Volatility Trading Analysis with Python

Requirements

• Python programming language needed. Instructions for downloading are included.
• Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions are provided.
• Practical examples of information and Python code files included with the course.
• A basic Python programming language experience is helpful but not necessary.

Description

Full Course Content Last Update 11/2018

Learn how to analyze volatility trading through an interactive course in the Python programming language. It uses CBOE(r) and S&P 500(r) risk strategies, benchmark indexes, and replication of ETFs and ETNs historical data to test the risk-adjusted performance of testing back-testing. It teaches the most fundamental concepts starting from the advanced, which will help you earn higher grades, advance your academic skills, apply your knowledge in your work environment or research as a skilled and sophisticated investor. This is all while examining the knowledge of Nobel Prize winners and best practitioners in the discipline.

Learn to become a Volatility Trading Analyst in this course that is practical and uses Python

• Download or read CBOE(r) or S&P 500(r) the benchmarks for volatility strategies and replicating funds data for performing an analysis of historical volatility in trading through the installation of related packages and running the 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 predicted volatility using the annual random walks historical mean simple moving average exponentially weighted moving mean and an integrated autoregressive moving average and general autoregressive conditional heteroscedasticity models.
• Determine the implied volatility of market participants by using the related volatility index.
• Find out the futures price and investigate the correlation between asset returns and volatility and volatility risk premium structure of the volatility term and Skewed patterns.
• Examine the volatility of hedged futures trading strategy’s performance with risk-adjusted results using the hedged equity volatility strategies for futures. Benchmark index that replicates ETF as well as ETN.
• Approximate price of options calls and put via Black and Scholes model, as well as the related options Greeks.
• Analyze the performance of buy-write put write, volatility options strategies that hedge tails. With the help of related buy-write, past performance in risk-adjusted terms was put into writing and hedged equity volatility strategies benchmark indexes and replicating ETFs.

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

Understanding the volatility of trading is essential for careers in finance in areas like derivatives research, development of derivatives and derivatives trading, mostly within hedge funds. It is also crucial for academic jobs in the finance of derivatives. It is essential for investors with a solid background in research into strategies for trading volatility.

The learning curve could get more difficult as complexity increases. This course assists by guiding you step-by-step through the use of CBOE(r) and S&P 500(r) strategy for volatility to benchmark indexes and replicating ETNs and ETFs historical data to allow risk-adjusted-performance back-testing to improve efficiency.

Content and Overview

This course is practical and includes six hours of material. It is designed for advanced volatility trading analysis at a high level. Basic knowledge of Python programming language. But not necessary.

In the beginning, you’ll be taught how to download or read CBOE(r) as well as S&P 500(r) strategy for volatility, benchmark indexes and replicating ETNs and ETFs’ data to analyze historical volatility in trading through the installation of related packages and running Python code in the 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. Then, you’ll apply these estimates to predict the risk of volatility using seasonal random walk historical mean simple moving average exponentially weighted averages and an autoregressive integrated move as well as general autoregressive conditional heteroscedasticity models. In the next step, you’ll determine the implied volatility of participants in markets using a related volatility index.

Then, you’ll calculate the futures price and compare it against historical data. In the next step, you’ll study the relationship between volatility and returns to assets and risk-based volatility. You’ll also study the volatility risk and the structure of volatility terms, and the volatility skew pattern. Then, you’ll evaluate the risk of volatility based on the historical implied volatility index’s daily returns and the non-normality of the probability distribution. In the next phase, you’ll examine the volatility hedge futures trading strategy historical performance about risk-adjusted using the related to the hedged equity volatile futures strategies benchmark index, which is a replicating ETF as well as ETN.

Then, you’ll calculate options call prices and place them through the Black and Scholes model in conjunction with options Greeks. In the next step, you’ll evaluate the risk associated with asset returns by studying the historical returns of stocks indexes daily Probability distribution anomalies. In the final, you’ll examine the risk of covered calls or buy writes and cash-secured short puts or put write as well as volatile tail hedged options strategies for trading’ historical performance that is risk-adjusted using associated buy-write put write, hedged equity volatility strategies benchmarks and replicating ETFs.

Who should this course be intended for:

• Postgraduates or undergraduates who wish to understand volatility trading analysis with the help of Python, the programming language.
• Researchers from academia or finance who want to expand their knowledge of the field of derivatives finance.
• Highly educated investors with expertise in financial derivatives and who want to learn about strategies for trading volatility.
• This course is not all about “get rich quick” trading strategies or magical formulas.