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**Fitting non-Gaussian persistent data.**
*(English)*
Zbl 1306.62202

Summary: This paper discusses a new methodology for modeling non-Gaussian time series with long-range dependence. The class of models proposed admits continuous or discrete data and considers the conditional variance as a function of the conditional mean. These types of models are motivated by empirical properties exhibited by some time series. The proposed methodology is illustrated with the analysis of two real-life persistent time series. The first application is concerned with the modeling of stock market daily trading volumes, whereas the second application consists of a study of mineral deposit measurements.

### MSC:

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |

62P05 | Applications of statistics to actuarial sciences and financial mathematics |

91B84 | Economic time series analysis |

62P12 | Applications of statistics to environmental and related topics |

86A32 | Geostatistics |

### Keywords:

ARFIMA models; conditional variance; long-range dependence; persistence; quasi-maximum likelihood; prediction
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\textit{W. Palma} and \textit{M. Zevallos}, Appl. Stoch. Models Bus. Ind. 27, No. 1, 23--36 (2011; Zbl 1306.62202)

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