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

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BlackTom AI · Accepted Answer

The question asks about an intermediate step in estimating a GARCH model and whether predicting tomorrow's return is part of that process.
Option 1: 'Make sense' — This would imply that predicting tomorrow's return is a standard intermediate step in GARCH estimation. In typical GARCH modeling, the core focus is on modeling and forecasting the conditional variance (volatility) rather than producing a forecast of the next period's return itself. While you might report a mean return forecast in some applications, the formal estimation and most common forecasting routines center on volatility dynamics, so predicting tomorrow's return is not a fundamental intermediate step. Therefore, this statement does not align with standard GARCH procedure.
Option 2: 'Make no sense' — This correctly identifies that the principal intermediate objective in GARCH is to model and forecast the conditional variance, not the next period's return. The return is often assumed to have a mean structure that is either constant or modeled separately, but the volatility process governs the conditional variance, which is the primary quantity of interest for GARCH estimation.
In summary, the rationale hinges on recognizing that GARCH targets volatility dynamics rather than direct point forecasts of returns, making the idea of predicting tomorrow's return an unusual or inappropriate intermediate step in typical estimation workflows.

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

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