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Abstract:
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In many instances the Generalized Autoregressive Conditional Heteroscedastic (GARCH) formulations are inadequate to model the persistent volatility found in financial assets. The integrated version of the GARCH formulation, namely the IGARCH model, was developed to handle such situations. Fractionally Integrated Generalized Autoregressive Conditionally Heteroscedastic (FIGARCH) models, however, provide a more flexible alternative to modeling long-term dependence of volatility, providing a leptokuric unconditional distribution for returns having long memory behavior. Here in a method, based on the residual bootstrap, to obtain prediction intervals for the returns of the process is proposed. A Monte-Carlo simulation study, conducted using a variety of distributions for the error terms, show that the proposed intervals have reasonable coverage probabilities in most cases.
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