000			02055cam a2200301 a 4500
003			EG-GiCUC
008			201124s2020 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aPh.D
099			_aCai01.03.01.Ph.D.2020.Em.B
100	0		_aEman Mahmoud Abdelmetaal Mohamed
245	1	0	_aBayesian analysis of DSARMA-GARCH models / _cEman Mahmoud Abdelmetaal Mohamed ; Supervised Mohamed Ali Ismail
246	1	5	_aالتحليل البيزى لنماذج أرما- جارش ذات الموسمية المزدوجة
260			_aCairo : _bEman Mahmoud Abdelmetaal Mohamed , _c2020
300			_a87 P. : _bcharts ; _c25cm
502			_aThesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics
520			_aMultiple 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			_aIssued also as CD
653		4	_aHastings algorithm
653		4	_aMetropolis
653		4	_aMultiple seasonality
700	0		_aMohamed Ali Ismail , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c78949 _d78949