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Updated: Jun 6, 2025
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
10.3K
Learning extreme expected shortfall and conditional tail moments with neural networks. Application to cryptocurrency
Michaël Allouche1, Stéphane Girard2, Emmanuel Gobet3
1Kaiko - Quantitative Data, 2 rue de Choiseul, Paris, 75002, France.
Summary
This study introduces a novel neural network approach for estimating extreme Expected Shortfall and tail moments in heavy-tailed distributions. The method demonstrates superior performance and stability compared to existing techniques, even on cryptocurrency data.
Area of Science:
- Quantitative Finance
- Machine Learning
- Extreme Value Theory
Background:
- Estimating extreme conditional tail moments, such as Expected Shortfall, is crucial for risk management in finance.
- Heavy-tailed distributions pose significant challenges for traditional estimation methods.
Purpose of the Study:
- To propose a novel neural network-based method for estimating extreme Expected Shortfall and conditional tail moments.
- To establish the theoretical convergence properties of the proposed neural network estimator.
Main Methods:
- Utilizing extreme-value theory and high-order tail conditions to analyze neural network approximation error.
- Employing specific activation functions (eLU and ReLU) within the neural network architecture.
- Comparing the finite sample performance against bias-reduced extreme-value competitors.
Main Results:
- The proposed neural network method significantly outperforms existing competitors in estimating extreme tail moments.
- The neural network approach offers easier and stabler selection of the anchor point.
- Excellent accuracy was achieved when applied to real-world cryptocurrency extreme loss return data.
Conclusions:
- Neural networks provide a powerful and accurate tool for estimating extreme tail moments in heavy-tailed settings.
- The developed method offers practical advantages in terms of performance and parameter selection.
- The approach is validated on both synthetic and real-world financial data, demonstrating its robustness.
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