Estimating semiparametric ARCH (∞) models by kernel smoothing methods

Oliver Linton and Enno Mammen

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: 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.

Keywords: ARCH; inverse problem; kernel estimation; news impact curve; nonparametric regression; profile likelihood; semiparametric estimation; volatility (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Pages: 57 pages
Date: 2003-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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http://eprints.lse.ac.uk/58068/ Open access version. (application/pdf)

Related works:
Journal Article: Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:58068

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