Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data
University of Chicago - Booth School of Business
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.
Number of Pages in PDF File: 44
Keywords: Integrated volatility, Market microstructure noise, Quasi-Maximum Likelihood Estimator, Realized Kernels, Stochastic volatility
JEL Classification: C13, C22, C51
Date posted: September 18, 2008 ; Last revised: June 28, 2010
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