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- W2111774431 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." @default.
- W2111774431 created "2016-06-24" @default.
- W2111774431 creator A5050253426 @default.
- W2111774431 date "2010-01-01" @default.
- W2111774431 modified "2023-09-27" @default.
- W2111774431 title "Quasi-Maximum Likelihood Estimation of Volatility with High Frequency Data" @default.
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- W2111774431 doi "https://doi.org/10.2139/ssrn.1269810" @default.
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