@inbook{5383ea68841c4578afaa0b7ba8e0650c,
title = "Approximating long-memory processes with low-order autoregressions: Implications for modeling realized volatility",
abstract = "Several articles have attempted to approximate long-memory, fractionally integrated time series by fitting a low-order autoregressive AR(p) model and making subsequent inference. We show that for realistic ranges of the long-memory parameter, the OLS estimates of an AR(p) model will have non-standard rates of convergence to non-standard distributions. This gives rise to very poorly estimated AR parameters and impulse response functions. We consider the implications of this in some AR type models used to represent realized volatility (RV) in financial markets.",
keywords = "ARFIMA, HAR models, Long-memory, Realized volatility",
author = "Baillie, \{Richard T.\} and Dooyeon Cho and Seunghwa Rho",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
year = "2024",
doi = "10.1007/978-3-031-48385-1\_17",
language = "English",
series = "Advanced Studies in Theoretical and Applied Econometrics",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "455--481",
booktitle = "Advanced Studies in Theoretical and Applied Econometrics",
}