AbstractIn this study, the wavelet multiscale model is applied to selected assets to hedge time‐dependent exposure of an agent with a preference for a certain hedging horizon. Based on the in‐sample and out‐of‐sample portfolio variances, the wavelet‐based generalized autoregressive conditional heteroskedasticity (GARCH) model produces the lowest variances. From a utility standpoint, wavelet networks combined with GARCH have the highest utility. Finally, the wavelet‐GARCH model has the lowest minimum capital risk requirements. Overall, the wavelet GARCH and wavelet networks offer improvements over traditional hedging models.