Constructing an information matrix for multivariate DCC-MGARCH(1,1) method; ARPN Journal of Engineering and Applied Sciences; Vol. 13, № 8

Dades bibliogràfiques
Parent link:ARPN Journal of Engineering and Applied Sciences
Vol. 13, № 8.— 2018.— [P. 2838-2845]
Autor principal: Maleeva E. A. Ekaterina Aleksandrovna
Autor corporatiu: Национальный исследовательский Томский политехнический университет Инженерная школа ядерных технологий Отделение экспериментальной физики
Altres autors: Kritski O. L. Oleg Leonidovich, Amini M. H. M. Mohd Hazim Mohamad
Sumari:Title screen
The analytic form of Fisher Information Matrix (IM) for DCC-MGARCH (1, 1) was suggested. After that, it was applied for simplifying the general algorithm: the statistical hypothesis about constant correlation matrix usage was put forward and statistical verification was made. IM was employed for Russian share market: to do investigations the five equilibrium portfolios was compounded from four different shares in each case. Computations made showed that there are three types T1–T3 of trading days on the market and day type changing from T1 to T2 and vice versa is happening over the time moments T3. Moreover, the clustarisation effect of multivariate volatility that was investigated by scientists from all around the world in the univariate case was discovered and described.
Idioma:anglès
Publicat: 2018
Matèries:
Accés en línia:http://umkeprints.umk.edu.my/id/eprint/10487
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666919

MARC

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300 |a Title screen 
320 |a [References: 20 tit.] 
330 |a The analytic form of Fisher Information Matrix (IM) for DCC-MGARCH (1, 1) was suggested. After that, it was applied for simplifying the general algorithm: the statistical hypothesis about constant correlation matrix usage was put forward and statistical verification was made. IM was employed for Russian share market: to do investigations the five equilibrium portfolios was compounded from four different shares in each case. Computations made showed that there are three types T1–T3 of trading days on the market and day type changing from T1 to T2 and vice versa is happening over the time moments T3. Moreover, the clustarisation effect of multivariate volatility that was investigated by scientists from all around the world in the univariate case was discovered and described. 
461 |t ARPN Journal of Engineering and Applied Sciences 
463 |t Vol. 13, № 8  |v [P. 2838-2845]  |d 2018 
610 1 |a электронный ресурс 
610 1 |a труды учёных ТПУ 
610 1 |a fisher matrix 
610 1 |a multivariate conditional dynamic correlation DCC-MGARCH method 
610 1 |a матрица Фишера 
610 1 |a многомерные методы 
700 1 |a Maleeva  |b E. A.  |g Ekaterina Aleksandrovna 
701 1 |a Kritski  |b O. L.  |c mathematician  |c Associate Professor of Tomsk Polytechnic University, Candidate of physical and mathematical sciences  |f 1976-  |g Oleg Leonidovich  |3 (RuTPU)RU\TPU\pers\31888  |9 15960 
701 1 |a Amini  |b M. H. M.  |g Mohd Hazim Mohamad 
712 0 2 |a Национальный исследовательский Томский политехнический университет  |b Инженерная школа ядерных технологий  |b Отделение экспериментальной физики  |3 (RuTPU)RU\TPU\col\23549 
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