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chirindaopensource / taming_tail_risk_in_financial_markets

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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.

python machine-learning reproducible-research market-data kernel-methods quantitative-finance uncertainty-quantification value-at-risk gradient-boosting risk-management time-series-analysis backtesting quantile-regression financial-econometrics portfolio-management conformal-prediction sequential-prediction tail-risk icml-2026 crsp-data
  • Updated Feb 8, 2026
  • Jupyter Notebook

chirindaopensource / measuring_market_stress_with_machine_learning

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End-to-End replication of Schmitt's (2026) Market Stress Probability Index. Implements cross-sectional fragility signal extraction, dynamic stress labeling via volatility quantiles and Lasso-Logit forecasting. Includes Jordà local projections for structural impulse response analysis. Strict real-time discipline & expanding-window training.

python machine-learning reproducible-research scikit-learn pandas logistic-regression quantitative-finance financial-data research-paper statsmodels panel-data risk-management time-series-analysis lasso-regression financial-econometrics probabilistic-forecasting systemic-risk bootstrap-methods market-stress crsp-data
  • Updated Feb 15, 2026
  • Jupyter Notebook

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