Introduction

This analysis delves into the volatility of Google’s stock (GOOGL) over the past two years. Utilizing the quantmod package to retrieve historical adjusted closing prices and the rugarch package for advanced time series modeling, we aim to understand and forecast the stock’s volatility. The process involves calculating daily returns, fitting a GARCH(1,1) model to capture the conditional heteroscedasticity inherent in financial time series data, and subsequently generating a 30-day forecast of the volatility. Visualizations of the adjusted closing prices, daily returns, and the GARCH model’s unconditional volatility and time series predictions are included to provide a comprehensive overview of the analysis.

## [1] "GOOGL"