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.

You can still contribute to the project and get perks for your contribution by becoming a paid subscriber to this publication, which will give you access to the Applied Quantitative Investment Management course and the expanding collection of exclusive case studies.

Watch the next video here:

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