Estimating stochastic volatility diffusion using conditional moments of integrated volatility
Abstract
We exploit the distributional information contained in high-frequency intraday data
in constructing a simple conditional moment estimator for stochastic volatility diffusions.
The estimator is based on the analytical solutions of the first two conditional moments
for the latent integrated volatility, the realization of which is effectively approximated
by the sum of the squared high-frequency increments of the process. Our simulation
evidence indicates that the resulting GMM estimator is highly reliable and accurate.
Our empirical implementation based on high-frequency five-minute foreign exchange
returns suggests the presence of multiple latent stochastic volatility factors and
possible jumps. © 2002 Elsevier Science B.V. All rights reserved.
Type
Journal articlePermalink
https://hdl.handle.net/10161/1893Published Version (Please cite this version)
10.1016/S0304-4076(01)00141-5Publication Info
Bollerslev, T; & Zhou, H (2002). Estimating stochastic volatility diffusion using conditional moments of integrated
volatility. Journal of Econometrics, 109(1). pp. 33-65. 10.1016/S0304-4076(01)00141-5. Retrieved from https://hdl.handle.net/10161/1893.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Tim Bollerslev
Juanita and Clifton Kreps Distinguished Professor of Economics, in Trinity College
of Arts and Sciences
Professor Bollerslev conducts research in the areas of time-series econometrics, financial
econometrics, and empirical asset pricing finance. He is particularly well known
for his developments of econometric models and procedures for analyzing and forecasting
financial market volatility. Much of Bollerslev’s recent research has focused on
the analysis of newly available high-frequency intraday, or tick-by-tick, financial
data and so-called realized volatility measures, macroeconomic news annou

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