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Time Series, Conditional Heteroscedasticity, and GARCH Models
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报告题目:Time Series, Conditional Heteroscedasticity, and GARCH Models
     报告人:Saeid Rezakhah,副教授,阿米尔卡比尔理工大学(德黑兰)
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邀请人:董从造
    报告时间:2019年03月13日(周三)下午14:00-15:00
    报告地点:信远楼II206数统院报告厅
    报告人简介:
Saeid Rezakhah 是伊朗德黑兰阿米尔卡比尔理工大学副教授(stage 32),博士生导师,1996年取得英国伦敦大学玛丽皇后和韦斯特菲尔德学院(Queen Mary and Westfield College, University of London)概率统计博士学位,先后在美国密歇根州立大学和英国伦敦大学做访问教授。Saeid Rezakhah教授研究兴趣包括Selfsimilar Process; Hidden Markov Mixture models, Periodically Correlated Processes, Stable distributions, Random Polynomials; Time-Series Analysis; Stable Process和 Information Theory等等,目前在国际学术期刊发表sci检索论文30余篇。
    报告摘要:In this talk first I present a brief explanation of the difference between  Random Samples in usual statistics fields and time series, which I call one hundred percent differences. This gives the audience good understanding to distinguish such samples and have better undrestanding of their applications. Then I give another description regarding Stationary Time Series, which are dealt with time series models that are of constant variance and there is no volatility and also their properties are classified as homoscedasticity. I also describe short memory and long memory effects modelling in such time series. Then the heteroscedasticity  time series are described and their applications in modelling financial time series and natural phenomena are discussed.  Finally the ARCH and GARCH models for modelling returns and log returns in financial time series are described and their corresponding models are introduced.

    主办单位:数学与统计学院

 

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