The Precision-Value Paradox: Can Complex Machine Learning Models Truly Enhance Volatility Forecasting?

Replication package for When Does Complexity Pay? — a multi-asset volatility-forecasting pipeline covering S&P 500, Bitcoin, and clean-energy/carbon ETFs, with KRBN, SPYX, and PBW as robustness assets. main.py is the entry point, with four subcommands: screen (liquidity and proxy-validity screening, run first), run (main results), robust (rolling-window grid), and selftest (offline check on synthetic data). config.py holds all configuration — assets, transaction costs, window lengths, paths — and is the only file that should need editing. src/ contains the eight computation modules: data cleaning and the Garman–Klass proxy, market-efficiency diagnostics, the seven forecasting models (GARCH, EGARCH, GJR, HAR-RV, XGBoost, LSTM, Hybrid), statistical evaluation (DM, GW, MCS, and three VaR backtests), economic-value calculations, mechanism decomposition, and the table and figure writers. data/ holds raw yfinance dumps and cleaned series for six tickers. output.zip contains the produced results: forecast caches, 25 tables in CSV and LaTeX, six figures in PNG and PDF, and full run logs. requirements.txt lists the eleven dependencies.