Book ChapterDOI
An introduction to long-memory time series models and fractional differencing
Clive W. J. Granger,Roselyne Joyeux +1 more
- pp 321-337
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TLDR
Generation and estimation of models that provide potentially useful long-memory forecasting properties and applications on generated and real data presented.Abstract:
The idea of fractional differencing is introduced in terms of the infinite filter that corresponds to the expansion of (1 - B)d. When the filter is applied to white noise, a class of time series is generated with distinctive properties, particularly in the very low frequencies and provides potentially useful long-memory forecasting properties. Such models are shown possibly to arise from aggregation of independent components. Generation and estimation of these models are considered and applications on generated and real data presented.read more
Citations
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Journal ArticleDOI
The Distribution of Realized Exchange Rate Volatility
TL;DR: In this article, the authors construct model-free estimates of daily exchange rate volatility and correlation that cover an entire decade using high-frequency data on deutschemark and yen returns against the dollar.
Journal ArticleDOI
25 years of time series forecasting
Jan G. De Gooijer,Rob J. Hyndman +1 more
TL;DR: A review of the past 25 years of research into time series forecasting can be found in this paper, where the authors highlight results published in journals managed by the International Institute of Forecasters.
Book ChapterDOI
A long memory property of stock market returns and a new model
TL;DR: In this paper, the power transformation of the absolute turn |rt|d also has a high autocorrelation for long lags, and it is possible to characterize |rt |d to be "long memory" and this property is strongest when d is around 1.
Journal ArticleDOI
Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns
Clive W. J. Granger,Namwon Hyung +1 more
TL;DR: In this article, the authors compare two time series models, an occasional break model and an I(d) model, to analyze S&P 500 absolute stock returns, and find that an occasional-break model provides less competitive forecasts, but not significantly.
Journal ArticleDOI
The MVGC Multivariate Granger Causality Toolbox: A New Approach to Granger-causal Inference
Lionel Barnett,Anil K. Seth +1 more
TL;DR: The theoretical basis, computational strategy and application to empirical G-causality inference of the MVGC Toolbox are explained and the advantages of the Toolbox over previous methods in terms of computational accuracy and statistical inference are shown.
References
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Journal ArticleDOI
Fractional Brownian Motions, Fractional Noises and Applications
Journal ArticleDOI
Spurious regressions in econometrics
Clive W. J. Granger,P. Newbold +1 more
TL;DR: In this paper, it is pointed out that it is very common to see reported in applied econometric literature time series regression equations with an apparently high degree of fit, as measured by the coefficient of multiple correlation R2 or the corrected coefficient R2, but with an extremely low value for the Durbin-Watson statistic.
Journal ArticleDOI
Long memory relationships and the aggregation of dynamic models
TL;DR: In this paper, it was shown that the aggregate series may have univariate long-memory models and obey integrated, or infinite length transfer function relationships, and that if series obeying such models occur in practice, from aggregation, then present techniques being used for analysis are not appropriate.
Journal ArticleDOI
Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon
A. I. McLeod,Keith W. Hipel +1 more
TL;DR: In this paper, the Akaike information criterion (AIC) is suggested as a method for choosing between a discrete fractional Gaussian noise (FGN) process and a Box-Jenkins model.
Journal ArticleDOI
A Fast Fractional Gaussian Noise Generator
TL;DR: The definition of fast fractional Gaussian noises, as sums of Markov-Gauss and other simple processes, fits the intuitive ideas that climate can be visualized as either unpredictably inhomogeneous or ruled by a hierarchy of variable regimes.