Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is particularly useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the prediction variable to other explanatory variables. Time series can be modeled in a variety of ways e.g. using exponential smoothing techniques, regression models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will, along the lines of an existing study in the literature, investigate the use of a hybrid approach to time series forecasting using both linear and neural network models. The proposed methodology consists of two basic steps. In the first step, a linear model is used to analyze the linear part of the problem and in the second step a neural network model is developed to model the residuals from the linear model. The results from the neural network can then be used to predict the error terms for the linear model. This means that the combined forecast of the time series will depend on both models. Following an overview of the models, empirical tests on real world data will be performed to determine the forecasting performance of such a hybrid model. Results have indicated that depending on the forecasting period, it might be worthwhile to consider the use of a hybrid model. / Thesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010.

Estimation of the probability of occurrence of future flood events at one or more locations across a river system is frequently required for the design of bridges, culverts, spillways, dams and other engineering works. This study investigates some of the statistical aspects for estimating the flood frequency distribution at a single site and on regional basis. It is demonstrated that generalized logistic (GL) distribution has many properties well suited for the modelling of flood frequency data. The GL distribution performs better than the other commonly recommended flood frequency distributions in terms of several key properties. Specifically, it is capable of reproducing almost the same degree of skewness typically present in observed flood data. It appears to be more robust to the presence of extreme outliers in the upper tail of the distribution. It has a relatively simpler mathematical form. Thus all the well known methods of parameter estimation can be easily implemented. It is shown that the method of probability weighted moments (PWM) using the conventionally recommended plotting position substantially effects the estimation of the shape parameter of the generalized extreme value (GEV) distribution by relocating the annual maximum flood series. A location invariant plotting position is introduced to use in estimating, by the method of PWM, the parameters of the GEV and the GL distributions. Tests based on empirical distribution function (EDF) statistics are proposed to assess the goodness of fit of the flood frequency distributions. A modified EDF test is derived that gives greater emphasis to the upper tail of a distribution which is more important for flood frequency prediction. Significance points are derived for the GEV and GL distributions when the parameters are to be estimated from the sample data by the method of PWMs. The critical points are considerably smaller than for the case where the parameters of a distribution are assumed to be specified. Approximate formulae over the whole range of the distribution for these tests are also developed which can be used for regional assessment of GEV and GL models based on all the annual maximum series simultaneously in a hydrological region. In order to pool at-site flood data across a region into a single series for regional analysis, the effect of standardization by at-site mean on the estimation of the regional shape parameter of the GEV distribution is examined. Our simulation study based on various synthetic regions reveals that the standardization by the at-site mean underestimates the shape parameter of the GEV by about 30% of its true value and also contributes to the separation of skewness of observed and simulated floods. A two parameter standardization by the at-site estimates of location and scale parameters is proposed. It does not distort the shape of the flood frequency data in the pooling process. Therefore, it offers significantly improved estimate of the shape parameter, allows pooling data with heterogeneous coefficients of variation and helps to explain the separation of skewness effect. Regions on the basis of flood statistics L-CV and USKEW are derived for Scotland and North England. Only about 50% of the basins could be correctly identified as belonging to these regions by a set of seven catchment characteristics. The alternative approach of grouping basins solely on the basis of physical properties is preferable. Six physically homogeneous groups of basins are identified by WARD's multivariate clustering algorithm using the same seven characteristics. These regions have hydrological homogeneity in addition to their physical homogeneity. Dimensionless regional flood frequency curves are produced by fitting GEV and GL distributions for each region. The GEV regional growth curves imply a larger return period for a given magnitude flood. When floods are described by GL model the respective return periods are considerably smaller.

This dissertation contains four papers in the field of financial econometrics. In the first paper, A Smooth Transition ARCH Model for Asset Returns, a new class of ARCH models is introduced. The model class allows for non-linearity in the equation for the conditional variance. Two forms of non-linearity are considered: (i) asymmetry regarding the sign of residuals, and (ii) non-linearity regarding the size of residuals. Furthermore, specification tests for the models are presented. The second paper, Specification Tests for Asymmetric GARCH, presents two new Lagrange multiplier test statistics designed for testing the null of GARCH(1,1), against the alternative of asymmetric GARCH. Small sample properties for the statistics are presented and the power of both tests is shown to be superior to that of previously proposed tests. This is true for a large group of asymmetric GARCH models, providing that the proposed tests can detect general GARCH asymmetry. The third paper, Modeling Nordic Stock Returns with Asymmetric GARCH models, investigates the presence of asymmetric GARCH effects in a number of equity return series, and compares the modeling performance of seven different asymmetric GARCH models. The data consists of daily returns for 45 Nordic stocks, for the period July 1991 to July 1996. The paper also introduces three new procedures for asymmetry testing. The proposed LM tests allow for heterokurtosis under the null. The final paper, Discrete Time Hedging of OTC Options in a GARCH Environment: A Simulation Experiment, examines the effect of using the Black and Scholes formula for pricing and hedging options in a discrete time heteroskedastic environment using a simulation procedure. It is shown that the variance of the returns on the hedged position is considerably higher in a GARCH(1,1) environment than in a homoskedastic environment. The variance of returns is heavily dependent on the level of kurtosis in the returns process and on the first-order autocorrelation in centered and squared returns.Each paper is self-contained and can be read in an order chosen by the reader.In an introductory chapter, the reader is given a general summary of the ARCH literature and will gain a clear understanding of how the four essays relate to previous work in the econometrics and finance literature, and to practical considerations of econometric modeling. / Diss. Stockholm : Handelshögsk.

Thesis (Ph. D.)--University of Adelaide, 1992. / "February 1992." Includes bibliographical references (leaf 323-344). Also available electronically.

Thesis (Ph. D.)--Ohio State University, 2007.

Thesis (M.Sc.) - Carleton University, 2005. / Includes bibliographical references (p. 77-80). Also available in electronic format on the Internet.

Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (leaves 91-92). Also available via the World Wide Web.

Thesis (M.S.)--University of North Carolina at Wilmington, 2006. / Includes bibliographical references (leaves [39]-40)

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 122-146).

Thesis--University of Oklahoma. / Bibliography: leaves 62-63.