AbstractsStatistics

This study investigates and examines the advantages and forecasting performance of combining the dynamic factor model (DFM) and artificial neural networks (ANNs) leading to new novel models that have capabilities to produce more accurate forecasts with application to the South African financial sector data. The overall aim of the study is to provide forecasting models that accommodate all relevant variables and the presence of any nonlinearity in the data to produce more adequate forecasts and serve as an alternative to traditional and current forecasting models, particularly in the presence of a changing and interacting environment. The thesis consists of four independent papers corresponding to four chapters. The first chapter brings together two important developments in forecasting literature; the artificial neural networks (ANNs) and factor models. The chapter introduces the Factor Augmented Artificial Neural Network (FAANN) hybrid model in order to produce a more accurate forecasting. The model is applied to forecasting three time series variables, namely, Deposit rate, Gold mining share prices and Long term interest rate. The out-of-sample root mean square error (RMSE) and Diebold-Mariano test results show that the FAANN model yields substantial improvements over the autoregressive AR benchmark model and standard dynamic factor model (DFM). The superiority of the FAANN model is due to the ANNs flexibility to account for potentially complex nonlinear relationships that are not easily captured by linear models. In the second chapter we introduce a new model that exploits the artificial neural networks model as a data smoother to alleviate the effect of major financial crisis and nonlinearity due to high fluctuations such as those associated with the 2008 crisis. The chapter introduces the ANN-DF model, where in the first stage the best fitted ANNs for each single series of the data set which contains 228 monthly series is used to obtain the in-sample forecasts of each series. In the second stage, the factor model is used to extract the factors from the smoothed data set, and then these factors are used as explanatory variables in forecasting. The model is applied to forecast three South Africa variables, namely, Rate on 3-month trade financing, Lending rate and Short term interest rate in the period 1992:01 to 2011:12. The results, based on the root mean square errors of three, six and twelve months ahead out-of-sample forecasts over the period 2007:01 to 2011:12 indicate that, in all of the cases, the ANN-DFM and the DFM statistically outperform the autoregressive (AR) models. In the majority of the cases the ANN-DFM outperforms the DFM. The results indicate the usefulness of smoothing and factor extraction in forecasting performance. The forecast results are confirmed by the test of the equality of forecast accuracy proposed by Diebold-Mariano (1995). The third chapter evaluates the role of the DFM model (liner in nature) and the ANN model (with capacity to handle nonlinearity) as competing forecasting…