Quasi-Maximum Likelihood Estimation of Volatility with High-Frequency Data

• Activity Number: 604
• Type: Invited
• Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Dacheng Xiu*+
• Companies: Princeton University
• Keywords: Integrated volatility ; Market microstructure noise ; Quasi-Maximum Likelihood Estimator ; Realized Kernels ; Stochastic volatility

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. 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.