Volatility Trading Analysis with Python

Full Course Content Last Update 11/2018

Learn volatility trading analysis through a practical course with Python programming language using CBOE® and S&P 500® volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced sophisticated investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become a Volatility Trading Analysis Expert in this Practical Course with Python

• Read or download CBOE® and S&P 500® volatility strategies benchmark indexes and replicating funds data to perform historical volatility trading analysis by installing related packages and running code on Python IDE.

• Estimate historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, and Garman-Klass-Yang-Zhang metrics.

• Calculate forecasted volatility through seasonal random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models.

• Measure market participants implied volatility through related volatility index.

• Estimate futures prices and explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. 

• Assess volatility hedge futures trading strategy historical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN. 

• Approximate options call and put prices through Black and Scholes model together with related option Greeks. 

• Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs.

Become a Volatility Trading Analysis Expert and Put Your Knowledge in Practice

Learning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors’ volatility trading strategies research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using CBOE® and S&P 500® volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 44 lectures and 6 hours of content. It’s designed for advanced volatility trading analysis knowledge level and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read or download CBOE® and S&P 500® volatility strategies benchmark indexes and replicating ETFs or ETNs data to perform historical volatility trading analysis by installing related packages and running code on Python IDE. 

Then, you’ll do volatility analysis by estimating historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell and Garman-Klass-Yang-Zhang metrics. After that, you’ll use these estimations to forecast volatility through seasonal 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 related volatility index.

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 risk through historical implied volatility index daily returns probability distribution non-normality. Next, you’ll evaluate volatility hedge futures trading strategy historical risk adjusted performance using related hedged equity volatility futures strategy benchmark index replicating ETF or ETN.

After that, you’ll estimate option call and put prices through Black and Scholes model together with related option Greeks. Next, you’ll assess asset returns risk through historical stocks index daily returns probability distribution non-normality. Finally, you’ll evaluate covered call or buy write, cash secured short put or put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and hedged equity volatility options strategy benchmark indexes and replicating ETFs.