School of Mathematical Sciences,Shanxi University

We propose two estimators, an integral
estimator and a discretized estimator, for the wavelet coefficient
of volatility in time series models. These estimators can be used to
detect the changes of volatility in time series models. The location
estimators of the jump points, we proposed, have been shown to have
the minimax convergence rate, which is the optimal rate for the
estimation of change points. The jump sizes and locations of change
points can be consistently estimated by wavelet coefficients. The
convergency rates of these estimators are derived and the asymptotic
distributions of the statistics are established.