Abstract
The class of Randomized Generalized Autoregressive Conditional Heteroskedastic (R-GARCH) models represents a generalization of the GARCH models, adding a random term to the volatility with the purpose to better accommodate the heaviness of the tails expected for returns in the financial field. In fact, it is assumed that this term has stable distribution. Allowing both, returns and volatility, to have stable distribution, a new class of models to describe volatility arises: Stable Randomized Generalized Autoregressive Conditional Heteroskedastic Models (SR-GARCH). The indirect inference method is proposed to estimate the SR-GARCH parameters, theoretical results concerning dependence structure are obtained. Simulations and an empirical application are presented.
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