- How to Calculate Historical Volatility
- Approaches to Estimating Volatility
- Using Excel's Goal Seek Function to Estimate Implied Volatility
- Volatility: Moving Average Approaches
- Volatility: Exponentially Weighted Moving Average (EWMA)
- Using GARCH (1,1) Approach to Estimate Volatility
- How to Forecast Volatility Using GARCH (1,1)
- Calculate Historical Volatility Using EWMA

# Using GARCH (1,1) Approach to Estimate Volatility

This video provides an introduction to the GARCH approach to estimating volatility, i.e., Generalized AutoRegressive Conditional Heteroskedasticity.

GARCH is a preferred method for finance professionals as it provides a more real-life estimate while predicting parameters such as volatility, prices and returns.

GARCH(1,1) estimates volatility in a similar way to EWMA (i.e., by conditioning on new information) except that it adds a term for mean reversion. It says the series is "sticky" or somewhat persistent to a long-run average.

*This video is developed by David from Bionic Turtle.*

### You may find these interesting

## Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.