Abstract:
In economics and finance, minimising errors while building an abstract re-
presentation of financial assets plays a critical role due to its application in
areas such as risk management, decision making and option pricing. Despite
the many methods developed to handle this problem, modelling processes
with fixed and random periodicity still remains a major challenge. Such me-
thods include Artificial Neural networks (ANN), Fuzzy Inference system
(FIS), GARCH models and their hybrids. This study seeks to extend literature
of hybrid ANN-Time Varying GARCH model through simulations and ap-
plication in modelling weather derivatives. The study models daily tempera-
ture of Kenya using ANN-Time Varying GARCH (1, 1), Time Lagged Feed-
forward neural network (TLNN) and periodic GARCH family models. Mean
square error (MSE) and coefficient of determination
R 2 were used to deter-
mine performance of the models under study. Results obtained show that the
ANN-Time Varying GARCH model gives the best results.
