Improve the forecasting accuracy of a GARCH model using a decomposition method

Abstract: In recent years, there has been a greater emphasis on the forecasting accuracy of heteroscedastic models. Instead of estimating the returns volatility using a generalised autoregressive conditional heteroscedastic model ( model), this study separates the returns internal components from the external trend first using a decomposition method called “external trend and internal components analysis method” (ETICA), then estimates the returns volatility using a . The study's goal is to determine whether this separation has an effect on the prediction accuracy of the volatility of S&P 500, NASDAQ and Dow Jones stock indices. To explore the ETICA method effect, the root mean squared error has been used to compare the prediction accuracy before and after decomposition. The findings show that on average, the RMSE results were found to be lower before decomposition which means that stock returns had a higher prediction accuracy.

Keywords: GARCH model, decomposition method, S&P 500, NASDAQ, Dow Jones, RMSE.

Title: Improve the forecasting accuracy of a GARCH model using a decomposition method

Author: Dioubi Fatene, Loudahi Lamia

International Journal of Mathematics and Physical Sciences Research  

ISSN 2348-5736 (Online)

Vol. 10, Issue 2, October 2022 - March 2023

Page No: 57-74

Research Publish Journals

Website: www.researchpublish.com

Published Date: 17-February-2023

DOI: https://doi.org/10.5281/zenodo.7649004

Paper Download Link (Source)

https://www.researchpublish.com/papers/improve-the-forecasting-accuracy-of-a-garch-model-using-a-decomposition-method