Modeling Forecast-Error Volatility with GARCH in ARIMAX

Summary

The document poses a modeling question: whether GARCH can be applied to time-series models that include exogenous variables, such as an ARIMAX model. It gives cash-flow forecasting as an example and asks whether the model’s residuals could be described with GARCH to forecast volatility in cash-flow prediction errors.

No answer, estimation procedure, data, or empirical evidence is included. The material therefore introduces a possible extension of a forecasting model rather than establishing that it is appropriate or effective. It does not discuss assumptions about residual behavior, model specification, forecast horizons, or how a volatility forecast would be used. Those questions would need to be examined before drawing conclusions about a particular cash-flow application.

Key ideas

• The document asks whether GARCH volatility modeling can accompany a time-series model with exogenous predictors.
• Cash-flow forecast errors are offered as a possible application for conditional volatility modeling.
• The source poses the question but gives no model specification, method, or empirical findings.
• Whether the approach is suitable depends on the behavior of residuals and the forecasting context, neither of which is assessed here.

Tags

# Can you apply GARCH to ARIMAX models?


# Can you apply GARCH to ARIMAX models?












Is it possible to apply the idea of GARCH to time series models that include exogenous variables?

For example, say I estimate a cash flow forecast model. Does it make sense to model the residuals by use of a GARCH model? Or formulated alternatively: Does it make sense to try to forecast volatility in cash flow forecast errors by usage of a GARCH model?

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.