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  • 标题:Estimating Semiparametric ARCH (∞) Models by Kernel Smoothing Methods
  • 本地全文:下载
  • 作者:Oliver Linton ; Enno Mammen
  • 期刊名称:Japanese Studies Programme Papers
  • 出版年度:2003
  • 出版社:Suntory Toyota International Centres for Economics and Related Disciplines
  • 摘要:We investigate a class of semiparametric ARCH(∞) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible function form with regard to the 'news impact' function. We propose an estimation method that is based on kernel smoothing and profiled likelihood. We establish the distribution theory of the parametric components and the pointwise distribution of the nonparametric component of the model. We also discuss efficiency of both the parametric and nonparametric part. We investigate the performance of our procedures on simulated data and on a sample of S&P500 daily returns. We find some evidence of asymmetric news impact functions in the data
  • 关键词:ARCH; inverse problem; kernel estimation; news impact curve; ;nonparametric regression; profile likelihood; semiparametric estimation; volatility
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