long-memory processes

Wednesday, April 25, 2018 - 9:00am - 9:30am
Alain Hecq (Universiteit Maastricht (Rijksuniversiteit Limburg))
This paper aims at evaluating the forecasting performances of a set of univariate fractional white noise processes versus multivariate factor models for realized volatility measures. The literature on the sources of long-memory is quite large, from the aggregation across heterogeneous series to the impact of structural changes that spuriously lead to the detection of fractional integrated process. Alternatively, Chevillon, Hecq and Laurent (2018) investigate the mechanisms underlying the long-memory feature generated from large vector autoregressive models.
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