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

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.

ANN-Time Varying GARCH Model: Simulations and Application in Modelling Temperature for Weather Derivatives

“…Plots of the estimated parameters are as shown in Figure 2 below. From the simulation, it is evident that parameters estimation techniques as proposed in [5] are workable and gives consistent estimates.…”

“…The estimation of parameters for the model is done through non parametric techniques as discussed by [5].…”

“…Varying Garch model and the estimation procedure proposed in [5]. For computational simplicity, we used ANN-Tv GARCH (1, 1) model in our simulations.…”

“…and t Z is an i.i.d white noise, The parameters were estimated using the local polynomial techniques as proposed in [5]. Plots of the estimated parameters are as shown in Figure 2 below.…”

“…This study uses ANN-Time Varying GARCH (1, 1) model for processes with fixed and random periodicity proposed by [5] in modelling daily average temperature of Kenya from 1991 to 2016. The performance of ANN-Time Varying GARCH (1, 1) model is compared with that of four other models using MSE and R 2 .…”