# QuantOracle GARCH Volatility Forecast

> QuantOracle GARCH Volatility Forecast is a paid API for AI agents from api.quantoracle.dev, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-15).

Fits a GARCH model to a time series of returns and forecasts future volatility (conditional variance) over a specified horizon

## Facts

- Endpoint: POST https://api.quantoracle.dev/v1/stats/garch-forecast
- Price: $0.015/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/api-quantoracle-dev-67af3bde
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FePrZ-eaiVRXYuCWn088a

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability api-quantoracle-dev-67af3bde -d '<json body>'
```

Example prompt: Run a GARCH(1,1) volatility forecast on this daily BTC return series [0.02, -0.01, 0.03, ...] and give me the predicted conditional volatility for the next 10 days with 95% confidence intervals.

## When to prefer this

Use this endpoint when you need a statistically rigorous conditional volatility forecast based on GARCH modeling, especially for assets exhibiting volatility clustering (e.g., crypto, equities). Prefer this over simple rolling-window volatility when you need forward-looking variance estimates with confidence bounds rather than just historical realized volatility.

## Known failure modes

- Non-stationary or insufficient return series causes model fitting failure
- Invalid GARCH order (p or q = 0 or excessively large) returns parameter error
- Return series too short to estimate parameters reliably
- Convergence failure if series has extreme outliers or near-unit-root behavior
- Missing or malformed input array returns 400 validation error

## How this service works

QuantOracle: stats/garch-forecast

## Output

Returns GARCH model coefficients (omega, alpha, beta), forecasted conditional variance and volatility for each step in the horizon, and confidence intervals around the volatility forecast. May also include model fit statistics such as log-likelihood or AIC.

## Example request

```json
{
 "p": 1,
 "q": 1,
 "horizon": 10,
 "returns": [
  0.0125,
  -0.0089,
  0.0234,
  -0.0156,
  0.0098,
  0.0167,
  -0.0203,
  0.0145,
  -0.0112,
  0.0189,
  0.0076,
  -0.0134,
  0.0201,
  -0.0167,
  0.0143,
  0.0098,
  -0.0176,
  0.0212,
  -0.0145,
  0.0167,
  0.0089,
  -0.0198,
  0.0156,
  -0.0123,
  0.0178,
  0.0134,
  -0.0167,
  0.0201,
  -0.0089,
  0.0145,
  0.0112,
  -0.0176,
  0.0198,
  -0.0134,
  0.0167,
  0.0098,
  -0.0145,
  0.0189,
  -0.0156,
  0.0123,
  0.0167,
  0.0145,
  -0.0198,
  0.0176,
  -0.0112,
  0.0134,
  0.0201,
  -0.0167,
  0.0089,
  0.0156
 ],
 "mean_model": "constant"
}
```

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "returns": {
   "type": "array",
   "items": {
    "type": "number"
   },
   "title": "Returns",
   "maxItems": 5000,
   "minItems": 30,
   "description": "Array of return data (max 5000)"
  },
  "mean_model": {
   "enum": [
    "zero",
    "constant"
   ],
   "type": "string",
   "title": "Mean Model",
   "default": "zero",
   "description": "Mean model specification"
  },
  "forecast_periods": {
   "type": "integer",
   "title": "Forecast Periods",
   "default": 5,
   "maximum": 252,
   "minimum": 1,
   "description": "Number of periods to forecast ahead (max 252)"
  }
 }
}
```

## More

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