--- title: "How do volatility forecasting models like GARCH work?" description: "GARCH models forecast volatility by making today variance depend on yesterday squared return and yesterday variance." source_url: "https://www.rupeecase.com/learn/questions/garch-volatility-forecasting" --- Realised volatility comes in calm and stressed stretches, the clustering that a GARCH model is built to track Twenty day realised volatility of the Nifty close, daily, from 1 Jul 2025 to 15 Sep 2026, as an annual percentage. A volatility model is fitted to daily returns, and a series like this is what its forecast is compared with. Source: NSE end of day data, computed by RupeeCase. As of 15 Sep 2026. Volatility is not constant. A large move makes the next large move more likely, and a calm stretch tends to be followed by another calm stretch. A GARCH model writes that pattern down. The variance of tomorrow return is a weighted sum of a constant, yesterday squared return and yesterday variance. The parameters are estimated from the return history, usually by maximum likelihood. The chart shows what a forecast is judged against, not a forecast: twenty day realised volatility of the Nifty close from 1 Jul 2025 to 15 Sep 2026. It is high in some stretches and low in others, and the peak came on 8 Apr 2026. The model itself is fitted to the daily returns, and a series like this one is the check. What the model adds to a simple average is the weighting. A recent shock counts for more than an old one, and the weights decay over time. That makes the forecast jump after a stressed day and fade over the following weeks, which is the shape a volatility index shows after a sharp fall in the index. A GARCH forecast depends on the sample, the distribution assumed for returns and the frequency of the data. Parameters fitted on a calm year can understate the next stressed one. A fit is tested against realised volatility that the model was not shown, and a model that fits the past well can still miss the next regime. GARCH is a volatility model, so it says nothing about the direction of the index. Its forecast can also be set beside option implied volatility. The gap between the two is the subject of the variance risk premium page. **Before you trade.** Derivatives trading carries a high risk of loss. SEBI's study "Profitability of Individual Traders in the Equity Derivatives Segment (FY25 to FY26)" (August 2026) found that 87.7% of individual traders in equity F&O made net losses in FY26, with aggregate net losses of Rs 91,685 crore. [Read the SEBI study](https://www.sebi.gov.in/reports-and-statistics/research/aug-2026/study-profitability-of-individual-traders-in-the-equity-derivatives-segment-fy25-fy26-_103835.html) How do volatility forecasting models like GARCH work?GARCH models forecast volatility by making today variance depend on yesterday squared return and yesterday variance, so large moves raise the forecast for the days after and calm sessions pull it down. They capture volatility clustering, which the record shows clearly, but they do not predict the direction of the index, and the forecast is only as good as the data and the fit. LGo deeper ## Where Learn teaches this. [Go deeperForecasting volatility: EWMA, GARCH and HAR11 min, Advanced](/learn/path-16/module-16-12-forecasting-volatility)[Go deeperMeasuring realised volatility: four estimators9 min, Intermediate](/learn/path-16/module-16-5-realised-volatility-estimators) SSources ## Go to the source. 2 sources, NSE the most cited The publisher of each source listed below. Source: Module undefined source list. 1. [NSE, India VIX: what it measures and how it is computed](https://www.nseindia.com/static/products-services/indices-indiavix-index) NSE 2. [Bollerslev, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics (1986)](https://doi.org/10.1016/0304-4076(86)90063-1) Paper ?Questions people ask ## Related questions. 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