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An Empirical Analysis of Volatility by the SIML Estimation with High-Frequency Trades and Quotes

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Intelligent Decision Technologies 2018 (KES-IDT 2018 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 97))

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Abstract

Estimating the volatility of financial asset prices is an issue of considerable importance in financial econometrics research. For estimating the integrated volatility by using high frequency data, Kunitomo and Sato [6] proposed the separating information maximum likelihood (SIML) method for estimating volatility from data contaminated by market microstructure noise. Subsequently, Misaki and Kunitomo [10] investigated the method when used with randomly sampled data. The method has not been tested for estimating the volatility of individual stocks with high-frequency data. This article analyzes tick-by-tick prices data to compare daily volatility estimates calculated with SIML estimation with estimates delivered by conventional methods. We first test the method with transaction prices from several firms and then with quote and transaction prices to investigate its robustness against noise. Our findings suggest that SIML estimation is useful for analyzing actual markets with high-frequency data.

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Notes

  1. 1.

    Now both markets are integrated under the Japan Exchange Group, Inc.

References

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Acknowledgements

I would like to thank the editors and chairs for the invitation to KES-IDT-18. I also thank two anonymous reviewers for useful comments and recommendations that improved this manuscript. This research is supported by Grant for Social Science from Nomura Foundation.

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Authors and Affiliations

  1. University of Tsukuba, Tennodai 1-1 -1, Tsukuba, Ibaraki, 305-8577, Japan

    Hiroumi Misaki

Authors
  1. Hiroumi Misaki
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Correspondence to Hiroumi Misaki .

Editor information

Editors and Affiliations

  1. Gdynia Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University, Poole, United Kingdom

    Robert J. Howlett

  3. Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia

    Lakhmi C. Jain

  4. Griffith Sciences - Centres and Institutes, Griffith University, South Brisbane, Queensland, Australia

    Ljubo Vlacic

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Misaki, H. (2019). An Empirical Analysis of Volatility by the SIML Estimation with High-Frequency Trades and Quotes. In: Czarnowski, I., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Decision Technologies 2018. KES-IDT 2018 2018. Smart Innovation, Systems and Technologies, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-92028-3_7

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