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Surprising information, the MDH, and the relationship between volatility and trading volume

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  • Park, Beum-Jo

Abstract

This paper explains the concept of surprising information with a sign effect. Employing the mixture of distribution hypothesis (MDH), this paper also theoretically demonstrates that the effect of surprising information on the relationship between volatility and trading volume contrasts with that of general information, and proposes a method to detect the unobservable surprising information. Furthermore, incorporating surprising information with a sign effect, this paper suggests an information-type switching GARCH-V model. Strong evidence in favor of the model specification over the standard GARCH models is based on empirical application with high frequency data, supporting the dependence of the relationship between volatility and trading volume on the type of information.

Suggested Citation

  • Park, Beum-Jo, 2010. "Surprising information, the MDH, and the relationship between volatility and trading volume," Journal of Financial Markets, Elsevier, vol. 13(3), pages 344-366, August.
    • Handle: RePEc:eee:finmar:v:13:y:2010:i:3:p:344-366
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    6. Olivier Damette & Stéphane Goutte, 2014. "Tobin tax and trading volume tightening: a reassessment," Working Papers halshs-00926805, HAL.
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    9. Beata Szetela & Grzegorz Mentel & Yuriy Bilan & Urszula Mentel, 2021. "The relationship between trend and volume on the bitcoin market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 25-42, March.
    10. Park, Beum-Jo, 2022. "The COVID-19 pandemic, volatility, and trading behavior in the bitcoin futures market," Research in International Business and Finance, Elsevier, vol. 59(C).
    11. Beum-Jo Park, 2011. "Forecasting Volatility in Financial Markets Using a Bivariate Stochastic Volatility Model with Surprising Information," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 37-58, September.
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    13. Olivier Damette & St鰨ane Goutte, 2015. "Tobin tax and trading volume tightening: a reassessment," Applied Economics, Taylor & Francis Journals, vol. 47(29), pages 3124-3141, June.
    14. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
    15. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
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    17. Liu, Li & Pan, Zhiyuan, 2020. "Forecasting stock market volatility: The role of technical variables," Economic Modelling, Elsevier, vol. 84(C), pages 55-65.
    18. Pengfei Wang & Wei Zhang & Xiao Li & Dehua Shen, 2019. "Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 377-418, June.
    19. Park, Beum-Jo, 2011. "Asymmetric herding as a source of asymmetric return volatility," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2657-2665, October.
    20. Kao, Yu-Sheng & Chuang, Hwei-Lin & Ku, Yu-Cheng, 2020. "The empirical linkages among market returns, return volatility, and trading volume: Evidence from the S&P 500 VIX Futures," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
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