With few previous data and literature based on the South African banking sector, the key aim of this study was to contribute further results concerning the effect of operational loss events on the reputation of South African banks. The main distinction between this study and previous empirical research is that a small sample of South African banks listed on the JSE, between 2000 and 2014 was used. Insurance companies fell outside the scope of the study. The study primarily focused on identifying reputational risk among Regal Treasury Bank, Saambou Bank, African Bank and Standard Bank. The events announced by these banks occurred between 2000 and 2014. The precise date of the announcement of the operational events was also determined. Stock price data were collected for those banks that had unanticipated operational loss announcements (i.e. the event). Microsoft Excel models applied to the reputational loss as the difference between the operational loss announcement and the loss in the stock returns of the selected banks. The results indicated significant negative abnormal returns on the announcement day for three of the four banks. For one of the banks it was assumed that the operational loss was not significant enough to cause reputational risk. The event methodology similar to previous literature, furthermore examined the behaviour of return volatility after specific operational loss events using the sample of banks. The study further aimed at making two contributions. Firstly, to analyse return volatility after operational loss announcements had been made among South African banks, and secondly, to compare the sample of affected banks with un-affected banks to further identify whether these events spilled over into the banking industry and the market. The volatility of these four banks were compared to three un-affected South African banks. The results found that the operational loss events for Regal Treasury Bank and Saambou Bank had no influence on the unaffected banks. However the operational loss events for African Bank and Standard Bank influenced the sample of unaffected banks and the Bank Index, indicating systemic risk.

This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.

Researchers recommended investing according to the long only momentum (MOM) strategy to generate excess returns for private investors. The general problem of this study was that it was unclear when to enter and when to exit declining financial markets to avoid larger losses and to improve the overall performance with the MOM strategy. Therefore, it was important to understand the influence of a timing indicator on the MOM strategy. The purpose of this study was to examine the relationship between different moving average (MA) settings, the MOM strategy, and the performance of the returns from the construction of small U.S. stock portfolios. The research question was what MA setting as a strategy adjustment could improve the MOM strategy performance for small portfolios of U.S. stocks. A quasi-experimental research design was chosen to answer this research question. For the methods and analysis, simple- and exponential- MA, 2 econometric models, and abnormal Sharpe ratios were computed on the sample basis of 30 Dow Jones Industrial Average (DJIA) stocks. The computations allowed me to determine the optimal trading frequencies for the MA MOM strategy. The key result was that the MA MOM strategy could improve the MOM strategy on average by 0.16% per month. The optimal trading frequency for the MA MOM strategy with $5,000 was tri yearly through which (0.90 - 1.85 %) net monthly return could be achieved. The MOM strategy can be adjusted by a simple moving average (SMA) indicator on a 6 versus 36-month basis as a recommendation. This study might contribute to positive social change by adjusting the MOM strategy, which specifically impacts private investors in declining stock markets to improve the overall performance when trading the MA MOM strategy.

In environmental studies, measurements of interest are often taken on multiple variables. The results of spatial data analyses can be substantially affected by the spatial configuration of the sites where measurements are taken. Hence, optimal designs which result in data guaranteeing efficient statistical inferences need to be studied. We study optimal designs on two large classes of spatial regions with respect to three design criteria, which were prediction, covariance parameter estimation, and empirical prediction. The first class of regions includes those in the plane, where Euclidean distance is used. The performance of the optimal designs is compared to that of randomly chosen designs. Optimal designs for a small example and a relatively large example are obtained. For the small example, complete enumeration of all possible designs is computationally feasible. For the large example, the computational difficulty in searching for the optimal spatial sampling design is overcome by a simulated annealing algorithm. The second class of spatial regions includes streams and rivers, where the distance is defined as distance along the stream network. A moving average construction is used to establish valid covariance and cross-covariance models using stream distance. Optimal designs for small and large examples are obtained. An application of our methodology to a real stream network is included. We discuss the impact of asymmetry in the cross covariance function on the spatial multivariate design. The relationship between multivariate optimal design and univariate optimal design if the multivariate design is restricted to be completely collocated is studied. The efficiency lost if we consider the design that is optimal within the class of collocated designs is discussed.

Since the introduction of CUSUM statistic by E.S. Page (1951), detection of change or a structural break in time series has gained significant interest as its applications span across various disciplines including economics, industrial applications, and environmental data sets. However, many of the early suggested statistics, such as CUSUM or MOSUM, lose their effectiveness when applied to time series data. Either the size or power of the test statistic gets distorted, especially for higher order autoregressive moving average processes. We use the test statistic from Gombay and Serban (2009) for detecting change in the mean of an autoregressive process and show how the application of sieve bootstrap to the time series data can improve the performance of our test to detect change. The effectiveness of the proposed method is illustrated by applying it to economic data sets.

This study investigates the trends and varieties of concentration of ozone in recent years in Taiwan in order to understand the situation of air qualities in different areas. Ozone is the secondary pollutant produced by nitrogen oxides, reactive hydrocarbons and sunlight. Because ozone has strong oxidizing power, it is easy to stimulate the respiratory system, which may cause cough, asthma, headache, tiredness and harmful to lung; and it is also harmful to plants and even synthetic materials. Here, we tried to study the trends and varieties of the time effect to the ozone level in each region and compare the similarities and heterogeneity of the models in different regions by the ozone data obtained from all air quality monitoring stations of environmental protection administration. Analysis of building appropriate temporal and spatial models are performed and factor analysis on the model residuals are used to investigate the possible latent variables to interpret the patterns of the ozone values in different regions. These may help to set up strategies for ozone control in the future.

¡@¡@The impacts of global warming and climate change were the important issue in the last few decades. The sea level rising was one of most discussion topics of physical impact which derived from global warming. In this study, about forty year¡¦s sea level records (from Harbor & Marine Technology Center) were used to analysis the long term water level trends at Keelung Harbor, Taichung Harbor and Kaohsiung Harbor in the western Taiwan. ¡@¡@We rearranged the records format, filtered out error data, and then sorted the data by the logged time. Three kinds of analysis method were applied to investigate the trend of water level change. The first one was harmonic analysis which was used to eliminate the effects of astronomical tide. The second method was spectrum analysis and it assisted to obtain the amplitudes and phases of tidal component. Finally, we used the moving average method to smooth data and discussed the tendency of water level change. ¡@¡@The result showed no evidence of sea level rising along the western coast of Taiwan. The average water level raised and declined evenly in the duration of forty years, and the general trends of water level varied near the zero level. The result also showed the variation of water level that affected by the measurement instruments was more over the physical effects.

The study uses Moving Average, On Balance Volume, and KD (Stochastic Oscillator) to analyze that the technical analysis in which the bull or bear stock markets is efficiency. Also, verifies the changes of market efficiency before and after the financial crisis and whether it can earn excess returns or not by using technical analysis. That is, the returns earned by using technical analysis significantly greater than buy and hold which means the efficiency of technical analysis. Nevertheless, the study also aims to realize that whether the returns of the portfolio of technical indicators better than unit indicator. The companies in our samples are selected by the size of market value top 30 companies in the industries of electronic and finance in order to avoid the effect of market micro structure. Our results are as follows: (1) The returns in bear market are significantly higher than bull market by using MA6-144. (2) The MA6-72 and MA6-144 of financial stock before financial crisis, the returns of technical analysis are significantly better than buy and hold. In the other hand, in the electronic stock, we can use MA6-22-250, KD, and OBV to beat the buy and hold strategy and verify that the market efficiency does not exist. (3) The returns which combine of KD and OBV indicators are significantly higher than KD.

In some production environments the defect rates are considerably low such that measurement of fraction of nonconforming items reaches parts per million level. In such environments, monitoring the number of conforming items between consecutive nonconforming items, namely the time between events (TBE) is often suggested. However, in the design of control charts for TBE monitoring a common practice is the assumptions of known process parameters. Nevertheless, in many applications the true values of the process parameters are not known. Their estimates should be determined from a sample obtained from the process at a time when it is expected to operate in a state of statistical control. Additional variability introduced through sampling may significantly effect the performance of a control chart. In this study, the effect of parameter estimation on the performance of Time Between Events Exponentially Weighted Moving Average (TBE EWMA) schemes is examined. Conditional performance is evaluated to show the effect of estimation. Marginal performance is analyzed in order to make recommendations on sample size requirements. Markov chain approach is used for evaluating the results.

In the last few years, it has been proved that the movements of financial asset have the property of non-linearity and show some tendency within a given period. Increasing evidence that technical trading rules can detect non-linearity in financial time series has renewed interest in technical analysis. This study evaluates the market timing ability of the moving average trading rules in twelve equity markets in the developed markets and the emerging markets from January 1990 through Match 2002. We use traditional test, bootstrap p-value test, Cumby-Modest¡¦s market timing ability test and simulation stock trade to evaluate market timing ability. The overall results indicate that the moving average trading rules have predictive ability with respect to market indices in the Asia emerging stock markets. These findings may provide investors with important asset allocation information.