concepts and characteristics necessary to understand the issues related to the topic of the thesis. The analytical part of the work is focused on the description of the current state of the company in selected foreign markets. The proposal part of the work used the method of multiple regression analysis and also presents possible proposals and recommendations for price discrimination in individual foreign markets with regard to the purchasing power of individual countries.

This diploma thesis deals with the real estate trading theme, this means the construction market and the real estate market. The introduction is devoted to basic terms, followed by familiarization with the investment project and the division of market to the construction one and the one with the real estate. There are also defined subjects (participants) of these markets and institutions that can affect these markets. The main part of the thesis is the description of a development project from pre-investment phase to final sale or lease of real estate. It describes the course of construction project in the perspective of the developer. The final part is focused to an economic evaluation of the project, determining the profitability of rental and sale of individual parts of the building