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Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing

Author

Listed:
  • Charles S. Bos
  • Paweł Janus
  • Siem Jan Koopman

Abstract

This paper considers spot variance path estimation from datasets of intraday high-frequency asset prices in the presence of diurnal variance patterns, jumps, leverage effects, and microstructure noise. We rely on parametric and nonparametric methods. The estimated spot variance path can be used to extend an existing high-frequency jump test statistic, to detect arrival times of jumps, and to obtain distributional characteristics of detected jumps. The effectiveness of our approach is explored through Monte Carlo simulations. It is shown that sparse sampling for mitigating the impact of microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach, we can analyze high-frequency price observations that are contaminated with microstructure noise and rounding effects without the need for sparse sampling. An empirical illustration is presented for the intraday EUR/USD exchange rates. Our main finding is that fewer jumps are detected when sampling intervals increase. Copyright The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

Suggested Citation

  • Charles S. Bos & Paweł Janus & Siem Jan Koopman, 2012. "Spot Variance Path Estimation and Its Application to High-Frequency Jump Testing," Journal of Financial Econometrics, Oxford University Press, vol. 10(2), pages 354-389, 2012 06.
    • Handle: RePEc:oup:jfinec:v:10:y:2012:i:2:p:354-389
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbr013
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    Cited by:

    1. Lahaye, Jerome & Shaw, Philip, 2014. "Can we reject linearity in an HAR-RV model for the S&P 500? Insights from a nonparametric HAR-RV," Economics Letters, Elsevier, vol. 125(1), pages 43-46.
    2. Mustafayeva, Konul & Wang, Weining, 2020. "Non-Parametric Estimation of Spot Covariance Matrix with High-Frequency Data," IRTG 1792 Discussion Papers 2020-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Markus Bibinger & Nikolaus Hautsch & Peter Malec & Markus Reiss, 2019. "Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 419-435, July.
    4. Lee, Suzanne S. & Mykland, Per A., 2012. "Jumps in equilibrium prices and market microstructure noise," Journal of Econometrics, Elsevier, vol. 168(2), pages 396-406.
    5. Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
    6. Katahira, Kei & Chen, Yu & Hashimoto, Gaku & Okuda, Hiroshi, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 503-518.
    7. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2017. "Decoupling the short- and long-term behavior of stochastic volatility," CREATES Research Papers 2017-26, Department of Economics and Business Economics, Aarhus University.
    8. Boudt, Kris & Laurent, Sébastien & Lunde, Asger & Quaedvlieg, Rogier & Sauri, Orimar, 2017. "Positive semidefinite integrated covariance estimation, factorizations and asynchronicity," Journal of Econometrics, Elsevier, vol. 196(2), pages 347-367.
    9. Zu, Yang & Peter Boswijk, H., 2014. "Estimating spot volatility with high-frequency financial data," Journal of Econometrics, Elsevier, vol. 181(2), pages 117-135.
    10. Yucheng Sun, 2024. "Testing for jumps with robust spot volatility estimators," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(1), pages 79-104, February.
    11. repec:hum:wpaper:sfb649dp2014-055 is not listed on IDEAS
    12. Boudt, Kris & Petitjean, Mikael, 2014. "Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks," Journal of Financial Markets, Elsevier, vol. 17(C), pages 121-149.
    13. Mikkel Bennedsen & Asger Lunde & Mikko S. Pakkanen, 2016. "Decoupling the short- and long-term behavior of stochastic volatility," Papers 1610.00332, arXiv.org, revised Jan 2021.
    14. Charles S. Bos & Pawel Janus, 2013. "A Quantile-based Realized Measure of Variation: New Tests for Outlying Observations in Financial Data," Tinbergen Institute Discussion Papers 13-155/III, Tinbergen Institute.

    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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