Volatility Modeling for Prediction Markets
Build volatility models for Polymarket Up/Down markets using historical order book snapshots from PolyHistorical.
Why Volatility Modeling Matters
Volatility in prediction markets behaves differently from traditional financial markets. Polymarket contracts are bounded between 0 and 1, have binary outcomes, and exhibit time-dependent behavior as resolution approaches. Understanding these dynamics using PolyHistorical data gives traders a significant edge.
Types of Volatility in Prediction Markets
| Type | Description | How to Measure |
|---|---|---|
| Realized Volatility | Actual historical price variation | Standard deviation of log returns from midpoint prices |
| Order Book Volatility | Variation in order book depth and spread | Track bid-ask spread and depth changes over time |
| Implied Volatility | Market-expected future variation | Derived from option-like properties of binary contracts |
| Event Volatility | Spikes around scheduled events | Compare pre/post event order book state |
Realized Volatility from Order Book Data
The most straightforward approach uses midpoint prices from PolyHistorical order book snapshots. Calculate log returns at your desired frequency (5-minute, 15-minute, hourly) and compute rolling standard deviations. Sub-second data from PolyHistorical lets you compute realized volatility at much finer granularity than minute-level data allows.
Implementation Steps
- Fetch order book snapshots for your target market using the PolyHistorical API
- Extract midpoint price: (best_bid + best_ask) / 2
- Compute log returns: ln(price_t / price_{t-1})
- Calculate rolling standard deviation (e.g., 24-hour window)
- Annualize if needed: multiply by sqrt(periods_per_year)
GARCH Models for Prediction Markets
GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models capture volatility clustering — periods of high volatility tend to follow high volatility. Prediction markets exhibit strong clustering around news events and market resolution windows. Use PolyHistorical's 30-day rolling history to fit GARCH(1,1) models and forecast near-term volatility.
Time Decay Effects
Prediction market volatility has a unique property: it tends to decrease as resolution approaches when the outcome becomes clearer, or spike dramatically if uncertainty remains. PolyHistorical data lets you study these patterns across historical markets to build more accurate volatility models.
Key Observations
- Markets with prices near 0.50 tend to have higher volatility (maximum uncertainty)
- Volatility compresses as prices approach 0 or 1 (outcome becomes certain)
- BTC/ETH Up/Down markets show volatility spikes around CPI and FOMC announcements
- Sub-second data captures microstructure volatility invisible in minute-level data
Start Building Volatility Models
PolyHistorical's free tier provides enough BTC market data to build and validate volatility models. For production systems tracking multiple markets, the Pro plan at $11/month gives you the multi-market coverage needed for comprehensive volatility analysis.