When estimating the GARCH model, an intermediate step is to predict tomorrow's return.

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BlackTom AI · Accepted Answer

Question restatement: The prompt asks about the reasoning behind predicting tomorrow's return as an intermediate step when estimating a GARCH model.
Option 1: 'Make sense' — This would claim that forecasting tomorrow's return is a sensible or meaningful step within GARCH estimation. In typical GARCH modeling, the primary intermediate quantity of interest is the conditional variance (volatility) or the conditional mean for forecasting return, but the standard estimation procedure focuses on updating the conditional variance process and fitting parameters, not on predicting a one-step-ahead return as an essential part of estimating the model parameters. Therefore, claiming that predicting tomorrow's return is an intermediate step would be misleading or incomplete, as the emphasis is on conditional variance dynamics and parameter estimation rather than producing a point forecast as part of the estimation routine.
Option 2: 'Make no sense' — This option argues that predicting tomorrow's return is not an intrinsic part of the GARCH parameter estimation process. This aligns with the typical understanding that GARCH models are estimated using past information to model volatility dynamics, and while one-step-ahead returns can be produced after estimation for forecasting, they are not a required intermediate step in the estimation itself. Consequently, labeling the step as making no sense within the estimation framework is reasonable because the core estimation procedure concentrates on variance dynamics and parameter likelihood, not on generating tomorrow's return forecasts during parameter estimation.
Considerations and potential pitfalls: Some readers might conflate forecasting with estimation. In practice, practitioners may generate one-step-ahead return forecasts after fitting the model, but this is distinct from the estimation procedure itself. The question appears to be about the role of predicting tomorrow's return as part of estimating the model, which is not standard practice, hence the 'Make no sense' stance.
In summary, Option 1 asserts a sense that is not typically part of the GARCH estimation workflow, while Option 2 correctly reflects that predicting tomorrow's return is not an intrinsic intermediate step in estimating a GARCH model. The contrast hinges on distinguishing estimation from forecasting, and recognizing that volatility modeling relies on updating conditional variance dynamics rather than producing one-step-ahead return predictions as a core estimation step.

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When estimating the GARCH model, an intermediate step is to predict tomorrow's return.

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