Article2010

GARCH Parameter Estimation Using High-Frequency Data

55 citations0/0 files storedJournal of Financial Econometrics

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

Abstract

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: journals.permissions@oxfordjournals.org, Oxford University Press.

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References

1986Generalized autoregressive conditional heteroskedasticity 2003Modeling and Forecasting Realized Volatility 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 1992Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances 2001The distribution of realized stock return volatility 2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models 1998Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data 2005A forecast comparison of volatility models: does anything beat a GARCH(1,1)?

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2022Deep learning for volatility forecasting in asset management 2010Realized GARCH: A Joint Model of Returns and Realized Measures of Volatility 2012Financial Risk Measurement for Financial Risk Management 2011Common Intraday Periodicity

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