- 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
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.
Data Science in Finance: 9-Book Bundle
Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.
What's Included:
- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python
Each book includes PDFs, explanations, instructions, data files, and R code for all examples.
Get the Bundle for $39 (Regular $57)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.
