ARCH/GARCH Models - EViews Material

This tutorial provides a comprehensive guide to estimating ARCH (AutoRegressive Conditional Heteroskedasticity) models using EViews. The tutorial begins by introducing ARCH models, which are designed to address changing volatility over time in financial data. It covers essential concepts such as volatility clustering and model assumptions.

The tutorial outlines the step-by-step process of estimating an ARCH model:

1. Checking for stationarity of the time series data.
2. Identifying an appropriate ARIMA model for the mean equation.
3. Conducting a heteroskedasticity test to detect ARCH effects.
4. Determining the order of ARCH components by analyzing correlograms and partial autocorrelation functions.
5. Estimating the ARCH model with appropriate lag orders.
6. Performing model diagnostics to ensure the absence of heteroskedasticity and autocorrelation in residuals.

Throughout the tutorial, emphasis is placed on maintaining model parsimony and interpreting results effectively.