This project focuses on forecasting stock price volatility using various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models in R. By leveraging time series analysis techniques and volatility forecasting tools, we aim to provide accurate and insightful predictions of stock market volatility.
- Data collection and preprocessing: Obtain historical stock price data for the target stocks, clean the data, and ensure consistency.
- Exploratory data analysis: Visualize the stock price trends, identify patterns, and analyze statistical characteristics.
- GARCH model selection: Experiment with different GARCH variants such as GARCH(1,1), GARCH-M, EGARCH, etc., to determine the best-fitting model.
- Model estimation and evaluation: Estimate the chosen GARCH models, evaluate their performance, and compare their forecasting accuracy.
- Volatility forecasting: Generate future volatility forecasts using the selected GARCH model, providing insights for risk management and trading strategies.
- Visualization and interpretation: Present the results using informative plots and graphs to aid in understanding the volatility dynamics of the target stocks.
- Install the required packages specified in the project's dependencies section.
- Fetch the historical stock price data or use the provided dataset.
- Preprocess the data, ensuring data quality and consistency.
- Run the analysis scripts, including GARCH model estimation, evaluation, and volatility forecasting.
- Interpret and visualize the results using appropriate plots and graphs.
- Customize and experiment with different GARCH model variants or additional time series techniques.
- Document your findings, insights, and conclusions based on the analysis.
- Feel free to contribute improvements, bug fixes, or additional analysis methods to enhance the project.
- R version 3.x or higher
- Required R packages:
quantmod,rugarch,forecast,ggplot2, and any additional packages specified in the project's scripts.
This project is licensed under the MIT License.