Estimating Semiparametric Arch (∞) Models by Kernel Smoothing Methods

62 Pages Posted: 21 Jul 2008

See all articles by Oliver B. Linton

Oliver B. Linton

University of Cambridge

Enno Mammen

University of Mannheim - Department of Economics

Date Written: May 2003

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.

JEL Classification: C16, C53, G12

Suggested Citation

Linton, Oliver B. and Mammen, Enno, Estimating Semiparametric Arch (∞) Models by Kernel Smoothing Methods (May 2003). LSE STICERD Research Paper No. EM453, Available at SSRN: https://ssrn.com/abstract=1162618

Oliver B. Linton (Contact Author)

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

Enno Mammen

University of Mannheim - Department of Economics ( email )

Mannheim, 68131
Germany

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