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extreme-value-theory

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Automatic optimal sequential investment decisions. Forecasts made using advanced stochastic processes with Monte Carlo simulation. Dependency is handled with vine copulas.

  • Updated Feb 25, 2024
  • Jupyter Notebook
VaR-threshold-and-confidence-interval

This project studies the effects of the shape parameter estimator uncertainty at different threshold levels on the value-at-risk confidence interval for quantitative risk management (QRM) using the Generalized Pareto Distribution (GPD) from the Extreme Value Theory (EVT) approach.

  • Updated Aug 30, 2022
  • Jupyter Notebook

Official Python implementation of the Pioneer Detection Method (PDM) — convergence-based expert aggregation and opinion pooling under structural change. Code for Vansteenberghe (2026), The Geneva Papers 51(1).

  • Updated Sep 24, 2026
  • Python

heavytails is a Python library implementing heavy-tailed probability distributions, built from first principles with NumPy-backed vectorized evaluation. The library provides comprehensive support for continuous and discrete heavy-tailed distributions, tail index estimation methods, and diagnostic utilities

  • Updated Sep 21, 2026
  • Python

End-to-End Python implementation of Hayward et. al's (2026) method for modeling financial volatility as a nonlinear wave system. Extracts VIX/VXO/VSTOXX envelopes via FFT and Hilbert transforms, builds a Schrödinger-type Hamiltonian, and tracks eigenvalue-gradient shifts signaling Anderson localisation ahead of volatility events. 

  • Updated Jul 4, 2026
  • Jupyter Notebook

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