If you have ever tried to predict stock market volatility, you have run into a frustrating reality:
April 14, 2026 | Reading Time: 5 minutes
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
Beyond the White Noise: Why Financial Markets Need ARCH and GARCH Models
This matches reality. After the COVID crash in March 2020, the VIX (fear index) stayed above 25 for nearly six months. 1. Risk Management If you assume volatility is constant, your Value at Risk (VaR) will be wrong 90% of the time. GARCH models give you dynamic VaR—higher during crises, lower during calm periods. If you have ever tried to predict stock
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.
But an ARCH model recognizes a pattern: Large errors tend to be followed by large errors of either sign. At its core, an ARCH(q) model says: Today's variance depends on the squared "shocks" (unexpected returns) from the previous q days. In simple terms: If the market has been crazy for the last week, tomorrow will probably also be crazy. After the COVID crash in March 2020, the
For decades, standard statistical models assumed something called homoscedasticity —a fancy way of saying "constant variance." But financial returns are clearly heteroscedastic (changing variance).