Big moves tend to be followed by big moves (in either direction), and quiet periods tend to be followed by quiet periods. If you plot the S&P 500 or Bitcoin returns, you don’t see random scatter. You see pockets of chaos and pockets of calm.
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. arch models
If you have ever tried to predict stock market volatility, you have run into a frustrating reality: Big moves tend to be followed by big
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. It’s conditional heteroskedasticity in action
April 14, 2026 | Reading Time: 5 minutes