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文章基本信息

  • 标题:Semi- and Nonparametric ARCH Processes
  • 本地全文:下载
  • 作者:Oliver B. Linton ; Yang Yan
  • 期刊名称:Journal of Probability and Statistics
  • 印刷版ISSN:1687-952X
  • 电子版ISSN:1687-9538
  • 出版年度:2011
  • 卷号:2011
  • DOI:10.1155/2011/906212
  • 出版社:Hindawi Publishing Corporation
  • 摘要:ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
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