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GARCH Parameter Estimation Using High-Frequency Data

  • Marcel P. Visser

A standard procedure for obtaining parameter values of a GARCH model for financial volatility is the quasi maximum likelihood estimator (QMLE) based on daily close-to-close returns. This paper generalizes the QMLE based on daily returns to a QMLE based on intraday high-frequency data. Volatility proxies, such as the realized volatility or the daily high--low range, are used for estimating the parameters of discrete-time GARCH models. Empirical analysis of the S&P 500 index tick data shows that a well-chosen proxy may reduce the variances of the estimators of the GARCH(1,1) autoregression parameters by a factor 20. C14, C22, C51, G1 Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:, Oxford University Press.

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Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 9 (2011)
Issue (Month): 1 (Winter)
Pages: 162-197

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Handle: RePEc:oup:jfinec:v:9:y:2011:i:1:p:162-197
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