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To infinity and beyond: Efficient computation of ARCH(∞) models

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  • Morten Ørregaard Nielsen
  • Antoine L. Noël

Abstract

This article provides an exact algorithm for efficient computation of the time series of conditional variances, and hence the likelihood function, of models that have an ARCH(∞) representation. This class of models includes, for example, the fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model. Our algorithm is a variation of the fast fractional difference algorithm of Jensen, A.N. and M.Ø. Nielsen (2014), Journal of Time Series Analysis 35, 428–436. It takes advantage of the fast Fourier transform (FFT) to achieve an order of magnitude improvement in computational speed. The efficiency of the algorithm allows estimation (and simulation/bootstrapping) of ARCH(∞) models, even with very large data sets and without the truncation of the filter commonly applied in the literature. In Monte Carlo simulations, we show that the elimination of the truncation of the filter reduces the bias of the quasi‐maximum‐likelihood estimators and improves out‐of‐sample forecasting. Our results are illustrated in two empirical examples.

Suggested Citation

  • Morten Ørregaard Nielsen & Antoine L. Noël, 2021. "To infinity and beyond: Efficient computation of ARCH(∞) models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(3), pages 338-354, May.
    • Handle: RePEc:bla:jtsera:v:42:y:2021:i:3:p:338-354
    DOI: 10.1111/jtsa.12570
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    References listed on IDEAS

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    1. Andreas Noack Jensen & Morten Ørregaard Nielsen, 2014. "A Fast Fractional Difference Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(5), pages 428-436, August.
    2. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
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    Cited by:

    1. Martin Magris & Alexandros Iosifidis, 2023. "Variational Inference for GARCH-family Models," Papers 2310.03435, arXiv.org.

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