Learn volatility trading analysis from advanced to expert level with practical course using Python programming language.
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
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.
This comprehensive Volatility Trading Analysis with Python curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
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What you need before starting this Volatility Trading Analysis with Python course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Diego Fernandez
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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