A wavelet Whittle estimator of generalized long-memory stochastic volatility

We consider a k-GARMA generalization of the long-memory stochastic volatility (LMSV) model, discuss the properties of the model and propose a wavelet-based Whittle estimator for its parameters. Its consistency is shown. Monte Carlo experiments show favorable properties of the proposed method with respect to the Whittle estimator and a wavelet-based approximate maximum likelihood estimator. An application is given for the Microsoft stock, modeling the intraday seasonal patterns of its realized volatility.