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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...
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