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*How do you forecast an event that has never happened before?*

How do you forecast an event that has never happened before?

The recent closure and reopening of the Strait of Hormuz are unique events. For events like these, traditional risk models lose their statistical basis: repetition. Alexander Denev returns to the podcast to show how causal models (Bayesian networks) let us reason about rare events despite this limitation.

In this episode, we cover:

- Why value-at-risk and other correlation-based models break exactly when you need them most
- How a causal structure can "hold in time"
- Building scenarios with LLMs - benefits, drawbacks, and lessons learned
- Historical analogy as a modeling tool: Bosphorus, Hormuz, and more
- A three-way robustness test for any Bayesian network
- How the model's call held up: a ceasefire, a still-closed strait, and lasting infrastructure damage keeping oil elevated

"History doesn't repeat itself, but it rhymes."

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Video version available on the Youtube: https://youtu.be/FzKy2ws-7qs
Recorded on May 29, 2026 in London, UK.

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*About The Guest*
Alexander Denev works at the intersection of quantitative finance, causality, and AI. He's the CEO of Turnleaf Analytics and the author of two books on applying Bayesian networks and probabilistic graphical models to finance and scenario analysis.

Connect with Alexander:
- Alexander on LinkedIn: https://www.linkedin.com/in/alexander-denev-66a25824/
- Alexander's web page: https://turnleafanalytics.com/

*About The Host*
Aleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4 ).

Connect with Alex:
- Alex on the Internet: https://bit.ly/aleksander-molak

*Links*
Web

- Alexander's LinkedIn post, Bayesian-network scenario for the Strait of Hormuz / Israel-Iran-US conflict: https://www.linkedin.com/posts/alexander-denev-66a25824_when-modelling-the-impact-of-events-that-share-7442892381668048896-JDs5/
- Risk.net article, "Iran confusion makes the case for causal modelling": https://www.risk.net/our-take/7963361/iran-confusion-makes-the-case-for-causal-modelling

Books

- Rebonato, R. & Denev, A. - Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation (https://amzn.to/3vE6Jc1)
- López de Prado, M. - Advances in Financial Machine Learning (https://amzn.to/3PXD8kH)
- Molak, A. - Causal Inference and Discovery in Python (https://amzn.to/3VVK4m3)
- Denev, A. - Probabilistic Graphical Models: A New Way of Thinking in Financial Modelling (https://amzn.to/3VQeLJm)
- Pearl, J. & Mackenzie, D. - The Book of Why (recommended entry point) (https://amzn.to/4e0ATrZ)
- Pearl, J. - Causality: Models, Reasoning and Inference (for advanced readers) (https://amzn.to/49zBKf5)
- Rebonato, R. - Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress (https://amzn.to/3RC411e)

*Perks & resources*
🚀 Join FREE Causal Python Weekly Newsletter: https://causalpython.io
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*Let's connect!*
👉🏼 Linkedin: https://www.linkedin.com/in/aleksandermolak/
👉🏼 Bluesky: https://alxndrmlk.bsky.social
👉🏼 Tiktok: https://www.tiktok.com/@alex.molak

*Business*
👉🏼 Consulting and Causal AI Training For Your Team: [email protected]

*Podcast Playlist*
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*Causal Bandits Team*
Project Coordinator: Taiba Malik (https://www.instagram.com/taibasplay/)
Video and Audio Editing: Navneet Sharma

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