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Let's get LADE: robust estimation of semiparametric multiplicative volatility models

Authors: Bonsoo Koo and Oliver Linton
Date: 19 March 2013
Type: cemmap Working Paper, CWP11/13
DOI: 10.1920/wp.cem.2013.1113

Abstract

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 semiparamentric 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 non-parametric 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.

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Now published:
Bonsoo Koo and Oliver Linton August 2015, Let's get LADE: robust estimation of semiparametric multiplicative volatility models, Journal Article, Cambridge University Press

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