Fitting non‐Gaussian persistent data
Copyright policy )Fitting non‐Gaussian persistent data
AbstractThis 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. Copyright © 2010 John Wiley & Sons, Ltd.
Applications of statistics to actuarial sciences and financial mathematics, ARFIMA models, persistence, prediction, quasi-maximum likelihood, 08 Decent Work and Economic Growth, 08 Trabajo decente y crecimiento económico, Economic time series analysis, Time series, auto-correlation, regression, etc. in statistics (GARCH), ECONOMICS & BUSINESS, long-range dependence, Geostatistics, conditional variance, Applications of statistics to environmental and related topics
Applications of statistics to actuarial sciences and financial mathematics, ARFIMA models, persistence, prediction, quasi-maximum likelihood, 08 Decent Work and Economic Growth, 08 Trabajo decente y crecimiento económico, Economic time series analysis, Time series, auto-correlation, regression, etc. in statistics (GARCH), ECONOMICS & BUSINESS, long-range dependence, Geostatistics, conditional variance, Applications of statistics to environmental and related topics
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