Disclaimer: If you sign up for a (paid) course using this link, R-exercises earns a commission. It does not impact what you pay for a course, and helps us to keep R-exercises free.
This practical course contains 43 lectures and 5 hours of content. It’s designed for advanced volatility trading analysis knowledge level and a basic understanding of R statistical software is useful but not required.
At first, you’ll learn how to download S&P 500®, CBOE S&P 500 volatility and options indexes® replicating data to perform historical volatility trading analysis by installing related packages and running script on RStudio IDE.
Then, you’ll do volatility analysis by estimating historical or realized volatility through Close to Close, Parkinson, Garman-Klass, Rogers-Satchell, Garman-Klass-Yang-Zhang and Yang-Zhang metrics. After that, you’ll use these estiomations to forecast volatility through Random Walk, Historical Mean, Simple Moving Average, Exponentially Weighted Moving Average, Autoregressive Integrated Moving Average and General Autoregressive Conditional Heteroscedasticity models. Next, you’ll measure market participants implied volatility through CBOE Volatility Index VIX®.
Later, you’ll estimate futures prices and compare them with actual historical data. Then, you’ll explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. After that, you’ll assess volatility futures risk through S&P 500 index SPX® realized or historical volatility monthly differences. Next, you’ll assess Capped Volatility Futures Premium trading strategy historical risk adjusted performance through CBOE Capped VIX Premium Strategy Index VPN®.
After that, you’ll estimate option call and put prices through Black and Scholes and Binomial Trees models together with related Options Greeks. Finally, you’ll evaluate three options trading strategies historical risk adjusted performance using CBOE Options and Volatility Indexes® and replicating Exchange Traded Funds ETFs and Exchange Traded Notes ETNs. First strategy you’ll evaluate is Buy Write through CBOE 30-Delta Buy Write Index BXMD® and related investment vehicle. Second strategy you’ll evaluate is Put Write only through CBOE Put Write Index PUT®. Third strategy you’ll evaluate is Volatility Tail Hedge through CBOE VIX Tail Hedge Index VXTH® and related investment vehicle.
What are the requirements?
- R statistical software is required. Downloading instructions included.
- Prior basic R statistical software knowledge is useful but not required.
What am I going to get from this course?
- Calculate forecasted volatility through Random Walk, Historical Mean, Simple, Exponentially Weighted, or Autoregressive Integrated Moving Averages and General Autoregressive Conditional Heteroscedasticity models
- Estimate historical or realized volatility through Close to Close, Parkinson, Garman-Klass, Rogers-Satchell, and Yang-Zhang metrics.
- Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using CBOE Options and Volatility Indexes® and replicating ETFs or ETNs.
- Approximate options call and put prices through Black and Scholes and Binomial Trees models together with related Options Greeks.
Who is the target audience?
- Sophisticated investors with experience in financial derivatives who desire to research volatility trading strategies.
- Students who want to learn about volatility trading analysis using R statistical software.