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LET’S GET LADE: ROBUST ESTIMATION OF SEMIPARAMETRIC MULTIPLICATIVE VOLATILITY MODELS

Published online by Cambridge University Press:  05 November 2014

Bonsoo Koo and
Oliver Linton
Bonsoo Koo*
Affiliation:
Monash University
Oliver Linton
Affiliation:
University of Cambridge
*
*Address correspondence to Bonsoo Koo, Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria 3800, Australia; e-mail: bonsoo.koo@monash.edu.
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Abstract

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We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH(1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility is totally unspecified whereas the short-run conditional volatility is a parametric semi-strong GARCH(1,1) process. We propose different robust estimation methods for nonstationary and strictly stationary GARCH parameters with nonparametric long-run volatility function. Our estimation is based on a two-step LAD procedure. We establish the relevant asymptotic theory of the proposed estimators. Numerical results lend support to our theoretical results.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 4 , August 2015, pp. 671 - 702
Copyright
Copyright © Cambridge University Press 2014 

Footnotes

We are grateful to Wolfgang Hardle and anonymous referees for helpful comments. Linton gratefully acknowledges financial support from the ERC.

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