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Estimation of the integrated volatility using noisy high-frequency data with jumps and endogeneity

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  • Cuixia Li
  • Erlin Guo

Abstract

In this paper, we investigate a new estimator of the integrated volatility of Itô semimartingales in the presence of both market microstructure noise and jumps when sampling times are endogenous. In the first step, our estimation wipes off the effects of the microstructure noise, and in the second step our estimator shrinks the effects of jumps. We provide consistency of the estimator when the jumps have finite variation and infinite variation and establish a central limit theorem for the estimator in a general endogenous time setting when the jumps only have finite variation. Simulation illustrates the performance of the proposed estimator.

Suggested Citation

  • Cuixia Li & Erlin Guo, 2018. "Estimation of the integrated volatility using noisy high-frequency data with jumps and endogeneity," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(3), pages 521-531, February.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:3:p:521-531
    DOI: 10.1080/03610926.2017.1307403
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    Cited by:

    1. Erlin Guo & Cuixia Li & Fengqin Tang, 2023. "The Convergence Rates of Large Volatility Matrix Estimator Based on Noise, Jumps, and Asynchronization," Mathematics, MDPI, vol. 11(6), pages 1-11, March.
    2. Dohyun Chun & Donggyu Kim, 2022. "State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 105-124, January.

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