Lessons
- 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
Volatility: Moving Average Approaches
Within stochastic volatility, moving average is the simplest approach. It simply calculates volatility as the unweighted standard deviation of a window of X trading days. This video demonstrates three "flavors:" population variance (volatility = SQRT[variance]), sample, and simple.
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 $29 (Regular $57)JOIN 30,000 DATA PROFESSIONALS
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.
