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Bayesian analysis of DSARMA-GARCH models / (Record no. 78949)

MARC details
000 -LEADER
fixed length control field 02055cam a2200301 a 4500
003 - CONTROL NUMBER IDENTIFIER
control field EG-GiCUC
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201124s2020 ua d f m 000 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency EG-GiCUC
Language of cataloging eng
Transcribing agency EG-GiCUC
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title eng
049 ## - LOCAL HOLDINGS (OCLC)
Holding library Deposite
097 ## - Thesis Degree
Thesis Level Ph.D
099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC)
Classification number Cai01.03.01.Ph.D.2020.Em.B
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Eman Mahmoud Abdelmetaal Mohamed
245 10 - TITLE STATEMENT
Title Bayesian analysis of DSARMA-GARCH models /
Statement of responsibility, etc. Eman Mahmoud Abdelmetaal Mohamed ; Supervised Mohamed Ali Ismail
246 15 - VARYING FORM OF TITLE
Title proper/short title التحليل البيزى لنماذج أرما- جارش ذات الموسمية المزدوجة
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Cairo :
Name of publisher, distributor, etc. Eman Mahmoud Abdelmetaal Mohamed ,
Date of publication, distribution, etc. 2020
300 ## - PHYSICAL DESCRIPTION
Extent 87 P. :
Other physical details charts ;
Dimensions 25cm
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics
520 ## - SUMMARY, ETC.
Summary, etc. Multiple seasonal patterns are noticeable in time series data. Therefore, seasonal autoregressive moving average (SARMA) models have been recently extended to double SARMA (DSARMA) models. In this study, DSARMA models is extended to double seasonal autoregressive moving average- generalized autoregressive conditional heteroskedasticity (DSARMA-GARCH) in order not only to capture multiple seasonal patterns but also to take into account the volatility of the series at the same time. A Bayesian approach is used here to estimate these models. Although, DSARMA-GARCH models are non-linear in their coefficients, the Metropolis-Hastings (MH) algorithm is one of the most used Markov Chain Monte Carlo (MCMC) methods to overcome this problem.Therefore, the MH algorithm is used and investigated to provide Bayesian estimation of DSARMA-GARCH models. The obtained results demonstrate that this algorithm is suitable for Bayesian estimation of DSARMA-GARCH models
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Issued also as CD
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Hastings algorithm
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Metropolis
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Multiple seasonality
700 0# - ADDED ENTRY--PERSONAL NAME
Personal name Mohamed Ali Ismail ,
Relator term
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Nazla
Reviser Revisor
905 ## - LOCAL DATA ELEMENT E, LDE (RLIN)
Cataloger Shimaa
Reviser Cataloger
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Thesis
Holdings
Source of classification or shelving scheme Not for loan Home library Current library Date acquired Full call number Barcode Date last seen Koha item type Copy number
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة قاعة الرسائل الجامعية - الدور الاول 11.02.2024 Cai01.03.01.Ph.D.2020.Em.B 01010110082156000 22.09.2023 Thesis  
Dewey Decimal Classification   المكتبة المركزبة الجديدة - جامعة القاهرة مخـــزن الرســائل الجـــامعية - البدروم 11.02.2024 Cai01.03.01.Ph.D.2020.Em.B 01020110082156000 22.09.2023 CD - Rom 82156.CD