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DOUBLY STOCHASTIC MODELS WITH THRESHOLD GARCH INNOVATIONS

Recently, there has been a growing interest in the methods addressing volatility in computational finance and econometrics. Peiris et al. [8] have introduced doubly stochastic volatility models with GARCH innovations. Random coefficient autoregressive sequences are special case of doubly stochastic time series. In this paper, we consider some doubly stochastic stationary time series with GARCH and Threshold GARCH errors. Some general properties of process, like variance and kurtosis are derived.