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Intraday dynamics of volatility and duration: Evidence from Chinese stocks

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  • Liu, Chun
  • Maheu, John M.

Abstract

We propose a new joint model of intraday returns and durations to study the dynamics of several Chinese stocks. We include three U.S. stocks for comparison. Flexible innovation distributions are used for durations and returns, and the total variance of returns is decomposed into different volatility components associated with different transaction horizons. The new model provides strong improvements in density forecasts for duration and returns and only modest gains for points forecasts of the variance of returns. The conditional hazard functions are non-monotonic and there is strong evidence for different volatility components. Although diurnal patterns, volatility components, and market microstructure implications are similar across the markets, there are interesting differences. Durations for lightly traded Chinese stocks tend to carry more information than heavily traded stocks. Chinese investors usually have longer investment horizons, which may be explained by the specific trading rules in China.

Suggested Citation

  • Liu, Chun & Maheu, John M., 2012. "Intraday dynamics of volatility and duration: Evidence from Chinese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 20(3), pages 329-348.
  • Handle: RePEc:eee:pacfin:v:20:y:2012:i:3:p:329-348
    DOI: 10.1016/j.pacfin.2011.11.001
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    2. Karaa, Rabaa & Slim, Skander & Hmaied, Dorra Mezzez, 2018. "Trading intensity and the volume-volatility relationship on the Tunis Stock Exchange," Research in International Business and Finance, Elsevier, vol. 44(C), pages 88-99.
    3. Roman Huptas, 2016. "The UHF-GARCH-Type Model in the Analysis of Intraday Volatility and Price Durations – the Bayesian Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 1-20, March.
    4. Doojin Ryu, 2017. "Comprehensive market microstructure model: considering the inventory holding costs," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 183-201, March.
    5. Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020. "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper 103250, University Library of Munich, Germany, revised 01 Oct 2020.
    6. Doojin Ryu, 2013. "Spread and depth adjustment process: an analysis of high-quality microstructure data," Applied Economics Letters, Taylor & Francis Journals, vol. 20(16), pages 1506-1510, November.
    7. Vladim'ir Hol'y, 2022. "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations," Papers 2211.12376, arXiv.org, revised Sep 2023.
    8. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    9. Maria Čuljak & Josip Arnerić & Ante Žigman, 2022. "Is Jump Robust Two Times Scaled Estimator Superior among Realized Volatility Competitors?," Mathematics, MDPI, vol. 10(12), pages 1-11, June.
    10. Thomas Dimpfl & Stefania Odelli, 2020. "Bitcoin Price Risk—A Durations Perspective," JRFM, MDPI, vol. 13(7), pages 1-18, July.
    11. Doojin Ryu, 2015. "Information content of inter-transaction time: A structural approach," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(4), pages 697-711, August.
    12. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Market microstructure; Transaction horizon; High-frequency data; ACD; GARCH;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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