Detection of jumps by wavelets in a heteroscedastic autoregressive model

Wavelets are applied to detect the jumps in a heteroscedastic autoregressive model. The empirical wavelet coefficients are defined respectively for the conditional mean and the conditional variance of the model. It is shown that the wavelet coefficients exhibit high peaks near the jump points, based on which a procedure is developed to identify and then to locate the jumps. All estimators are shown to be consistent.Department of Applied Mathematic