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

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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(infinity) 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.o. 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(infinity) 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.

Original languageEnglish
JournalJournal of Time Series Analysis
Pages (from-to)338-354
Number of pages17
Publication statusPublished - May 2021

    Research areas

  • Circular convolution theorem, conditional heteroskedasticity, fast Fourier transform, FIGARCH, truncation, LONG-MEMORY, CONDITIONAL HETEROSCEDASTICITY

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