Simulation of tail risk noise and asset price
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Updated
Aug 28, 2024 - Jupyter Notebook
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tail-risk
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Regime detection using Hidden Markov Models with Swan Beta features to identify tail-risk market states.
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Jan 4, 2026
Analyze cryptocurrency return dynamics, volatility, and risk from 2010–2025.
docker ggplot2 r shiny rstudio tidyverse forecast cryptocurrency arima scales volatility garch lubridate tseries drawdown tail-risk qq-plot log-return crash-risk rugarch
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Jan 29, 2026 - HTML
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
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Feb 8, 2026 - Jupyter Notebook
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.
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Feb 1, 2026 - Jupyter Notebook
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