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Trading Frequency and Volatility Clustering

Author

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  • Yi Xue

    (Department of Economics, Simon Fraser University)

  • Ramazan Gencay

    (Department of Economics, Simon Fraser University)

Abstract

Volatility clustering, with autocorrelations of the hyperbolic decay rate, is unquestionably one of the most important stylized facts of financial time series. This paper presents a market microstructure model, that is able to generate volatility clustering with hyperbolic autocorrelations through traders with multiple trading frequencies using Bayesian information updating in an incomplete market. The model illustrates that signal extraction, which is induced by multiple trading frequency, can increase the persistence of the volatility of returns. Furthermore, we show that the local temporal memory of the underlying time series of returns and their volatility varies greatly varies with the number of traders in the market.

Suggested Citation

  • Yi Xue & Ramazan Gencay, 2009. "Trading Frequency and Volatility Clustering," Working Paper series 31_09, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:31_09
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    Cited by:

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    2. Alessio Brini & Giacomo Toscano, 2024. "SpotV2Net: Multivariate Intraday Spot Volatility Forecasting via Vol-of-Vol-Informed Graph Attention Networks," Papers 2401.06249, arXiv.org.
    3. Lorraine Muguto & Paul-Francois Muzindutsi, 2022. "A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets," JRFM, MDPI, vol. 15(2), pages 1-27, February.
    4. Xue, Yi & Gençay, Ramazan, 2012. "Hierarchical information and the rate of information diffusion," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1372-1401.
    5. Nikitopoulos, Christina Sklibosios & Thomas, Alice Carole & Wang, Jianxin, 2023. "The economic impact of daily volatility persistence on energy markets," Journal of Commodity Markets, Elsevier, vol. 30(C).
    6. Borgards, Oliver & Czudaj, Robert L., 2021. "Features of overreactions in the cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 31-48.
    7. Wang, Jianxin, 2022. "Market distraction and near-zero daily volatility persistence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    8. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2015. "High frequency trading and end-of-day price dislocation," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 330-349.
    9. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
    10. Chen, Pei-Fen & Zeng, Jhih-Hong, 2014. "Asymmetric effects of households’ financial participation on banking diversification," Journal of Financial Stability, Elsevier, vol. 13(C), pages 18-29.
    11. Cao, Guangxi & Zhang, Minjia & Li, Qingchen, 2017. "Volatility-constrained multifractal detrended cross-correlation analysis: Cross-correlation among Mainland China, US, and Hong Kong stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 67-76.

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    More about this item

    Keywords

    Trading frequency; Volatility clustering; Signal extraction; Hyperbolic decay;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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