Enter (introduced by Tim Bollerslev in 1986). A GARCH(1,1) model—the industry workhorse—uses only three parameters to capture volatility dynamics:
The equation looks intimidating, but it’s just a weighted average of past surprises: arch models
April 14, 2026 | Reading Time: 5 minutes Enter (introduced by Tim Bollerslev in 1986)
[ \sigma_t^2 = \omega + \alpha_1 \epsilon_t-1^2 + \alpha_2 \epsilon_t-2^2 + ... + \alpha_q \epsilon_t-q^2 ] It’s conditional heteroskedasticity in action
If you have ever tried to predict stock market volatility, you have run into a frustrating reality:
Next time you see a market flash crash or a sudden calm, remember: it’s not randomness. It’s conditional heteroskedasticity in action. Have you used GARCH models in production? Or do you prefer modern alternatives like stochastic volatility or deep learning? Let me know in the comments.
The Black-Scholes model assumes constant volatility—which traders know is false. GARCH-based option pricing models (e.g., Heston-Nandi) better capture the volatility smile.