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.
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. arch models
This is where (Autoregressive Conditional Heteroskedasticity) and its big brother GARCH (Generalized ARCH) come to save the day. The Problem with "Constant Volatility" Imagine trying to forecast tomorrow's temperature using a model that assumes the weather has the same variability in July as it does in December. That would be absurd. If you work in trading, risk, or quantitative
For decades, standard statistical models assumed something called homoscedasticity —a fancy way of saying "constant variance." But financial returns are clearly heteroscedastic (changing variance). It’s conditional heteroskedasticity in action
Enter (introduced by Tim Bollerslev in 1986). A GARCH(1,1) model—the industry workhorse—uses only three parameters to capture volatility dynamics: