[ \sigma_t^2 = \omega + \alpha_1 \epsilon_t-1^2 + \alpha_2 \epsilon_t-2^2 + ... + \alpha_q \epsilon_t-q^2 ]
If you work in trading, risk, or quantitative finance, GARCH(1,1) should be as familiar to you as linear regression. It is the baseline—the "check your assumptions" model for anything involving volatility. arch models
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. [ \sigma_t^2 = \omega + \alpha_1 \epsilon_t-1^2 +