A combined filtering approach to high‐frequency volatility estimation with mixed‐type microstructure noises

Yinfen Tang and Zhiyuan Zhang

Applied Stochastic Models in Business and Industry, 2019, vol. 35, issue 3, 603-623

Abstract: This paper introduces a solution that combines the Kalman and particle filters to the challenging problem of estimating integrated volatility using high‐frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of how the proposed combined filtering approach is able to correct for bias due to this mixed‐type microstructure effect. Simulation and empirical studies on the tick‐by‐tick trade price data for four US stocks in the year 2009 show that our method has clear advantages over existing high‐frequency volatility estimation methods.

Date: 2019
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https://doi.org/10.1002/asmb.2352

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:35:y:2019:i:3:p:603-623

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