Calibration
Once a particular SV model is chosen, it must be calibrated against existing market data. Calibration is the process of identifying the set of model parameters that are most likely given the observed data. One popular technique is to use Maximum Likelihood Estimation (MLE). For instance, in the Heston model, the set of model parameters can be estimated applying an MLE algorithm such as the Powell Directed Set method to observations of historic underlying security prices.
In this case, you start with an estimate for, compute the residual errors when applying the historic price data to the resulting model, and then adjust to try to minimize these errors. Once the calibration has been performed, it is standard practice to re-calibrate the model periodically.
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