Application of one-dimension STS-distribution for modelling magnitudes of stock indexes [Electronic resource] / О. А. Belsner, О. L. Kritskiy

Уровень набора: (RuTPU)RU\TPU\book\169973, Bulletin of the Tomsk Polytechnic University / Tomsk Polytechnic University (TPU) = 2006-2007Основной Автор-лицо: Belsner, O. A., Mathematician, Senior Lecturer of Tomsk Polytechnic University, 1983-, Olga AlexandrovnaАльтернативный автор-лицо: Kritski, O. L., mathematician, Associate Professor of Tomsk Polytechnic University, Candidate of physical and mathematical sciences, 1976-, Oleg LeonidovichЯзык: английский ; оригинала, русский.Страна: Россия.Описание: 1 файл (489 Кб)Серия: Mathematics and mechanics. PhysicsРезюме или реферат: Modified method STS-GARCH(1,1) has been considered. Modification consisted in rejection of the statement on normal low of logarithm distribution of time series day increment and in their application for the description of Smoothly Truncated a-Stable (STS)-distribution (smoothly abridged a-stable). The method parameters were found by the technique of maximum likelihood. Statistic investigation of the suggested algorithm accuracy was carried out and decrease of autocorrelation in data structure used for the analysis was shown. The method was used to predict share prices of lag 5..Примечания о наличии в документе библиографии/указателя: [Bibliography: p. 47 (17 titles)].Тематика: one-dimension distribution | modelling | magnitudes | stock indexes | modified method | modification | low distribution | logarithm | day increments | parameters | technique of maximum likelihood | statistic investigation | algorithm | autocorrelation | data | prices | lags | электронный ресурс | труды учёных ТПУ Ресурсы он-лайн:Щелкните здесь для доступа в онлайн
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[Bibliography: p. 47 (17 titles)]

Modified method STS-GARCH(1,1) has been considered. Modification consisted in rejection of the statement on normal low of logarithm distribution of time series day increment and in their application for the description of Smoothly Truncated a-Stable (STS)-distribution (smoothly abridged a-stable). The method parameters were found by the technique of maximum likelihood. Statistic investigation of the suggested algorithm accuracy was carried out and decrease of autocorrelation in data structure used for the analysis was shown. The method was used to predict share prices of lag 5.

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