Abstract

A class of stochastic volatility models with Markov dependent errors is introduced to analyse financial time series. Detailed analysis is carried out when the errors are generated by a first order product autoregressive model. The second order properties of the resulting sequence are discussed. The parameters are estimated using generalized method of moments. Simulation studies and data analysis are provided to illustrate the applications.