How to Forecast Volatility Using GARCH (1,1)
This video discusses how to use GARCH(1,1) to forecast future volatility. The key parameter is persistence (alpha + beta): high persistence implies slow decay toward the long run average.
GARCH models were developed by Robert Engle to deal with the problem of auto-correlated residuals (which occurs when you have volatility clustering for example) in time-series regression. One reason why the ARCH family of models is popular is that you only need price data to generate the model. ARCH is great for looking at volatility over very long periods.
This video is developed by David from Bionic Turtle.
- 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
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