This study set out to investigate the relationship between returns in the residential property markets and two key economic variables of output and interest rates. The main focus was on the short-term rates path and how it is influenced by the Taylor rule fundamentals and in turn, its effect on the returns in the property markets within the developing countries of South Africa, Bulgaria, Lithuania and Czech Republic. A secondary focus was on building a model that can be further developed into a full forecasting model of returns in the residential property markets. Output was found to be a strong driver of returns in the residential property markets across all four countries. Real changes in the economic activity feed into the residential property markets and drives returns. Output can be incorporated into a forecasting framework for returns in the residential property markets within these countries The short-term rate paths within the countries studied were found to be consistent with the Taylor rule but with heavy short run deviations from the rule. Short-term rates deviated from the rule in the short run, but showed a tendency to revert to the rule in subsequent periods. Returns and prices in the property markets were driven by the short-term rates only in two of the emerging markets. For these countries, this link between rate and returns mean there was also a link between monetary policy and returns in the property sector. Similar to the Taylor rule process, property returns in the two emerging markets were found to have short run deviations which could not be explained by interest rates and output. For the purposes of building a fully fledged forecasting model, this model must be expanded to include other explanatory factors. Adding the risk premium as an explanatory variable could be the starting point.

Volatility plays an important role in option pricing and risk management. It is crucial that volatility is modelled as accurately as possible in order to forecast with confidence. The challenge is in the selection of the ‘best’ model with so many available models and selection criteria. The Model Confidence Set (MCS) solves this problem by choosing a group of models that are equally good. A set of GARCH models were estimated for several JSE indices and the MCS was used to trim the group of models to a subset of equally superior models. Using the Mean Squared Error to evaluate the relative performance of the MCS, GARCH (1,1) and Random Walk, it was found that the MCS, with an equally weighted combination of models, performed better than the GARCH (1,1) and Random Walk for instances where volatility in the returns data was high. For instances of low volatility in the returns, the GARCH (1,1) had superior 5-day forecasts but the MCS had better performance for 10-days and greater. The EGARCH (2,1) volatility model was selected by the MCS for 5 out of the 6 indices as the most superior model. The Random Walk was shown to have better long term forecasting performance.

In partial fulfillment of the Degree: Master of Commerce (Accountancy), University of the Witwatersrand, October 2017 / This research examines through the use of survey data which key factors around a companies‟ industry positioning, strategic decisions and internal qualitative capabilities, are considered by financial analysts when preparing their financial forecasts. The research covered buy-side and sell-side analysts in South Africa. The results were however found to be non-conclusive and did not align to previous research on this matter. Comparisons between analysts covering the same company were performed with consistencies found on average across all variables. It is interesting to note that when a detailed analysis and comparison was performed by individual variable for analysts covering the same company, different views on some of the variables were identified between buy-side and sell-side analysts, therefore supporting the research obtained during the literature review. It was found based on the tests performed that the factors which have an impact on forecasted financials relate to superior product/service strategy, innovation and ability to execute strategy. These variables were however noted not to be consistent across all the financial forecast factors and are contradictory to the research highlighted in the literature review as well as the outcomes of the original study, ie. There are additional factors which are considered important. Further research is recommended on analyst behaviour in South Africa. / GR2018

During the 1979 Atlantic hurricane season a tropical wave left the west coast of Africa and continued westward where satellite and ship observation indicated strengthening to a tropical depression from which Hurricane Frederic developed. This particular tropical disturbance has an interesting history. In its westward progression it intensified to hurricane strength lasting less than 24 hours followed by a weakening east of the Lesser Antilles. Continuing westward, this disturbance became extratropical over Southeast Cuba, but quickly reintensified to hurricane strength over Northwest Cuba, tracking through the warm Gulf waters and eventually making landfall near Mobile, Alabama. A number of experiments were carried out on a multi-level primitive equation (PE) model, a one-level PE model, and a higher resolution multi-level PE model (T63) in order to simulate the progression and intensification from a wave to a hurricane over a specified limited domain. Although previous experiments using this model with its comprehensive physical processes exhibit a reasonable predictive rate of success, the early experiments during this case study produced poor results. The most successful forecasts will be examined carefully and discussed entirely. There is strong indication that for mesoscale features a higher resolution model would achieve better results. / Typescript. / "Submitted to the Department of Meteorology in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: T. N. Krishnamurti, Professor Directing Thesis. / Includes bibliographical references (leaves 165-167).

by Choi Sau Yuk. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1982. / Bibliography: l. 68.

by Ho Kwong-shing Lawrence. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 43-44). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / ACKNOWLEDGEMENT --- p.iv / CHAPTER / Chapter I. --- INTRODUCTION AND LITERATURE REVIEW --- p.1 / Chapter II. --- COMBINING SALES FORECASTS --- p.7 / Chapter III. --- EXPERIMENTAL DESIGN --- p.14 / Chapter IV. --- SIMULATION RESULTS --- p.19 / Chapter V. --- SUMMARY AND CONCLUSION --- p.27 / APPENDIX --- p.31 / BIBLIOGRAPHY --- p.43

Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 122-126.

Yung Wun Na. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 60-62). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Statistical Modeling --- p.4 / Chapter 2.1 --- Capture Recapture Model --- p.4 / Chapter 2.1.1 --- Petersen Estimate --- p.5 / Chapter 2.1.2 --- Chapman Estimate --- p.8 / Chapter 2.2 --- The Bootstrap Method --- p.9 / Chapter 2.2.1 --- The Bootstrap Percentile Method --- p.10 / Chapter 2.3 --- The Double Bootstrap Method --- p.12 / Chapter 2.3.1 --- The Robbins-Monro Method --- p.12 / Chapter 2.3.2 --- Confidence Interval generated by the Robbins-Monro Method --- p.13 / Chapter 2.3.3 --- Three Different Approaches --- p.16 / Chapter 3 --- Empirical Study --- p.19 / Chapter 3.1 --- Introduction --- p.19 / Chapter 3.2 --- Double Bootstrap Method --- p.20 / Chapter 3.2.1 --- Petersen Estimate --- p.20 / Chapter 3.2.2 --- Chapman Estimate --- p.27 / Chapter 3.2.3 --- Comparison of Petersen and Chapman Estimates --- p.31 / Chapter 3.3 --- Conclusion --- p.33 / Chapter 4 --- Simulation Study --- p.35 / Chapter 4.1 --- Introduction --- p.35 / Chapter 4.2 --- Simulation Results of Double Bootstrap Method --- p.36 / Chapter 5 --- Conclusion and Discussion --- p.52 / References --- p.60

Orientador: Glauco de Souza Rolim / Resumo: Precipitação, em pequenas escalas de tempo, é um fenômeno associado a altos níveis de incerteza e variabilidade. Dada a sua natureza, técnicas tradicionais de previsão são dispendiosas e exigentes em termos computacionais. Este trabalho apresenta um modelo para prever a ocorrência de chuvas em curtos intervalos de tempo por Redes Neurais Artificiais (RNAs) em períodos acumulados de 3 a 7 dias para cada estação climática, mitigando a necessidade de predizer o seu volume. Com essa premissa pretende-se reduzir a variância, aumentar a tendência dos dados diminuindo a responsabilidade do algoritmo que atua como um filtro para modelos quantitativos, removendo ocorrências subsequentes de valores de zero(ausência) de precipitação, o que influencia e reduz seu desempenho. O modelo foi desenvolvido com séries temporais de 10 regiões agricolamente relevantes no Brasil, esses locais são os que apresentam as séries temporais mais longas disponíveis e são mais deficientes em previsões climáticas precisas, com 60 anos de temperatura média diária do ar e precipitação acumulada. foram utilizados para estimar a evapotranspiração potencial e o balanço hídrico; estas foram as variáveis ​​utilizadas como entrada para as RNAs. A precisão média para todos os períodos acumulados foi de 78% no verão, 71% no inverno 62% na primavera e 56% no outono, foi identificado que o efeito da continentalidade, o efeito da altitude e o volume da precipitação normal , tem um impacto direto na precisão das RNAs. Os... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a model to forecast the occurrence of rainfall in short ranges of time by Artificial Neural Networks(ANNs) in accumulated periods from 3 to 7 days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from 10 agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60 years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have a direct impact on the accuracy of the ANNs. The models have ... (Complete abstract click electronic access below) / Mestre

Wong Yiu Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 68-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- The Importance of Multiple Forecasting and Examples --- p.2 / Chapter 1.2 --- Previous Literature on Prediction Interval and Simultaneous Prediction Interval --- p.3 / Chapter 1.3 --- Objectives --- p.7 / Chapter 2 --- The Holt-Winters forecasting procedure --- p.8 / Chapter 2.1 --- Exponential Smoothing --- p.8 / Chapter 2.2 --- Holt-Winters Forecasting Procedure --- p.10 / Chapter 2.2.1 --- Additive Holt-Winters Model --- p.14 / Chapter 2.2.2 --- Multiplicative Holt-Winters Model --- p.16 / Chapter 2.3 --- Some Practical Issues --- p.18 / Chapter 2.3.1 --- Choosing Starting Values --- p.19 / Chapter 2.3.2 --- Choosing the Smoothing Parameters --- p.20 / Chapter 3 --- Constructing Simultaneous Prediction Intervals Method --- p.23 / Chapter 3.1 --- Bonferroni Procedure --- p.24 / Chapter 3.2 --- The 'Exact' Procedure --- p.25 / Chapter 3.3 --- Summary --- p.25 / Chapter 3.4 --- Covariance of forecast errors --- p.26 / Chapter 3.4.1 --- Yar and Chatfield's approach --- p.26 / Chapter 3.4.2 --- "Koehler, Snyder and Ord Approach" --- p.28 / Chapter 3.5 --- Simulation Study --- p.31 / Chapter 4 --- An Illustrative Example --- p.37 / Chapter 5 --- Simulation --- p.56 / Chapter 5.1 --- Conclusion --- p.62 / Appendix --- p.64 / References --- p.68