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

Arbeitspapier

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 unspeci.ed 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP11/13

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Asset Pricing; Trading Volume; Bond Interest Rates
• Subject: semiparametric ; heavy-tailed errors ; time varying ; nonstationary multiplicative GARCH

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Koo, Bonsoo ; Linton, Oliver
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2013

Further information

• DOI: doi:10.1920/wp.cem.2013.1113
• Handle: http://hdl.handle.net/10419/79504
• Last update: 10.03.2025, 11:41 AM CET