Taming the Tails: Mastering Extreme Risk with Machine Learning and Python

The Python Lab
20 min readOct 20, 2024

I remember vividly the market crash of 2008. As a junior analyst, I watched in horror as seemingly stable portfolios crumbled overnight. Traditional risk models, relying heavily on historical data and normal distribution assumptions, had failed to predict the magnitude of the downturn. This experience sparked a deep interest in understanding how to better prepare for the unexpected, the black swan events that reside in the tails of the distribution.

This is where the powerful synergy of machine learning and extreme value theory comes into play. Machine learning excels at uncovering complex patterns within vast datasets, while extreme value theory provides the tools to model and predict events that lie outside the realm of “normal” market behavior.

Photo by Jack B on Unsplash

Table of Contents

  • Data Acquisition and Preprocessing for Financial Time Series: Wrangling real-world data, handling missing values and feature engineering for optimal model performance.
  • Extreme Value Theory in Action: Exploring distributions beyond the ordinary — the Generalized Pareto Distribution and techniques for threshold selection.
  • Machine Learning for Tail Risk: Training models to predict extreme events using algorithms like Support Vector Machines, Random Forests and…

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The Python Lab
The Python Lab

Written by The Python Lab

Discovering the power of algorithms in Python. Exploring ML, AI, and Deep Learning. Data-driven trading strategies.

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