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Statistical benefits of value-at-risk with long memory

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  • Andrea Beltratti, Claudio Morana

Abstract

ABSTRACT Are there substantial improvements associated with the use of long memory models in the computation of value-at-risk (VAR)? The performance of the GARCH and the ARFIMA models, the latter estimated using daily variance obtained from high-frequency data, are compared on various criteria. The results show that the long memory model provides a superior performance in terms of multi-step point forecasting. Allowing for time-varying variance of the realized variance process in the context of an ARFIMA–FIGARCH model also substantially improves VAR forecasting.

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  • Andrea Beltratti, Claudio Morana, . "Statistical benefits of value-at-risk with long memory," Journal of Risk, Journal of Risk.
    • Handle: RePEc:rsk:journ4:2161078
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