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dc.contributor.authorSung Y. Parkzh_CN
dc.contributor.authorAnil K. Berazh_CN
dc.date.accessioned2013-11-08T08:21:27Z
dc.date.available2013-11-08T08:21:27Z
dc.date.issued2013-11-08
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/56881
dc.description.abstractIn many applications, it has been found that the autoregressive conditional heteroskedasticity (ARCH) model under the conditional normal or Student’s t distributions are not general enough to account for the excess kurtosis in the data. Moreover, asymmetry in the financial data is rarely modeled in a systematic way. In this paper, we suggest a general density function based on the maximum entropy (ME) approach that takes account of asymmetry, excess kurtosis and also of high peakedness. The ME principle is based on the efficient use of available information, and as is well known, many of the standard family of distributions can be derived from the ME approach. We demonstrate how we can extract information functional from the data in the form of moment functions. We also propose a test procedure for selecting appropriate moment functions. Our procedure is illustrated with an application to the NYSE stock returns. The empirical results reveal that the ME approach with a fewer moment functions leads to a model that captures the stylized facts quite effectively.zh_CN
dc.language.isozhzh_CN
dc.source.urihttp://www.wise.xmu.edu.cn/paperInfor.asp?id=137zh_CN
dc.subjectMaximum entropy densityzh_CN
dc.subjectARCH modelszh_CN
dc.subjectExcess kurtosiszh_CN
dc.subjectAsymmetryzh_CN
dc.subjectPeakedness of distributionzh_CN
dc.subjectStock returns datazh_CN
dc.titleMaximum entropy autoregressive conditional heteroskedasticity modelzh_CN
dc.typeArticlezh_CN
dc.description.noteThis paper was published in Journal of Econometrics 150(2009) 219–230zh_CN


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