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Estimation of Market Information Shares: A Comparison

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  • Donald Lien
  • Zijun Wang

Abstract

This note investigates via Monte Carlo simulation the finite‐sample performance of two identification schemes that provide unique measures of Hasbrouck‐type information share in price discovery. The Lien and Shrestha (2009) method is based on factorization of the full correlation matrix and the Grammig and Peter (2013) method is based on different correlations of price innovations in the tails and in the center of the distributions. We find that the GP method performs poorly under the chosen data generation processes. The LS method provides at most marginal improvement over the method based upon the upper/lower bound midpoint of the Hasbrouck measure. The results, therefore, support the common practice of the midpoint approach. © 2016 Wiley Periodicals, Inc. Jrl Fut Mark 36:1108–1124, 2016

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  • Donald Lien & Zijun Wang, 2016. "Estimation of Market Information Shares: A Comparison," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(11), pages 1108-1124, November.
    • Handle: RePEc:wly:jfutmk:v:36:y:2016:i:11:p:1108-1124
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    Cited by:

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    2. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid-Ask Spread?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(5), pages 1482-1498.
    3. Ziliang Yu & Jian Yang & Robert I. Webb, 2023. "Price discovery in China's crude oil futures markets: An emerging Asian benchmark?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(3), pages 297-324, March.
    4. Li, Hong & Shi, Yanlin, 2021. "A new unique information share measure with applications on cross-listed Chinese banks," Journal of Banking & Finance, Elsevier, vol. 128(C).
    5. Kuck, Konstantin & Schweikert, Karsten, 2023. "Price discovery in equity markets: A state-dependent analysis of spot and futures markets," Journal of Banking & Finance, Elsevier, vol. 149(C).
    6. Mehdi Arzandeh & Julieta Frank, 2019. "Price Discovery in Agricultural Futures Markets: Should We Look beyond the Best Bid‐Ask Spread?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 101(5), pages 1482-1498, October.
    7. Hong Li & Yanlin Shi, 2022. "Robust information share measures with an application on the international crude oil markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 555-579, April.
    8. Michael Funke & Julius Loermann & Andrew Tsang, 2022. "Volatility transmission and volatility impulse response functions in the main and the satellite Renminbi exchange rate markets," Review of International Economics, Wiley Blackwell, vol. 30(2), pages 606-628, May.
    9. 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.
    10. Arzandeh, Mehdi & Frank, Julieta, 2017. "The Information Content of the Limit Order Book," 7th Annual Canadian Agri-Food Policy Conference, January 11-13, 2017, Ottawa, ON 253251, Canadian Agricultural Economics Society.
    11. Lin, Chu-Bin & Chou, Robin K. & Wang, George H.K., 2018. "Investor sentiment and price discovery: Evidence from the pricing dynamics between the futures and spot markets," Journal of Banking & Finance, Elsevier, vol. 90(C), pages 17-31.
    12. Chen, Wei-Peng & Ling Lin, Shu & Lu, Jun & Wu, Chih-Chiang, 2018. "The impact of funding liquidity on market quality," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 153-166.
    13. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.

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