Estimating semiparametric ARCH (∞) models by kernel smoothing methods

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

Text (DP511) - Published Version Download (946kB)

• https://www.fmg.ac.uk/publications/discu...

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.ac.uk
Additional Information:	© 2004 The Authors
Divisions:	Financial Markets Group
Subjects:	H Social Sciences > HG Finance H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
Date Deposited:	28 Aug 2009 09:11
Last Modified:	11 Dec 2024 18:39
URI:	http://eprints.lse.ac.uk/id/eprint/24762

Actions (login required)

View Item