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Bayesian analysis of DSARMA-GARCH models / Eman Mahmoud Abdelmetaal Mohamed ; Supervised Mohamed Ali Ismail

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Eman Mahmoud Abdelmetaal Mohamed , 2020Description: 87 P. : charts ; 25cmOther title:
  • التحليل البيزى لنماذج أرما- جارش ذات الموسمية المزدوجة [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics Summary: 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
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.Ph.D.2020.Em.B (Browse shelf(Opens below)) Not for loan 01010110082156000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.Ph.D.2020.Em.B (Browse shelf(Opens below)) 82156.CD Not for loan 01020110082156000

Thesis (Ph.D.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics

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

Issued also as CD

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