Journal of Risk
ISSN:
1465-1211 (print)
1755-2842 (online)
Editor-in-chief: Farid AitSahlia
Statistical benefits of value-at-risk with long memory
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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