Skip to main content
SpringerLink
Log in

Volume decomposition and volatility in dual-listing H-shares

  • Original Article
  • Published:
Journal of Asset Management Aims and scope Submit manuscript

Abstract

We investigate the volume impact on volatility for 14 Chinese ADRs and their underlying H-shares. We decompose volume into expected and unanticipated components and include those as determinants of conditional volatility in a bivariate GARCH model for each ADR and its underlying H-share. Expected volume denotes liquidity, while unanticipated volume implies information content in volume. The GARCH model fits the data well. In addition to the conventional GARCH parameters, for ADRs and their underlying H-shares, expected and unanticipated volumes significantly but asymmetrically affect both the variance and the covariance functions. Further, volume components asymmetrically impact volatility of ADRs and H-shares in high- versus low-liquidity and high- versus low-liquidity-risk buckets denoted by volume and standard deviation of volume, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. The literature on volume volatility is voluminous, and hence for the sake of space and contemporary relevance, we cite here only select and recent references post-2005 where available. See, for example, Alsubaie and Najand (2009), Qiao and Wong (2010), Xu et al. (2020), Girard and Biswas (2007), Jaiswal-Dale and Jitendranathan (2009), Sabbaghi (2011), Chuang et al. (2012), regarding single and multiple equity markets studies; Pati and Rajib (2010), Kamboroudis and McMillan (2016), Kao et al. (2020) regarding futures markets; and Omran and McKenzie (2000) and Carroll and Kearney (2012) regarding individual securities within a market.

  2. The Mixed distribution hypothesis (MDH) postulates that volume and volatility share a common distribution and has motivated early empirical testing of contemporaneous volume volatility relation. It is generally accepted that unanticipated rather than observed volume reveals information. While the term ‘abnormal’ volume is common in the accounting literature (refer to Bajo 2010), the finance literature prefers expected and unanticipated volume instead, referring to the expected value and the forecast error, respectively, from a forecasting model.

  3. Sita and Abdallah (2014) contain a brief but comprehensive literature review on bivariate GARCH models for return and volatility transmission between home and host country securities. Poshakwale and Aquino (2008) study volatility transmission across home and host markets for 70 ADRs from 13 countries.

  4. As of September 2010, there are 51 Chinese ADRs listed on NYSE but only 14 of them have underlying H-shares listed on the Stock Exchange of Hong Kong (SEHK).

  5. Regarding short- and long-run performance of ADRs from a diverse set of countries, refer to Schaub and Highfield (2004), Bancel et al. (2009), and Bandopadhyaya et al. (2008). Since ADR performance widely differs among countries, for the purpose of comparing our results, we have only referred to recently published studies related to Hong Kong ADRs.

  6. Lee and Rui (2002) decompose trading volume into expected and unanticipated or volume surprise based on a trend equation.

  7. We thank the referee for pointing us to the market microstructure literature as a way of explaining some of the empirical results in the paper. Nevertheless, we tread lightly and carefully regarding referencing the market microstructure theory and evidence since our data are low-frequency daily data and we do not use proper microstructure motivated variables like intraday quotes, trades, order size, trader types, and news arrival.

  8. A careful reader will immediately recognize that the mean volume is an unconditional measure, while the expected value measure of liquidity is a conditional measure.

References

  • Al-Ajmi, J. 2017. Trading volume and volatility in the Boursa Kuwait. The British Accounting Review 10: 0890–8389.

    Google Scholar 

  • Alsubaie, A., and M. Najand. 2009. Trading volume time varying conditional volatility and asymmetric volatility spillover in the Saudi stock market. Journal of Multinational Financial Management 19: 169–181.

    Article  Google Scholar 

  • Bancel, F., M. Kalimipalli, and U. Mittoo. 2009. Cross listing and the long term performance of ADRs: Revisiting European evidence. Journal of International Financial Markets, Institutions and Money 19: 895–923.

    Article  Google Scholar 

  • Bandopadhyaya, A., L. Chugh, and J. Grant. 2008. ADR characteristics and performance in international and global indexes. Journal of Asset Management 10: 9–21.

    Article  Google Scholar 

  • Bajo, E. 2010. The information content of abnormal trading volume. Journal of Business Finance and Accounting 37: 950–978.

    Article  Google Scholar 

  • Bohl, M.T., and H. Henke. 2003. Trading volume and stock market volatility: The Polish case. International Review of Financial Analysis 12 (5): 513–525.

    Article  Google Scholar 

  • Bose, S., and H. Rahman. 2015. Examining the relationship stock return volatility and trading volume: New evidence from an emerging market. Applied Economics 47: 1899–1908.

    Article  Google Scholar 

  • Brown, J., D. Crocker, and S. Foerster. 2009. Trading volume and investments. Financial Analyst Journal 65: 67–84.

    Article  Google Scholar 

  • Carroll, R., and C. Kearney. 2012. Do trading volumes explain the persistence of GARCH effects?. Applied Financial Economics 22: 1993–2008.

    Article  Google Scholar 

  • Chuang, W., H. Liu, and R. Susmel. 2012. The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and volatility. Global Finance Journal 23: 1–15.

    Article  Google Scholar 

  • Dey, M., and C. Wang. 2012. Return spread and liquidity: Evidence from Hong Kong ADRs. Review of International Business and Finance 26: 164–180.

    Article  Google Scholar 

  • Gallant, A.R., P.E. Rossi, and G. Tauchen. 1992. Stock prices and volume. The Review of Financial Studies 5: 199–242.

    Article  Google Scholar 

  • Gallo, G.M., and B. Pacini. 2010. The effects of trading activity on market volatility. The European Journal of Finance 6 (2): 163–175.

    Article  Google Scholar 

  • Girard, E., and R. Biswas. 2007. Trading volume and market volatility: Developed vs emerging stock markets. Financial Review 42: 429–459.

    Article  Google Scholar 

  • He, H., and J. Yang. 2012. Day and night returns of Chinese ADRs. Journal of Banking and Finance 36: 2795–2803.

    Article  Google Scholar 

  • Jaiswal-Dale, A., and T. Jitendranathan. 2009. Transmission of shocks from cross-listed markets to the return and volatility of domestic stocks. Journal of Multinational Financial Management 19: 395–408.

    Article  Google Scholar 

  • Jang, H.J., and B. Ro. 1989. Trading volume theories and their implications for information content studies. Contemporary Accounting Research 6: 242–262.

    Article  Google Scholar 

  • Kamboroudis, D., and D. McMillan. 2016. Does VIX or volume improve GARCH volatility forecasts?. Applied Economics 48: 1210–1228.

    Article  Google Scholar 

  • Kao, Y., H. Chuang, and Y. Ku. 2020. The empirical linkages among market returns, ret volatility, and trading volume: Evidence from the S&P 500 VIX futures. The North American Journal of Economics and Finance 54: 100871.

    Article  Google Scholar 

  • Kutan, A. 2007. Contagion or real linkage? Some evidence from China’s emerging parallel markets. China and World Economy 15: 52–65.

    Article  Google Scholar 

  • Kutan, A., and H. Zhou. 2006. Determinants of return and volatility of Chinese ADRs at NYSE. Journal of Multinational Financial Management 16: 1–15.

    Article  Google Scholar 

  • Lamoureux, C.G., and W.D. Lastrapes. 1990. Heteroskedasticity in stock return data: Volume versus GARCH effects. The Journal of Finance 45 (1): 221–229.

    Article  Google Scholar 

  • Lee, B.S., and O.M. Rui. 2002. The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence. Journal of Banking and Finance 26: 51–78.

    Article  Google Scholar 

  • Lo, A., and J. Wang. 2000. Trading volume definitions data analysis and impact of portfolio theory. Review of Financial Studies 13: 257–300.

    Article  Google Scholar 

  • Lucey, B. 2005. Does volume provide information? Evidence from the Irish market. Applied Financial Economics Letters 1: 105–109.

    Article  Google Scholar 

  • Mak, B.S.C., and A.M.S. Ngai. 2014. Market linkage for dual-listed Chinese stocks. The Chinese Economy 38 (2): 88–107.

    Article  Google Scholar 

  • Omran, M.F., and E. McKenzie. 2000. Heteroscedasticity in stock returns data revisited: Volume vs GARCH effects. Applied Financial Economics 10: 553–560.

    Article  Google Scholar 

  • Pati, P., and P. Rajib. 2010. Volatility persistence and trading volume in an emerging futures markets: Evidence from NSE Nifty stock index futures. The Journal of Risk Finance 11: 296–309.

    Article  Google Scholar 

  • Poshakwale, S., and K. Aquino. 2008. The dynamics of volatility transmission and information flow between ADRs and their underlying stocks. Global Finance Journal 19: 187–201.

    Article  Google Scholar 

  • Qiao, Z., and W. Wong. 2010. Revisiting volume vs. GARCH effects using univariate and bivariate GARCH models: Evidence from U.S. stock markets. In Handbook of Quantitative Finance and Risk Management, ed. C.F. Lee, A.C. Lee, and J. Lee. Boston: Springer.

    Google Scholar 

  • Sabbaghi, O. 2011. Asymmetric volatility and trading volume: The G5 evidence. Global Finance Journal 22: 169–181.

    Article  Google Scholar 

  • Schaub, M., and M. Highfield. 2004. The short and long term performance of IPOs and SEOs traded as ADR: Does timing matter?. Journal of Asset Management 5: 263–271.

    Article  Google Scholar 

  • Sita, B., and W. Abdallah. 2014. The volatility links between the home and host market for UK dual listed stocks on US markets. Journal of International Financial Markets, Institutions and Money 24: 1–36.

    Google Scholar 

  • Xu, X., and H. Fung. 2002. Information flows across markets: Evidence from China backed stocks dual listed in Hong Kong and New York. Financial Review 37: 563–588.

    Article  Google Scholar 

  • Xu, L., H. Gao, Y. Shi, and Y. Zhao. 2020. The heterogenous relation between volume-volatility relations in the ETF market: The evidence from China. Economic Modeling 85: 400–408.

    Article  Google Scholar 

  • Xuan, V., and C. Ellis. 2018. International financial integration: stock return linkage and volatility transmission between Vietnam and other advanced countries. Emerging Markets Review 36: 19–27.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Independent, 1091 Stillson Road, Fairfield, CT, 06824, USA

    Malay K. Dey

  2. School of Economics, UNNC-NFTZ Blockchain Laboratory, University of Nottingham, Ningbo, China

    Chaoyan Wang

Authors
  1. Malay K. Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaoyan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malay K. Dey.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dey, M.K., Wang, C. Volume decomposition and volatility in dual-listing H-shares. J Asset Manag 22, 301–310 (2021). https://doi.org/10.1057/s41260-021-00207-3

Download citation

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41260-021-00207-3

Keywords

Search

Navigation