Video Lecture for GARCH
1 min read
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Get full access to all Data Science, Machine Learning, and AI courses built for finance professionals.
One-time payment - Lifetime access
Or create a free account to start
A step-by-step guide covering Python, SQL, analytics, and finance applications.
Or create a free account to access more
GARCH, Generallized AutoRegressive Conditional Heteroskedasticity, is one of the popular methods of estimating volatility in finance.
GARCH estimates volatility similar to EWMA, however, it adds more information to the series related to mean reversion. Also some people use both EWMA and GARCH, EWMA has been widely superceded by GARCH.
There are three main steps in the GARCH process:
1. Estimate the best-fitting autoregressive model 2. Calculate autocorrelations of the error term 3. Test for significance
The following video demonstrates how GARCH(1,1) can be used to forecast volatility.