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ANN-Time Varying GARCH Model for Processes with Fixed and Random Periodicity

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.