GARCH Volatility Forecasting for Traders

Educational Guide · Updated March 2026

GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used to forecast volatility — the degree of variation in asset prices over time. Unlike simple historical volatility, GARCH captures the tendency of markets to cluster periods of high and low volatility.

Why Volatility Matters

Volatility directly impacts options pricing, position sizing, risk management, and strategy selection. Knowing whether volatility is likely to increase or decrease helps traders make better decisions about entries, exits, and hedge ratios.

How GARCH Works

The GARCH(1,1) model estimates tomorrow's variance as a weighted combination of:

1. Long-run average variance — the baseline volatility level
2. Yesterday's squared return — how much the last price move matters
3. Yesterday's variance forecast — persistence of the current regime

Algo-Succession uses a Student-t GARCH(1,1) model which better captures the "fat tails" common in financial returns — extreme moves happen more often than a normal distribution predicts.

Practical Applications

• Options trading — Compare GARCH-forecasted vol to implied vol for edge
• Position sizing — Scale positions inversely to forecasted volatility
• Risk management — Set stop-losses based on expected price ranges
• Regime detection — Identify shifts between calm and volatile markets