GARCH Model IBM Stock Forecasting of Price Volatility

Abstract

Risk and volatility are two related factors in research regarding capital markets. Many factors influence the movement of shares and indices. Volatility is common and affects risk assessment. Stock price volatility is an important aspect of understanding market behavior, with high volatility reflecting rapid and unstable price fluctuations. This research investigates the GARCH model in assessing volatility on the IBM Stock Exchange. The method employed was the symmetric GARCH model. It focuses on univariate analysis using the GARCH econometric model. The GARCH model allows modeling stock price variance over time based on the assumption that the variance was influenced by past stock price variance. The stages of this research were (1) data collection, (2) data pre-processing, and (3) forecasting model implementation. The best model obtained was ARMA(4,2)-GARCH(5,6) with an AIC value of 4.1017. A lower AIC value indicates that the model explains the data better or more optimally. A diagnostic test found that the model was adequate because the residual distribution followed a straight line, which means it was normally distributed.

Keywords

• arma
• garch
• ibm
• stock