| name | rough-vol-forecast |
| description | Rough-volatility-scaled vol forecast (Bayer-Friz-Gatheral 2016) for a ticker across multiple horizons. Under rough vol, realized vol scales as h^H with H around 0.14 empirically (Livieri et al. 2018), much slower than the sqrt(t) growth of Brownian motion. This dampens long-horizon extrapolation and lifts short-horizon estimates. Reports the rough-vol forecast alongside traditional Brownian scaling and EWMA for direct comparison at each horizon. Requires Stocks Basic. Runs on the free tier. |
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_KEY exported- Stocks Basic minimum
Optional:
--horizons (default 1,5,20,60,120)--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-patterns for REST + aggs.- Internal
quant_garage.monte_carlo.rough_vol_annualized helper.
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=true
One call per run.
Doesn't handle (yet)
- rBergomi Monte Carlo path simulation. The
simulate_rough_vol_paths helper is in quant_garage.monte_carlo
and 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.
