TITLE:
ANN-Time Varying GARCH Model: Simulations and Application in Modelling Temperature for Weather Derivatives
AUTHORS:
Elias K. Karuiru, John Mwaniki Kihoro, Thomas Mageto, Anthony Gichuhi Waititu
KEYWORDS:
Artificial Neural Network, Time Varying GARCH, Weather Derivatives, Temperature
JOURNAL NAME:
Open Journal of Statistics,
Vol.12 No.3,
June
30,
2022
ABSTRACT: In economics and finance, minimising errors while building an abstract
representation 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 methods
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 application in modelling
weather derivatives. The study models daily temperature of Kenya using ANN-Time
Varying GARCH (1, 1), Time Lagged Feedforward neural network (TLNN) and periodic GARCH
family models. Mean square error (MSE) and coefficient of determination R2 were used to determine
performance of the models under study. Results obtained show that the ANN-Time
Varying GARCH model gives the best results.