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On the estimation of integrated volatility in the presence of jumps and microstructure noise

Author

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  • Christian Brownlees
  • Eulalia Nualart
  • Yucheng Sun

Abstract

This paper is concerned with the problem of the estimation of the integrated volatility of log-prices based on high frequency data when both price jumps and market microstructure noise are present. We begin by providing a survey of the leading estimators introduced in the literature to tackle volatility estimation in this setting. We then introduce novel integrated volatility estimators based on a truncation technique and establish their properties. Finally, we carry out a simulation study to compare the performance of the different estimation techniques.

Suggested Citation

  • Christian Brownlees & Eulalia Nualart & Yucheng Sun, 2020. "On the estimation of integrated volatility in the presence of jumps and microstructure noise," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 991-1013, November.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:10:p:991-1013
    DOI: 10.1080/07474938.2020.1735751
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    Cited by:

    1. Hassan Zada & Huma Maqsood & Shakeel Ahmed & Muhammad Zeb Khan, 2023. "Information shocks, market returns and volatility: a comparative analysis of developed equity markets in Asia," SN Business & Economics, Springer, vol. 3(1), pages 1-22, January.
    2. Qiang Liu & Zhi Liu, 2022. "Estimating spot volatility under infinite variation jumps with dependent market microstructure noise," Papers 2205.15738, arXiv.org, revised Feb 2023.
    3. Hassan Zada & Arshad Hassan & Wing-Keung Wong, 2021. "Do Jumps Matter in Both Equity Market Returns and Integrated Volatility: A Comparison of Asian Developed and Emerging Markets," Economies, MDPI, vol. 9(2), pages 1-26, June.
    4. Manh Cuong Dong & Cathy W. S. Chen & Manabu Asai, 2023. "Bayesian non‐linear quantile effects on modelling realized kernels," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 981-995, January.
    5. Valentin Courgeau & Almut E. D. Veraart, 2022. "Likelihood theory for the graph Ornstein-Uhlenbeck process," Statistical Inference for Stochastic Processes, Springer, vol. 25(2), pages 227-260, July.

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