Modeling and forecasting volatility in the Iraq stock exchange: A survey study using ARCH and GARCH models
Author(s): Ahmed Ali Salman and Ansseif A Latif Ansseif
Abstract: This study aimed at the effectiveness of self-regression models conditional on the instability of variance ARCH in predicting the returns of shares traded for the Iraq Stock Exchange, in addition to the possibility of proposing a model in providing predictions with relatively small errors during the period from 5/1/2021 to 18/10/2024 by daily observations over the studied period, and to achieve the objectives of the study, the daily closing price was calculated as an indicator to predict fluctuations and estimate self-regression conditional on the instability of variance based on self-regression models conditional on heterogeneity variance. ARCH:"Auto Regressive Conditional Heteroscedasticity" and GARCH: "Generalized Auto Regressive Conditional Heteroscedasticity" are intended to model variance in data that have different fluctuations (different variability) in time series periods, i.e. have high variance sometimes and low variance across different time periods of the time series. For financial analysts, these patterns promise a period of wild and calm.
Ahmed Ali Salman, Ansseif A Latif Ansseif. Modeling and forecasting volatility in the Iraq stock exchange: A survey study using ARCH and GARCH models. Int J Stat Appl Math 2025;10(2):35-45. DOI: 10.22271/maths.2025.v10.i2a.1978
