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A combined filtering approach to high‐frequency volatility estimation with mixed‐type microstructure noises
Author(s) -
Tang Yinfen,
Zhang Zhiyuan
Publication year - 2018
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2352
Subject(s) - volatility (finance) , kalman filter , econometrics , particle filter , market microstructure , stochastic volatility , estimation , computer science , economics , mathematics , artificial intelligence , finance , management , order (exchange)
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.