8. Causal and Predictive Views and Stress-Testing

This is the tenth video that goes through the fortitudo-tech Python package available at: https://github.com/fortitudo-tech/fortitudo.tech¹

The video goes through the ninth example, which shows you how you can combine Entropy Pooling² with a causal Bayesian network layer³ on top for causal and predictive market views and stress-testing.

It is the accompanying code to the Causal and Predictive Market Views and Stress-Testing framework⁴ article.

The risk factor computations are from the previous week’s example.

For a deep and pedagogical presentation of Entropy Pooling and the causal views and stress-testing framework, see the Portfolio Construction and Risk Management book⁵.

Note that if you contribute €100 or more to the Portfolio Construction and Risk Management book, you will get one-year complimentary paid Substack subscription to this publication when the book is finished. This paid subscription will give you access to exclusive case studies that use the investment framework from the book.

This video is also available on YouTube⁶ if you prefer watching it there.

1. GitHub repository for the fortitudo.tech Python package: https://github.com/fortitudo-tech/fortitudo.tech

2. Entropy Pooling Collection: https://antonvorobets.substack.com/p/entropy-pooling-collection

3. Causal Stress-Testing: https://antonvorobets.substack.com/p/causal-stress-testing

4. Causal and Predictive Market Views and Stress-Testing SSRN article: https://ssrn.com/abstract=4444291

5. Portfolio Construction and Risk Management book: https://antonvorobets.substack.com/p/pcrm-book

6. fortitudo.tech Python package walkthrough YouTube playlist: https://www.youtube.com/playlist?list=PLfI2BKNVj_b2rurUsCtc2F8lqtPWqcs2K