Simulation of tail risk noise and asset price

End-to-End Python implementation of Regime-Weighted Conformal (RWC) prediction for sequential VaR control in nonstationary financial markets (Schmitt, 2026). Combines kernel-based regime similarity with exponential time decay to calibrate distribution-free risk bounds. CRSP data validation, GBDT quantile forecasting, and rigorous backtesting.

Analyze cryptocurrency return dynamics, volatility, and risk from 2010–2025.

Regime detection using Hidden Markov Models with Swan Beta features to identify tail-risk market states.

Predicting the probability of equity market crash events using historical return-based features, with a fixed crash definition and a focus on tail risk. The model is evaluated using the SPDR S&P 500 ETF (SPY) as a proxy for the S&P 500 Index, with data sourced via the yfinance API.

An End-to-End Python implementation of Köhler et al.'s (2026) orthogonalized tail-risk framework. Combines PCA-whitening spectral decomposition with Peaks-Over-Threshold EVT to quantify extreme risks in 479-dimensional financial networks. Implements Ferro-Segers clustering, dynamic residualization, and out-of-core processing for 2.6B+ data points.

VaR & CVaR python package. Computation and backtesting.

Actuarial tail risk quantile/expectile regression for insurance pricing - TVaR, large loss loading, ILF curves, CatBoost