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首页> 外文期刊>European Journal of Business and Management >Volatility Of AMS Stock Market (Jordan), Through A Comparable models And Approaches (1996 – 2010)
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Volatility Of AMS Stock Market (Jordan), Through A Comparable models And Approaches (1996 – 2010)

机译:通过可比较的模型和方法(1996 - 2010),AMS股票市场(约旦)的波动性(1996 - 2010)

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This paper focuses on the performance of various Garch models, were Arch model s not dismissed in term of their ability of delivering volatility forecasts for Amman stock market return data , in this paper a stationary Garch models were estimated , I have assess the performance of the maximum likelihood estimator , finally I have attempt to fit the dynamic of daily Amman stock return , by different models and BL approach , also has been used quantified the day –of – the week effect and the ( ? ) leverage effect in order to test for asymmetric volatility. This paper attempt to investigate and modules the volatility of Amman stock market using daily observations as the day – of – a week return index for the period from January , 1996 through the period to June , 30 , 2010 , to achieve this purpose I have divided the period of study into two periods , then I have estimated the data by using Arch (1), Garch , E Garch , and the Go –Garch models are employed . Arch and Garch models are used to capture the symmetry effects, whereas the E-Garch are used to capturing the asymmetric effect. Results can be stated as : the E-Garch model is most fitted model to forecasting data of returns volatility between Garch (1,1) and Garch (1,2 ) as model performance is very small , according to BL approach Alpha of AMS portfolio and frontiers returns is ( - 0.6342 ) , and the risk ratio is ( 0.5331 ) .
机译:本文重点介绍各种GARCH模型的性能,是拱形模型,不被驳回他们提供波动率预测的能力,为安曼股票市场返回数据提供挥发性预测,在本文中,估计了一个固定的加油模型,我评估了表现最大可能性估计器,最后我试图符合日常安曼股票回报的动态,通过不同的型号和BL方法,也已使用量化的一天 - 本周的效果和(?)杠杆效果,以便测试不对称波动。本文试图调查和模组在2019年1月至6月30日至6月,2010年6月至6月的日期期间,使用日常观察的日常观察,为实现此目的,以实现这一目标,以实现这一目标,以达到每日返回指数。研究时间为两个时期,然后我通过使用拱(1),GARCH,E GARCH和GO -GARCH模型估计了数据。拱形和加粗模型用于捕获对称效果,而E-GARCH用于捕获不对称效果。结果可以说明:E-GARCH模型是最适合的模型,以预测GARCH(1,1)和GARCH(1,2)之间的返回波动率数据,因为AMS组合的BL方法是模型性能非常小。边界返回是( - 0.6342),风险比率为(0.5331)。

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