Estimation of Generalized Long-Memory Stochastic Volatility for High-Frequency Data

摘要

We consider the generalized long-memory stochastic volatility (GLMSV) model, a relatively general model of stochastic volatility that accounts for persistent (or long-memory) and seasonal (or cyclic) behavior at several frequencies. We employ the decorrelating properties of discrete wavelet packet transform (DWPT) to provide a wavelet-based approximate maximum likelihood estimator that allows for analysis of high-frequency data by simplifying the variance-covariance matrix into a diagonalized matrix, whose diagonal elements have the least distinct variances to compute using a computationally efficient quadrature. We apply the proposed method to the estimation of high-frequency simulated data.