Finite Sample Accuracy Of Integrated Volatility Estimators
Morten Nielsen () and
Per Houmann Frederiksen
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Per Houmann Frederiksen: Nordea Markets
No 1225, Working Paper from Economics Department, Queen's University
We consider the properties of three estimation methods for integrated volatility, i.e. realized volatility, the Fourier estimator, and the wavelet estimator, when a typical sample of high-frequency data is observed. We employ several different generating mechanisms for the instantaneous volatility process, e.g. Ornstein-Uhlenbeck, long memory, and jump processes. The possibility of market microstructure contamination is also entertained using a model with bid-ask bounce in which case alternative estimators with theoretical justification under market microstructure noise are also examined. The estimation methods are compared in a simulation study which reveals a general robustness towards persistence or jumps in the latent stochastic volatility process. However, bid-ask bounce effects render realized volatility and especially the wavelet estimator less useful in practice, whereas the Fourier method remains useful and is superior to the other two estimators in that case. More strikingly, even compared to bias correction methods for microstructure noise, the Fourier method is superior with respect to RMSE while having only slightly higher bias.
Keywords: Bid-ask bounce; finite sample bias; integrated volatility; long memory; market microstructure; Monte Carlo simulation; realized volatility; wavelet (search for similar items in EconPapers)
JEL-codes: C15 C22 (search for similar items in EconPapers)
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Journal Article: Finite sample accuracy and choice of sampling frequency in integrated volatility estimation (2008)
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