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Return-Volume Dependence and Extremes in International Equity Markets

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  • Terry A. Marsh and Niklas Wagner.
Abstract
This paper reconsiders return-volume dependence for the U.S. and six international equity markets. We contribute to previous work by proposing surprise volume as a new proxy for private information flow and apply extreme value theory in studying dependence for large volume and return, i.e. under situations of market stress. Results from a GARCH-M model indicate that surprise volume is superior in explaining conditional variance and reveals a positive market risk premium. Under conditions of market stress, the return-volume dependence is weaker, albeit mostly significant. The results for the U.S. market are most pronounced in that surprise volume explains ARCH- as well as leverage- effects and, under market stress, the return-volume dependence remains significant and symmetric. For the European and Asian markets, however, the dependence is weaker with asymmetry under market stress, i.e. small minimal returns show lower volume dependence than large maximal returns. We argue that our results are more consistent with a Gennotte and Leland (1990) misinterpretation hypothesis for market crashes than with cascade or behavioral explanations which associate high volume with steep price declines.
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Suggested Citation

  • Terry A. Marsh and Niklas Wagner., 2000. "Return-Volume Dependence and Extremes in International Equity Markets," Research Program in Finance Working Papers RPF-293, University of California at Berkeley.
    • Handle: RePEc:ucb:calbrf:rpf-293
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    Cited by:

    1. Ning, Cathy & Wirjanto, Tony S., 2009. "Extreme return-volume dependence in East-Asian stock markets: A copula approach," Finance Research Letters, Elsevier, vol. 6(4), pages 202-209, December.
    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. Michael Rockinger & Eric Jondeau, 2001. "Conditional Dependency of Financial Series: An Application of Copulas," Working Papers hal-00601478, HAL.
    4. Batten, Jonathan A. & Boubaker, Sabri & Kinateder, Harald & Choudhury, Tonmoy & Wagner, Niklas F., 2023. "Volatility impacts on global banks: Insights from the GFC, COVID-19, and the Russia-Ukraine war," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 325-350.
    5. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2006. "Institutional Investors and Stock Market Volatility," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(2), pages 461-504.
    6. Rockinger, Michael & Poon, Ser-Huang & Tawn, Jonathan, 2001. "New Extreme-Value Dependence Measures and Finance Applications," CEPR Discussion Papers 2762, C.E.P.R. Discussion Papers.
    7. Go, You-How & Lau, Wee-Yeap, 2020. "The impact of global financial crisis on informational efficiency: Evidence from price-volume relation in crude palm oil futures market," Journal of Commodity Markets, Elsevier, vol. 17(C).
    8. Czauderna, Katrin & Riedel, Christoph & Wagner, Niklas, 2015. "Liquidity and conditional market returns: Evidence from German exchange traded funds," Economic Modelling, Elsevier, vol. 51(C), pages 454-459.
    9. Bartosz Gębka, 2012. "The Dynamic Relation Between Returns, Trading Volume, And Volatility: Lessons From Spillovers Between Asia And The United States," Bulletin of Economic Research, Wiley Blackwell, vol. 64(1), pages 65-90, January.
    10. Aboura, Sofiane & Wagner, Niklas, 2016. "Extreme asymmetric volatility: Stress and aggregate asset prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 47-59.
    11. Jian, Zhihong & Wu, Shuai & Zhu, Zhican, 2018. "Asymmetric extreme risk spillovers between the Chinese stock market and index futures market: An MV-CAViaR based intraday CoVaR approach," Emerging Markets Review, Elsevier, vol. 37(C), pages 98-113.
    12. Sun, Changyou, 2013. "Price variation and volume dynamics of securitized timberlands," Forest Policy and Economics, Elsevier, vol. 27(C), pages 44-53.
    13. Ngene, Geoffrey M. & Mungai, Ann Nduati, 2022. "Stock returns, trading volume, and volatility: The case of African stock markets," International Review of Financial Analysis, Elsevier, vol. 82(C).
    14. Razvan Stefanescu & Ramona Dumitriu, 2016. "Contrarian and Momentum Profits during Periods of High Trading Volume preceded by Stock Prices Shocks," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 378-384.
    15. Yousaf, Imran & Yarovaya, Larisa, 2022. "The relationship between trading volume, volatility and returns of Non-Fungible Tokens: evidence from a quantile approach," Finance Research Letters, Elsevier, vol. 50(C).
    16. Eric Jondeau & Michael Rockinger, 2002. "Conditional Dependency of Financial Series: The Copula-GARCH Model," FAME Research Paper Series rp69, International Center for Financial Asset Management and Engineering.
    17. Naeem, Muhammad & Bouri, Elie & Boako, Gideon & Roubaud, David, 2020. "Tail dependence in the return-volume of leading cryptocurrencies," Finance Research Letters, Elsevier, vol. 36(C).
    18. Fan, Yunqi & Fu, Hui, 2020. "Institutional investors, selling pressure and crash risk: Evidence from China," Emerging Markets Review, Elsevier, vol. 42(C).
    19. Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2022. "An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
    20. Gabaix, Xavier & Gopikrishnan, Parameswaran & Plerou, Vasiliki & Stanley, Eugene, 2007. "A unified econophysics explanation for the power-law exponents of stock market activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 81-88.
    21. Fousekis, Panos & Tzaferi, Dimitra, 2021. "Returns and volume: Frequency connectedness in cryptocurrency markets," Economic Modelling, Elsevier, vol. 95(C), pages 13-20.
    22. Będowska-Sójka, Barbara & Echaust, Krzysztof & Just, Małgorzata, 2022. "The asymmetry of the Amihud illiquidity measure on the European markets: The evidence from Extreme Value Theory," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    23. Gebka, Bartosz, 2006. "Leaders and Laggards: International Evidence on Spillovers in Returns, Variance, and Trading Volume," Working Paper Series 2006,1, European University Viadrina Frankfurt (Oder), The Postgraduate Research Programme Capital Markets and Finance in the Enlarged Europe.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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