Abstract:
Financial Time Series Forecasting is an important tool to support both indi-
vidual 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 characte-
rized 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.
