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

Tang Yinfen; Zhang Zhiyuan

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

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摘要

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.

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来源
《Applied stochastic models in business and industry》 |2019年第3期|603-623|共21页
作者
Tang Yinfen; Zhang Zhiyuan;
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作者单位

School of Statistics and ManagementShanghai University of Finance and EconomicsShanghai,China;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
combined filters; high‐frequency data; integrated volatility; market microstructure noise; price discreteness;

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

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