@unpublished{mpra9076,
           month = {June},
            year = {2008},
           title = {Garch Parameter Estimation Using High-Frequency Data},
            note = {Unpublished},
        keywords = {volatility estimation; quasi maximum likelihood; volatility proxy; Gaussian QMLE; log-Gaussian QMLE; autoregressive conditional heteroscedasticity},
          author = {Visser, Marcel P.},
        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.},
             url = {https://mpra.ub.uni-muenchen.de/9076/}
}