Volatility Regime Prediction via Causal Discovery in Option Markets - https://github.com/philippdubach/vol-regime-prediction/blob/...

Volatility regime models (Markov-switching GARCH, regime-switching stochastic volatility) are ubiquitous in finance. However, they share a fundamental limitation: regimes are identified ex post from return dynamics, providing no predictive power for regime transitions. The standard approach fits a Hidden Markov Model to returns, labels high and low volatility states, and estimates state transition probabilities that are essentially unconditional averages. This matters because the economic value of volatility timing depends entirely on predicting regime changes before they occur. A model that identifies regimes only after observing the returns is useless for trading volatility.

Existing research documents regime-dependent behavior but does not identify causal drivers of regime transitions. The papers on volatility forecasting factors, variance risk premium dynamics, and market instability from option flows dance around this question without directly addressing it. The recent work on causal ML in finance (double machine learning, causal forests) has focused primarily on equity return prediction rather than volatility states. The connection between options market variables and subsequent volatility regime transitions has not been rigorously established through causal methods.

We develop a causal framework for volatility regime prediction using option-implied variables as potential causes of regime transitions. The key insight is that options markets are forward-looking, so information embedded in the implied volatility surface, put-call ratios, option order flow, and term structure slopes may causally influence future realized volatility regimes rather than merely correlate with them.

Currently building a robust dataset.