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

Bonsoo Koo and Oliver Linton
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Bonsoo Koo: Institute for Fiscal Studies

No CWP11/13, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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.

Date: 2013-03-19
New Economics Papers: this item is included in nep-ets and nep-mst
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Related works:
Journal Article: LET’S GET LADE: ROBUST ESTIMATION OF SEMIPARAMETRIC MULTIPLICATIVE VOLATILITY MODELS (2015)
Working Paper: Let's get LADE: robust estimation of semiparametric multiplicative volatility models (2013)
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