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Dynamic Stock Return–Volume Relation: Evidence From Emerging Asian Markets


  • Hsin-Yi Lin


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Suggested Citation

  • Hsin-Yi Lin, 2013. "Dynamic Stock Return–Volume Relation: Evidence From Emerging Asian Markets," Bulletin of Economic Research, Wiley Blackwell, vol. 65(2), pages 178-193, April.
  • Handle: RePEc:bla:buecrs:v:65:y:2013:i:2:p:178-193
    DOI: j.1467-8586.2011.00428.x

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    Cited by:

    1. Laura Ferrando & Román Ferrer & Francisco Jareño, 2017. "Interest Rate Sensitivity of Spanish Industries: A Quantile Regression Approach," Manchester School, University of Manchester, vol. 85(2), pages 212-242, March.
    2. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    3. Xu, Yongdeng & Taylor, Nick & Lu, Wenna, 2018. "Illiquidity and volatility spillover effects in equity markets during and after the global financial crisis: An MEM approach," International Review of Financial Analysis, Elsevier, vol. 56(C), pages 208-220.
    4. Panpan Wang & Tsungwu Ho & Yishi Li, 2020. "The Price-Volume Relationship of the Shanghai Stock Index: Structural Change and the Threshold Effect of Volatility," Sustainability, MDPI, Open Access Journal, vol. 12(8), pages 1-17, April.
    5. Parab Narayan & Y. V. Reddy, 2017. "Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market," Metamorphosis: A Journal of Management Research, , vol. 16(2), pages 122-140, December.
    6. Lee, Jaeram & Lee, Geul & Ryu, Doojin, 2018. "Difference in the intraday return-volume relationships of spots and futures: A quantile regression approach," Economics Discussion Papers 2018-68, Kiel Institute for the World Economy (IfW).
    7. Nasiri, S. & Bektas, E. & Jafari, G.R., 2018. "The impact of trading volume on the stock market credibility: Bohmian quantum potential approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1104-1112.
    8. Zhu, Huiming & Guo, Yawei & You, Wanhai & Xu, Yaqin, 2016. "The heterogeneity dependence between crude oil price changes and industry stock market returns in China: Evidence from a quantile regression approach," Energy Economics, Elsevier, vol. 55(C), pages 30-41.
    9. Imtiaz Mohammad Sifat & Azhar Mohamad, 2019. "Circuit breakers as market stability levers: A survey of research, praxis, and challenges," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(3), pages 1130-1169, July.
    10. Ashok Chanabasangouda Patil & Shailesh Rastogi, 2019. "Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(2), pages 1-18, June.
    11. Zhu, Huiming & Huang, Hui & Peng, Cheng & Yang, Yan, 2016. "Extreme dependence between crude oil and stock markets in Asia-Pacific regions: Evidence from quantile regression," Economics Discussion Papers 2016-46, Kiel Institute for the World Economy (IfW).
    12. Mehmet Balcilar & Elie Bouri & Rangan Gupta & David Roubaud, 2016. "Can Volume Predict Bitcoin Returns and Volatility? A Nonparametric Causality-in-Quantiles Approach," Working Papers 201662, University of Pretoria, Department of Economics.
    13. Jiranyakul, Komain, 2016. "Dynamic relationship between stock return, trading volume, and volatility in the Stock Exchange of Thailand: does the US subprime crisis matter?," MPRA Paper 73791, University Library of Munich, Germany.

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