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How well does the weighted price contribution measure price discovery?

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  • Wang, Jianxin
  • Yang, Minxian

Abstract

The weighted price contribution (WPC) is a popular measure for price discovery. This paper examines the theoretical properties and empirical performance of the WPC in sequential markets. The benchmark used to judge the WPC is the information share (IS) measure based on the variation of the efficient price. We derive the asymptotic value of the WPC, which is a complex combination of the unconditional means and variances of the returns of sequential markets, under the assumption of normality. We show that the WPC correctly converges to the IS only when the returns are uncorrelated with zero means. Our theoretical predictions based on normality hold well in simulations and in empirical analyses of the overnight price discovery for the S&P 100 index and its constituent stocks. As the correlation between overnight and daytime returns increases, the WPC deviates from the IS substantially.

Suggested Citation

  • Wang, Jianxin & Yang, Minxian, 2015. "How well does the weighted price contribution measure price discovery?," Journal of Economic Dynamics and Control, Elsevier, vol. 55(C), pages 113-129.
    • Handle: RePEc:eee:dyncon:v:55:y:2015:i:c:p:113-129
    DOI: 10.1016/j.jedc.2015.04.002
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    2. Wang, Jianxin, 2014. "Overnight price discovery and the internationalization of a currency: The case of the Korean won," Pacific-Basin Finance Journal, Elsevier, vol. 29(C), pages 86-95.
    3. Hou, Yang & Li, Steven, 2017. "Time-Varying Price Discovery and Autoregressive Loading Factors: Evidence from S&P 500 Cash and E-Mini Futures Markets," MPRA Paper 81999, University Library of Munich, Germany.
    4. Chai, Edwina F.L. & Lee, Adrian D. & Wang, Jianxin, 2015. "Global information distribution in the gold OTC markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 206-217.
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    6. Abad, David & Pascual, Roberto, 2015. "The friction-free weighted price contribution," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 226-239.
    7. Lien, Donald & Hung, Pi-Hsia & Lin, Zong-Wei, 2020. "Whose trades move stock prices? Evidence from the Taiwan Stock Exchange," International Review of Economics & Finance, Elsevier, vol. 66(C), pages 25-50.
    8. Yang Hou & Steven Li & Fenghua Wen, 2021. "Time-varying information share and autoregressive loading factors: evidence from S&P 500 cash and E-mini futures markets," Review of Quantitative Finance and Accounting, Springer, vol. 57(1), pages 91-110, July.
    9. Dimpfl, Thomas & Schweikert, Karsten, 2023. "Information shares for markets with partially overlapping trading hours," Journal of Banking & Finance, Elsevier, vol. 154(C).
    10. Muzhao Jin & Youwei Li & Jianxin Wang & Yung Chiang Yang, 2018. "Price discovery in the Chinese gold market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1262-1281, October.
    11. Gau, Yin-Feng & Wu, Zhen-Xing, 2017. "Macroeconomic announcements and price discovery in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 232-254.

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    More about this item

    Keywords

    Price discovery; Weighted price contribution; Information share; Information flow; Efficient price; Overnight return;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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