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Estimating semiparametric ARCH models by kernel smoothing methods

Linton, Oliver and Mammen, Enno (2004) Estimating semiparametric ARCH models by kernel smoothing methods. Discussion paper, 511. Financial Markets Group, London School of Economics and Political Science, London, UK.

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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 show that the functional part of the model satisfies a type II linear integral equation and give simple conditions under which there is a unique solution. 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 index returns. We find evidence of asymmetric news impact functions, consistent with the parametric analysis.

Item Type: Monograph (Discussion Paper)
Official URL: http://fmg.lse.ac.uk
Additional Information: © 2004 The Authors
Library of Congress subject classification: H Social Sciences > HG Finance
H Social Sciences > HB Economic Theory
Sets: Research centres and groups > Financial Markets Group (FMG)
Collections > Economists Online
Collections > LSE Financial Markets Group (FMG) Working Papers
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Identification Number: 511
Date Deposited: 28 Aug 2009 09:11
URL: http://eprints.lse.ac.uk/24762/

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