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Inference on common intraday periodicity at high frequencies

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  • Wu, Fan
  • Wang, Guan-jun
  • Kong, Xin-bing

Abstract

In this paper, we investigate the presence of common intraday periodicity of assets using functional data analysis. We implement the information criterion to select the number of common intraday periodic factors, and model the volatility part using high-frequency data.

Suggested Citation

  • Wu, Fan & Wang, Guan-jun & Kong, Xin-bing, 2022. "Inference on common intraday periodicity at high frequencies," Statistics & Probability Letters, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:stapro:v:191:y:2022:i:c:s0167715222001717
    DOI: 10.1016/j.spl.2022.109646
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    References listed on IDEAS

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    1. Jacod, Jean & Li, Yingying & Mykland, Per A. & Podolskij, Mark & Vetter, Mathias, 2009. "Microstructure noise in the continuous case: The pre-averaging approach," Stochastic Processes and their Applications, Elsevier, vol. 119(7), pages 2249-2276, July.
    2. Xin-Bing Kong, 2017. "On the number of common factors with high-frequency data," Biometrika, Biometrika Trust, vol. 104(2), pages 397-410.
    3. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    4. Ole E. Barndorff-Nielsen & Neil Shephard, 2006. "Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 1-30.
    5. Xiu, Dacheng, 2010. "Quasi-maximum likelihood estimation of volatility with high frequency data," Journal of Econometrics, Elsevier, vol. 159(1), pages 235-250, November.
    6. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    7. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    8. Cecilia Mancini, 2009. "Non‐parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 270-296, June.
    9. Brock, William A. & Kleidon, Allan W., 1992. "Periodic market closure and trading volume : A model of intraday bids and asks," Journal of Economic Dynamics and Control, Elsevier, vol. 16(3-4), pages 451-489.
    10. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
    11. Boudt, Kris & Croux, Christophe & Laurent, Sébastien, 2011. "Robust estimation of intraweek periodicity in volatility and jump detection," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 353-367, March.
    12. Torben G. Andersen & Martin Thyrsgaard & Viktor Todorov, 2019. "Time-Varying Periodicity in Intraday Volatility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1695-1707, October.
    13. Ouysse, Rachida, 2006. "Consistent variable selection in large panels when factors are observable," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 946-984, April.
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