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Online Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models

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  • Rainer Dahlhaus
  • Jan C. Neddermeyer

Abstract

A technique for online estimation of spot volatility for high-frequency data is developed. The algorithm works directly on the transaction data and updates the volatility estimate immediately after the occurrence of a new transaction. Furthermore, a nonlinear market microstructure noise model is proposed that reproduces several stylized facts of high-frequency data. A computationally efficient particle filter is used that allows for the approximation of the unknown efficient prices and, in combination with a recursive EM algorithm, for the estimation of the volatility curve. We neither assume that the transaction times are equidistant nor do we use interpolated prices. We also make a distinction between volatility per time unit and volatility per transaction and provide estimators for both. More precisely we use a model with random time change where spot volatility is decomposed into spot volatility per transaction times the trading intensity--thus highlighting the influence of trading intensity on volatility. Copyright The Author, 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com, Oxford University Press.

Suggested Citation

  • Rainer Dahlhaus & Jan C. Neddermeyer, 2013. "Online Spot Volatility-Estimation and Decomposition with Nonlinear Market Microstructure Noise Models," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 174-212, December.
    • Handle: RePEc:oup:jfinec:v:12:y:2013:i:1:p:174-212
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbt008
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    Cited by:

    1. Timo Dimitriadis & Roxana Halbleib & Jeannine Polivka & Jasper Rennspies & Sina Streicher & Axel Friedrich Wolter, 2022. "Efficient Sampling for Realized Variance Estimation in Time-Changed Diffusion Models," Papers 2212.11833, arXiv.org, revised Dec 2023.
    2. Yoann Potiron & Per Mykland, 2020. "Local Parametric Estimation in High Frequency Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 679-692, July.
    3. Vladimír Holý & Petra Tomanová, 2023. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 463-485, June.
    4. István Barra & Agnieszka Borowska & Siem Jan Koopman, 2018. "Bayesian Dynamic Modeling of High-Frequency Integer Price Changes," Journal of Financial Econometrics, Oxford University Press, vol. 16(3), pages 384-424.
    5. Vladim'ir Hol'y & Petra Tomanov'a, 2020. "Streaming Approach to Quadratic Covariation Estimation Using Financial Ultra-High-Frequency Data," Papers 2003.13062, arXiv.org, revised Dec 2021.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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