Author(s)

Elias K. Karuiru¹, John Mwaniki Kihoro², Thomas Mageto³, Anthony Gichuhi Waititu³

¹The Pan African University Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi, Kenya.
²School of Computing and Mathematics, Co-Operative University, Nairobi, Kenya.
³Department of Statistics and Actuarial Sciences, JKUAT, Nairobi, Kenya.

Financial Time Series Forecasting is an important tool to support both individual and organizational decisions. Periodic phenomena are very popular in econometrics. Many models have been built aiding capture of these periodic trends as a way of enhancing forecasting of future events as well as guiding business and social activities. The nature of real-world systems is characterized by many uncertain fluctuations which makes prediction difficult. In situations when randomness is mixed with periodicity, prediction is even much harder. We therefore constructed an ANN Time Varying Garch model with both linear and non-linear attributes and specific for processes with fixed and random periodicity. To eliminate the need for time series linear component filtering, we incorporated the use of Artificial Neural Networks (ANN) and constructed Time Varying GARCH model on its disturbances. We developed the estimation procedure of the ANN time varying GARCH model parameters using non parametric techniques.

Fixed Periodicity, Random Periodicity, Artificial Neural Network, Time Varying GARCH

Karuiru, E. , Kihoro, J. , Mageto, T. and Waititu, A. (2021) ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity. Open Journal of Statistics, 11, 673-689. doi: 10.4236/ojs.2021.115040.