Market Volatility — VIX

Dedicated “Market Volatility Signal” capturing option-implied risk sentiment from the S&P 500 (CBOE VIX). Complements Liquidity, Credit, and USD signals.

Why: Captures risk sentiment in S&P 500 options. Source: CBOE / FRED (VIXCLS). Interpretation: <20 = calm; ≥30 = stress.

Abstract

We build a daily-to-monthly Market Volatility Signal (MVS) using the closing VIX index (VIXCLS). Components include level, short/medium-term momentum, and percentile rank vs. history. A composite score maps to regimes—CALM, NORMAL, ELEVATED, STRESSED, CRISIS—anchored to intuitive thresholds (<20 calm; ≥30 stress) and robust z-scores.

1) Data

Frequency: business daily. For monthly reporting, we snapshot month-end; for weekly, last business day of week. Bounded forward-fill is allowed for calendar alignment (≤3 business days).

2) Data Quality & Validation

  • Staleness: flag if last obs > 3 business days old.
  • Bounds & outliers: winsorise at 0.5–99.5th pct for stability; VIX is strictly non-negative.
  • Minimum history: ≥ 5 years preferred for percentile/z-scores; adaptive window ≥ 2 years allowed.
  • Trading holidays: treat gaps as non-trading days; no interpolation beyond limit=3.

data_quality_flag records staleness, winsorisation, and window adaptations.

3) Component Transforms

For daily series v_t (index points):

Level_t = v_t
Δ5d_t = v_t - v_{t-5}
Δ20d_t = v_t - v_{t-20}
PctRank_{2y}(t) = \text{percentile rank of } v_t \text{ in last 504 trading days}
EMA_{20}(t) = \text{exponential moving average over 20d}

We form gaps to intuitive thresholds: gap20 = v_t - 20, gap30 = v_t - 30.

4) Standardisation (Rolling z-scores)

Z-scores computed over rolling windows with robust median/MAD; fallback to mean/std if needed.

z_t = \dfrac{x_t - \text{median}_{W}(x)}{1.4826\,\text{MAD}_{W}(x)}

Suggested windows: W=504d (~2y) for level-based components; W=252d (~1y) for momentum.

5) Weighting & Composite Construction

We emphasise the level and percentile rank, with secondary weight to short-term momentum:

{
  "level_z": 0.40, "pct_rank": 0.25,
  "gap20_z": 0.10, "gap30_z": 0.10,
  "d5_z": 0.10, "d20_z": 0.05
}
MVS_t = \sum_k w_k\, z^k_t,\quad \sum_k w_k = 1

Missing components are renormalised out; contributions are stored for audit.

6) Scaling & Regime Mapping

Regimes reflect absolute thresholds and relative elevation:

  • CALM: VIX < 16 and MVS z < 0
  • NORMAL: 16 ≤ VIX < 20 or MVS z in [0, +0.5)
  • ELEVATED: 20 ≤ VIX < 30 or MVS z in [+0.5, +1.0)
  • STRESSED: VIX ≥ 30 or MVS z ≥ +1.0
  • CRISIS: VIX ≥ 45 or MVS z ≥ +2.0 sustained ≥ 5d

We also report a 0–100 score using rolling min–max scaling over 2–5 years.

7) Comparison Hooks

  • Liquidity Composite: Rising VIX with tight liquidity reinforces risk-off.
  • Credit Spreads: High-yield OAS widening with ELEVATED/ STRESSED VIX strengthens stress signal.
  • USD / Real Yields: Risk-off often coincides with stronger USD and higher real yields; note divergences.

Only the other signals’ latest regime/score are required; no structural dependency assumed.

8) Implementation Notes (Python)

# FRED series (daily closes)
vix = fred.get_series("VIXCLS")

# Align to business daily; bounded ffill (<=3)
vix_d = vix.asfreq("B").ffill(limit=3)

# Components
import pandas as pd
d5  = vix_d - vix_d.shift(5)
d20 = vix_d - vix_d.shift(20)
gap20 = vix_d - 20.0
gap30 = vix_d - 30.0
ema20 = vix_d.ewm(span=20, adjust=False).mean()

# Robust z-scores on rolling windows (252-504d), then weighted sum -> MVS, map regimes as specified.

9) Reproducibility & Monitoring

10) Interpretation & Applications

VIX is a short-horizon risk gauge. Values below 20 are generally consistent with calm conditions; 30 and above indicate market stress. Use alongside Liquidity and Credit to calibrate risk-on/off stance and to set expectations for drawdown risk.

11) Governance & Change Control