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This paper delves into the intricate process of refining GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model parameters for precise financial volatility forecasting. Leveraging data from yfinance, traditional approaches using autocorrelation function (ACF) and partial autocorrelation function (PACF) plots were employed. Optimal values derived from visual diagnostics did not yield significant parameters. So we proceeded to set both autoregressive order (p) and moving average order (q) to 1 produced the most favorable AIC and BIC metrics. Furthermore, the model, refined through this process, was seamlessly transitioned into a user-friendly web application for enhanced accessibility and practical implementation by financial analysts.
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