rgourley rough-vol-forecast
You hand over a ticker and a set of forecast horizons (default 1, 5, 20, 60, 120 trading days). The skill fits daily-return realized vol on a 2-year window, then applies three vol-scaling models across each horizon:
- Traditional Brownian: sigma(h) = sigma_daily × sqrt(h). Standard sqrt-time scaling.
- EWMA (RiskMetrics): same sqrt-time scaling but on a decay-weighted vol estimate that responds faster to recent regime.
- Rough vol (Bayer-Friz-Gatheral 2016): sigma(h) = sigma_daily × h^H with H = 0.14 (Livieri et al. 2018 empirical default). Damps long-horizon growth substantially.
Answers "how much does horizon really matter for vol?" — which turns out to be the big 2024-25 vol modeling debate.
When to invoke
- "What's my 60-day forward vol on SPY?"
- Comparing vol assumptions in options pricing / position sizing
- Auditing whether sqrt-time scaling is over-estimating your scenario vol
- The user says "rough vol", "Bayer Friz Gatheral", "vol scaling", "horizon vol"
Not for: options pricing (this is not a calibrated rBergomi engine). Not for regime detection (use change-point-detector or market-regime).
What you need
- A ticker (
--ticker) MASSIVE_API_KEYexported- Stocks Basic minimum
Optional:
--horizons(default1,5,20,60,120)--lookback-days(default 504)--hurst(default 0.14, Livieri et al. 2018 estimate on daily equity data)--ewma-lambda(default 0.94, RiskMetrics)
What you get back
Two output layers.
Layer 1: canonical JSON. Per-horizon traditional_vol,
ewma_vol, rough_vol, and rough_over_traditional ratio. Plus
realized_annualized_vol, ewma_annualized_vol, hurst_used, and
hurst_estimated_on_returns (for transparency, not used as default).
Layer 2: rendered note. Header + per-horizon table with the three vol estimates side by side and the rough-vs-traditional ratio, one-line Take.
How it works
Rough volatility literature: realized vol has Hurst exponent H ~ 0.05-0.20 empirically on financial series (Bayer-Friz-Gatheral 2016 established the framework; Livieri et al. 2018 estimated H ~ 0.14 on daily equity data). Under rough vol, sigma(h) scales as h^H rather than h^(1/2). For H < 0.5, this:
- Damps long horizons: 120-day vol forecasts drop meaningfully vs sqrt-time.
- Lifts short horizons: 1-day vol edges higher (though the effect is small at h=1).
Foundations used
massive-api-patternsfor REST + aggs.- Internal
quant_garage.monte_carlo.rough_vol_annualizedhelper.
Output mode: note
Narrative note with a per-horizon table. Fewer than 10 numbers per run; table reads better than pure prose.
Endpoints used
GET /v2/aggs/ticker/{T}/range/1/day/{from}/{to}?adjusted=trueOne call per run.
Doesn't handle (yet)
- rBergomi Monte Carlo path simulation. The
simulate_rough_vol_pathshelper is inquant_garage.monte_carloand can be called directly, but it isn't yet wired into position-sizer or mc-portfolio-simulator as--vol rough. Clean extension. - Options-implied H calibration. Real rBergomi calibration uses the options surface; this skill uses returns.
- Multi-name H estimation. Reports one H per run. Cross-name comparison is a workflow, not this skill.
- Regime-conditional H. Rough-vol H can shift with regime; this reports a single window estimate.
These are clean PR extensions.