Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data

44 Pages Posted: 18 Sep 2008 Last revised: 28 Jun 2010

Dacheng Xiu

University of Chicago - Booth School of Business

Date Written: May 2010

Abstract

This paper investigates the properties of the well-known maximum likelihood estimator in the presence of stochastic volatility and market microstructure noise, by extending the classic asymptotic results of quasi-maximum likelihood estimation. When trying to estimate the integrated volatility and the variance of noise, this parametric approach remains consistent, efficient and robust as a quasi-estimator under misspecified assumptions. Moreover, it shares the model-free feature with nonparametric alternatives, for instance realized kernels, while being advantageous over them in terms of finite sample performance. In light of quadratic representation, this estimator behaves like an iterative exponential realized kernel asymptotically. Comparisons with a variety of implementations of the Tukey-Hanning 2 kernel are provided using Monte Carlo simulations, and an empirical study with the Euro/US Dollar future illustrates its application in practice.

Keywords: Integrated volatility, Market microstructure noise, Quasi-Maximum Likelihood Estimator, Realized Kernels, Stochastic volatility

JEL Classification: C13, C22, C51

Suggested Citation

Xiu, Dacheng, Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data (May 2010). Available at SSRN: https://ssrn.com/abstract=1269810 or http://dx.doi.org/10.2139/ssrn.1269810

Dacheng Xiu (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

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