TITLE:
ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity
AUTHORS:
Elias K. Karuiru, John Mwaniki Kihoro, Thomas Mageto, Anthony Gichuhi Waititu
KEYWORDS:
Fixed Periodicity, Random Periodicity, Artificial Neural Network, Time Varying GARCH
JOURNAL NAME:
Open Journal of Statistics,
Vol.11 No.5,
October
8,
2021
ABSTRACT: 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.