<p> Probability forecasts play an important role in many decision and risk analysis applications. Research and practice over the years have shown that the shift towards distributional forecasts provides a more accurate and appropriate means of capturing risk in models for these applications. This means that mathematical tools for analyzing the quality of these forecasts, may it come from experts, models or data, become important to the decision maker. In this regard, strictly proper scoring rules have been widely studied because of their ability to encourage assessors to provide truthful reports. This dissertation contributes to the scoring rule literature in two main areas of assessment - probability forecasts and quantile assessments. </p><p>In the area of probability assessment, scoring rules typically studied in the literature, and commonly used in practice, evaluate probability assessments relative to a default uniform measure. In many applications, the uniform baseline used to represent some notion of ignorance is inappropriate. In this dissertation, we generalize the power and pseudospherical family of scoring rules, two large parametric families of commonly-used scoring rules, by incorporating the notion of a non-uniform baseline distribution for both the discrete and continuous cases. With an appropriate normalization and choice of parameters, we show that these new families of scoring rules relate to various well-known divergence measures from information theory and to well-founded decision models when framed in an expected utility maximization context. </p><p>In applications where the probability space considered has an ordinal ranking between states, an important property often considered is sensitivity to distance. Scoring rules with this property provide higher scores to assessments that allocate higher probability mass to events “closer” to that which occurs based on some notion of distance. In this setting, we provide an approach that allows us to generate new sensitive to distance strictly proper scoring rules from well-known strictly proper binary scoring rules. Through the use of the weighted scoring rules, we also show that these new scores can incorporate a specified baseline distribution, in addition to being strictly proper and sensitive to distance. </p><p>In the inverse problem of quantile assessment, scoring rules have not yet been well-studied and well-developed. We examine the differences between scoring rules for probability and quantile assessments, and demonstrate why the tools that have been developed for probability assessments no longer encourage truthful reporting when used for quantile assessments. In addition, we shed light on new properties and characterizations for some of these rules that could guide decision makers trying to choosing an appropriate scoring rule. </p> / Dissertation

This paper discusses the factors that affect the share price index of Taiwan solar power industry, crude oil prices and the size of the solar manufacturers in Taiwan and Taiwan's market index into the consideration. In addition, considering whether the policies implemented by our government would change the solar industry in Taiwan¡¦s stocks structural .Using the correlation coefficient, the unit root test, Chow test, cointegration test , vector error correction model, impulse response and forecast error variance decomposition to explore their relationship respectively. The study period starts from January 3,2002 until December 30,2011, a total of 2450 daily data for empirical analysis. By Chow test , we find that there is no structural change of solar stock index after the implementation of the domestic policies. Three international crude oil prices and the total share capital of solar manufacturers in Taiwan and the Taiwan solar power industry stocks index has co-integration relationship, means the three international crude oil prices and solar companies total share capital of solar stock index has a long-run equilibrium relationship. By the error correction model of West Texas crude oil price of Brent crude oil prices, the total share capital of the solar companies in Taiwan and Taiwan solar stock index mutual interaction, and the relationship between changes in Taiwan's solar stock price index and Brent crude oil price, West Texas crude oil prices and the total manufacturers of solar energy manufacturers in Taiwan¡¦s share capital are positive, besides, I also found a positive relationship in the impulse response.

The business environment nowadays becomes much more dynamically and tensely than the past driven by the trend of globalization and free trading. Therefore, any enterprise in the world has to face competition from everywhere in the world. Under such complicated business environment, it¡¦s dangerous to make decision based on past experience or instinct. If some key message is missed or not collected, a disaster caused by logical decision, but far away from the reality might just happen. In last couple decades, software providers launched DSS(Decision Support System), BI(Business Intelligence), ¡K, etc. based on current enterprise IT infrastructure like ERP (Enterprise Resource Planning), MRPII(Manufacturing Resource Planning), ¡K, etc. to help enterprise for decision making. However, such systems are not popular in Taiwan, not to mention the successful stories. While I studied the lesson ¡§information technology and competitive advantage¡¨ conducted by Profession Kuo, I concluded from classmates¡¦ discussion that the root causes were as follows. 1. Most Taiwan manufacturers¡¦ strategy is to launch product at lower cost to allow them to win business via price war. Therefore, they are willing to invest tangible hardware, not intangible software. 2. The branches of international companies can¡¦t develop their own information system due to Corporate policy or security concern. Based on above mentioned, I started thinking if we could have a BI system that doesn¡¦t need to spend money, is easy to implement, and no need for Corporate approval. Such BI system could help management to retrieve effective and enough information for precise decision making. After evaluation, I think Microsoft Excel spreadsheet software is the most suitable solution. It¡¦s because almost all enterprises have it, it can contain 1M units of data in a file, and useful tools of macro, pivot table, sorting, filtering, VBA(Visual Basic for Application). Furthermore, the nature of spreadsheet is similar to database structure, so it can be easily integrated with database like SQL database, Microsoft Access. Thanks to Profession Kuo¡¦s coaching, I started doing research, and studied necessary tool like VBA, ¡K, etc. to warm up for this thesis. After months, I finally finish it, and I hope it can contribute to the ones that have similar problem with me.

This study includes three issues about the dynamic of 30-days Taiwan Commercial Paper rate (CP2).The first issue focuses on the estimation of continuous-time short-term interest rate models. We discretize the continuous-time models by using two different approaches, and then use weekly and monthly data to estimate the parameters. The models are evaluated by data fit. We find that the estimated parameters are similar for different discretization approaches and would be more stable and efficient under quasi-maximum likelihood (QML) with weekly data. There exists mean reversion for Taiwan CP rate and the relationship between the volatility and the level of interest rates are less than 1 and smaller than that of American T-Bill rates reported by CKLS (1992) and Nowman (1997). We also find that CIR-SR model performs best for Taiwan CP rate. The second issue compares the continuous-time short-term interest rate models empirically both by predictive accuracy test and encompassing test. Having the estimated parameters of the models by discretization of Nowman(1997) and QML, we produce the forecasts on conditional mean and volatility for the interest rate over multiple-step-ahead horizons. The results indicate that the sophisticated models outperform the simpler models in the in-sample data fit, but have a distinct performance in the out-of-sample forecasting. The models equipped with mean reversion can produce better forecasts on conditional means during some period, and the heteroskedasticity variance model with outperform counterparts in volatility forecasting in some periods. The third issue concerns the persistent and massive volatility of short-term interest rates. This part inquires how the realizations on Taiwan short-term interest rates can be best described empirically. Various popular volatility specifications are estimated and tested. The empirical findings reveal that the mean reversion is an important characteristic for the Taiwan interest rates, and the level effect exists. Overall, the GARCH-L model fits well to Taiwan interest rates.

Abstract Japan is the second largest economic power in the world. It has a great deal of FDI outflows but few FDI inflows. Therefore, Japan is in the serious situation of ¡§FDI balance of payments deficit.¡¨ In terms of inward FDI stocks as a percentage of GDP and gross fixed capital formation, Japan is the lowest place of G-7. The purpose of this research is focusing on discussing the shortage of FDI inflows and causing factors which lower the desires of investments in Japan by using the simplest way which is based on the actual situation and the limit of the information in Japan. This paper takes the quarterly data of Japan from 1978 to 2005 and four variables (wage index, real exchange rate, trade and FDI inflows). In this research, the unit root test is used to check if the data have the stationarity or not, and then it uses vector autoregression model (VAR) to proceed impulse response function and forecast error variance decomposition. According to the result of these two approaches, we can figure out the influences of four variables for each other, and then find out the causing factors which lead Japan to have less FDI inflows. The calculation shows that the reason which leads Japanese wages to increase gradually results not only from real exchange rate, trade and FDI inflows, but also from Japanese labor system (lifetime employment system and payment according to working seniority) and the labor quantities. The causality runs from real exchange rate to trade is greater than vice versa. Trade has a positive impact from the real exchange rate which means that the depreciation can accelerate trade. However, the main factor of hindering FDI inflows is Japanese high wages rather than real exchange rate or trade. Therefore, in order to get rid of the depression which was caused by the bubble economy in 1990s, Japanese government not only opens up the restrictions in policy but also takes the control of the prime costs into the most important consideration.

The enterprise finance crisis forecast could provide alarm to managers and investors of the enterprise, many scholars advised different alarm models to explain and predict the enterprise is facing finance crisis or not. These models can be classified into three categories by analysis method, the first is single-variate model, it¡¦s easy to implement. The second is multi-variate model which need to fit some statistical assumption, and the third is Artificial Neural Network model which doesn¡¦t need to fit any statistical assumption. However, these models do not consider the industrial effect, different industry could have different finance crisis pattern. This study uses the advantage of Artificial Neural Network to build the process of the enterprise finance crisis forecast model, because it doesn¡¦t need to fit any statistical assumption. Finally, the study use reality finance data to prove the process, and compare with the other models. The result shows the model issued by this study is suitable in Taiwan Electronic Industry, but the performance in Taiwan architecture industry is not better than other models.

A risk assessment model (MAcRisk) have been developed to forecast the probability and the risk of maritime accidents on Bosphorus. Accident archives of Undersecretariat Maritime Affairs Search and Rescue Department, weather conditions data of Turkish State Meteorological Service and bathymetry and current maps of Office of Navigation, Hydrography and Oceanography have been used to prepare the model input and to forecast the accident probability. Accident data has been compiled according to stated sub-regions on Bosphorus and event type of accidents such as collision, grounding, capsizing, fire and other. All data that could be obtained are used to clarify the relationship on accident reasons. An artificial neural network model has been developed to forecast the maritime accidents in Bosphorus.

Fractal was first used in measuring the length of the coastline, with the fractal research and development, not only to break the traditional Archimedean geometry, but also to explain many scientific to ignore the complexity and nature of nonlinear phenomena structure .Fractal has been widely applied to such as physics, astronomy, geography and sociology and other fields, as a wave of interdisciplinary research in recent years. Coastal areas has always been cultural, economic and activities areas since ancient times. Coastal zone was land and sea for the interaction region by a variety of factors (ex: waves, tides, currents and wind, etc.) continue to function, derived from different coastal terrain. Therefore changes in the coast of the deep impact of humanity. Under the principle of the conservation and development, Coastal areas should be use of modern technology to prediction, analysis, assessment, planning, and management, so that a sustainable preservation of coastal resources. In this study, static and dynamic predict and simulation the coast shape base on fractal. The static part is observation of 29 beaches in South China coast. And collect and calculate the parameters and fractal dimensions of the coast. Through the shape of image processing and analysis of information, to find two generators of the coast. Through the data mining technology to identify the criteria for classification, and to simulation the coastline by generate iterations method. The dynamic part is based on hydraulic model¡¦s results, the use of traditional multiple linear regression and neural network to compare the dynamic prediction of the coastline. The results show that the use of neural networks to predict than the use of multiple linear regression, and effect of use difference angle (£c) to predict sub-coastlines than the effect of not use difference angle (£c) to predict, and add fractal dimension can effectively reduce the predict error and increase the degree of interpretation.

<p>This paper uses co-integration method and error-correction model to re-examine the relationship between real exchange rate and expected interest rate differentials, including cumulated current account balance, over floating exchange rate periods. As indicated by the dynamic model, I find that there is a long run relationship among the variables using Johansen co-integration method. Final conclusion is that the empirical evidence is provided to show that our error-correction model leads to a good real exchange rate forecast.</p>

This study examines whether earnings guidance contributes to investor short-termism -- excessive focus on a firm's short term performance and insufficient consideration of its long-term value creation potential. Using an adaptation of Ohlson's (1995) valuation model, I find that investors place significantly higher (lower) weight on short-term (long-term) earnings of quarterly guidance firms than on the corresponding earnings of non-guidance firms. Further tests indicate that the differential weighting cannot be fully explained by measurement errors, earnings properties, risk, or accuracy of analysts' forecasts. For a sample of guidance initiating firms, I find no differential valuations of firm value components before the initiation of guidance, but large differential valuations after guidance initiation. In contrast, for guidance discontinuation firms, I find that investors shift their focus from short-term to long-term earnings after the discontinuation of guidance. Together, the results support critics' claim that quarterly guidance contributes to short-term fixation in the market. / text