Garch Parameter Estimation Using High-Frequency Data

Marcel Visser

MPRA Paper from University Library of Munich, Germany

Abstract: Estimation of the parameters of Garch models for financial data is typically based on daily close-to-close returns. This paper shows that the efficiency of the parameter estimators may be greatly improved by using volatility proxies based on intraday data. The paper develops a Garch quasi maximum likelihood estimator (QMLE) based on these proxies. Examples of such proxies are the realized volatility and the intraday high-low range. Empirical analysis of the S&P 500 index tick data shows that the use of a suitable proxy may reduce the variances of the estimators of the Garch autoregression parameters by a factor 20.

Keywords: volatility estimation; quasi maximum likelihood; volatility proxy; Gaussian QMLE; log-Gaussian QMLE; autoregressive conditional heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 G1 (search for similar items in EconPapers)
Date: 2008-06-10
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: GARCH Parameter Estimation Using High-Frequency Data (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:9076

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