Selection of Heteroscedastic Models: A Time Series Forecasting Approach

Abstract

To overcome the weaknesses of in-sample model selection, this study adopted out-of-sample model selection approach for selecting models with improved forecasting accuracies and performances. Daily closing share prices were obtained from Diamond Bank and Fidelity Bank as listed in the Nigerian Stock Exchange spanning from January 3, 2006 to December 30, 2016. Thus, a total of 2713 observations were explored and were divided into two portions. The first which ranged from January 3, 2006 to November 24, 2016, comprising 2690 observations, was used for model formulation. The second portion which ranged from November 25, 2016 to December 30, 2016, consisting of 23 observations, was used for out-of-sample forecasting performance evaluation. Combined linear (ARIMA) and Nonlinear (GARCH-type) models were applied on the returns series with respect to normal and student-t distributions. The findings revealed that ARIMA (2,1,1)-EGARCH (1,1)-norm and ARIMA (1,1,0)-EGARCH (1,1)-norm models selected based on minimum predictive errors throughout-of-sample approach outperformed ARIMA (2,1,1)-GARCH (2,0)-std and ARIMA (1,1,0)-EGARCH (1,1)-std model chosen through in-sample approach. Therefore, it could be deduced that out-of-sample model selection approach was suitable for selecting models with improved forecasting accuracies and performances.

The improvement of the intelligent decision support system for forecasting non-linear non-stationary processes

“…The growing uncertainty of external environment increases the requirements for the quality, reasonability and efficiency of managerial decision-making [1][2][3]. Most often, the weakly structured problems of research, changes in the behaviour and structure of socio-economic systems under the influence of predictable and non-predictable external factors, the most complete use of available potential and ensuring support for making optimal managerial decisions regarding development of systems under study need to be solved today [4][5][6][7].…”

Modeling Heteroscedasticity in the Presence of Serial Correlations in Discrete-time Stochastic Series: A GARCH-in-Mean Approach

“…Lastly, neglecting heteroscedasticity can lead to spurious non-linearity in the conditional mean and difficulty in computing the confidence interval for forecasts (see [2,3,4,5]). Furthermore, details of heteroscedasticity modeling are documented in [6,7,8,9,10,11,12,13].…”