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We establish a negative moment bound for the sample autocovariance matrix ofa stationary process driven by conditional heteroscedastic errors. This momentbound enables us to asymptotically express the mean squared prediction error(MSPE) of the least squares predictor as the sum of three terms related tomodel complexity, model misspecification, and conditional heteroscedasticity. Adirect application of this expression is the development of a model selectioncriterion that can asymptotically identify the best (in the sense of MSPE)subset AR model in the presence of misspecification and conditionalheteroscedasticity. Finally, numerical simulations are conducted to confirm ourtheoretical results.

Negative Moment Bounds for Sample Autocovariance Matrices of Stationary Processes Driven by Conditional Heteroscedastic Errors and Their Applications