Volatility prediction for the GARCH model

Abstract

In this paper, the focus is on analyzing the returns of financial assets using the GARCH(1,1) model and various distributions: stable, Student’s t-distribution, and skewed Student’s t-distribution. The work includes a theoretical analysis of the model, as well as practical application to the return data of Apple Inc, Gazprom PJSC, Severstal PJSC, Microsoft, and Nike. The results show that stable distribution models provide more accurate volatility forecasts in conditions of high uncertainty. The choice of model and distribution proves to be critical for the precision of financial analysis, emphasizing the need to use more complex distributions to forecast volatility in financial markets