SKEW: The Market's Own Admission of Tail Risk

Description and use

SKEW reveals how the market prices crash risk even during calm periods. Integrating the index into a crash simulator helps detect hidden threats and enhance risk models.

Problem

Risk models that rely solely on realized history miss the market’s continuous pricing of tail risk, leading to underestimation of crash probability during calm periods.

Outcome

By integrating SKEW, the crash simulator identifies steep skew during rallies, signaling heavy tail pricing that historical data alone would miss, thus improving risk model accuracy.

Workflow

• 1. Download VIX and SKEW CSV data from Cboe public endpoints.
• 2. Compute 5‑year percentile ranks and 5‑day/21‑day changes.
• 3. Classify market regime as calm, stressed, or panic based on VIX and SKEW thresholds.
• 4. Output a hazard multiplier reflecting tail risk intensity.
• 5. Feed multiplier into downstream risk model (V9 P2) for endogenous hazard calibration.

Evidence

SKEW is a Cboe index that quantifies the volatility skew: the difference between what the options market charges for out‑of‑the‑money puts versus what it charges for upside calls.

Source

The simulator now ingests both Cboe indices through a small free‑data pipeline (src/market_state.py).

Source

Running it today: VIX 15.3 (25th percentile, calm), SKEW 151.58 (83rd percentile, high). The pipeline classified the regime as calm — while flagging the unusually heavy tail pricing that history alone would never show you.

Source