Is realized volatility autocorrelation due to causal relationships between current realized volatility and future latent volatility?

External question — Quantitative Finance Stack Exchange Author: Oneday Original post: https://quant.stackexchange.com/questions/85288 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Realized volatility is autocorrelated, that could be due to either: Latent volatility takes time to change, thus, time periods close together have similar latent volatility. There's a causal relationship between realized and latent volatility. Higher realized volatility now causes higher latent volatility later. Which one is correct?

External answer — Quantitative Finance Stack Exchange Author: QuantCalc.net Original post: https://quant.stackexchange.com/a/85290 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. Short answer is that the second one is more close to market consensus. Volatility autocorrelation (also known as volatility clustering or conditional heteroscedasticity) is typically modeled using ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. Autocorrelation in Shocks (Squared Returns): While daily returns (the actual percentage price change) are generally uncorrelated with previous returns (making the market "efficient"), their magnitude is not. If you look at the squared returns (a mathematical way to measure the size of the shocks, regardless of whether they are positive or negative), you'll see a strong positive correlation over time. The Role of GARCH Models: GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are essentially a formal, mathematical way to capture this persistence. They make the variance (volatility) of today's returns conditional on (dependent on) the squared returns and estimated variances from previous days.