Let's get LADE: robust estimation of semiparametric multiplicative volatility models
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.
|Date of creation:||19 Mar 2013|
|Date of revision:|
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- VAN BELLEGEM, Sébastien, 2011. "Locally stationary volatility modelling," CORE Discussion Papers 2011041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Da Huang & Hansheng Wang & Qiwei Yao, 2008. "Estimating GARCH models: when to use what?," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 27-38, 03.
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