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Mathematical Problems in Engineering
Volume 2016, Article ID 1285768, 15 pages
http://dx.doi.org/10.1155/2016/1285768
Research Article

Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression

1School of Economics, Qingdao University, Qingdao, Shandong 266071, China
2School of Business Administration, Acadia University, Wolfville, NS, Canada B4P 2R6
3School of Banking and Finance, University of International Business and Economics, Beijing 100029, China

Received 25 March 2016; Accepted 7 September 2016

Academic Editor: Anna Pandolfi

Copyright © 2016 Lili Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Y. Campbell, S. J. Grossman, and J. Wang, “Trading volume and serial correlation in stock returns,” Quarterly Journal of Economics, vol. 108, no. 4, pp. 905–939, 1993. View at Publisher · View at Google Scholar
  2. J. Y. Campbell, A. W. Lo, and A. C. MacKinlay, The Econometrics of Financial Markets, Princeton University Press, Princeton, NJ, USA, 1997.
  3. J. Lewellen, “Momentum and autocorrelation in stock returns,” Review of Financial Studies, vol. 15, no. 2, pp. 533–564, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. A. W. Lo and A. C. MacKinlay, “An econometric analysis of nonsynchronous trading,” Journal of Econometrics, vol. 45, no. 1-2, pp. 181–211, 1990. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. R. Koenker and Z. Xiao, “Quantile autoregression,” Journal of the American Statistical Association, vol. 101, no. 475, pp. 980–990, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. D. G. Baur, “The structure and degree of dependence: a quantile regression approach,” Journal of Banking & Finance, vol. 37, no. 3, pp. 786–798, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Gebka and M. E. Wohar, “Causality between trading volume and returns: evidence from quantile regressions,” International Review of Economics & Finance, vol. 27, pp. 144–159, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. H.-M. Zhu, Z. Li, W. You, and Z. Zeng, “Revisiting the asymmetric dynamic dependence of stock returns: evidence from a quantile autoregression model,” International Review of Financial Analysis, vol. 40, pp. 142–153, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. M. F. M. Osborne, “Brownian motion in the stock market,” Operations Research, vol. 7, no. 2, pp. 145–173, 1959. View at Publisher · View at Google Scholar · View at MathSciNet
  10. R. L. Crouch, “The volume of transactions and price changes on the New York stock exchange,” Financial Analysts Journal, vol. 26, no. 4, pp. 104–109, 1970. View at Publisher · View at Google Scholar
  11. R. Westerfield, “The distribution of common stock price changes: an application of transactions time and subordinated stochastic models,” The Journal of Financial and Quantitative Analysis, vol. 12, no. 5, pp. 743–765, 1977. View at Publisher · View at Google Scholar
  12. I. G. Morgan, “Stock prices and heteroscedasticity,” The Journal of Business, vol. 49, no. 4, pp. 496–508, 1976. View at Publisher · View at Google Scholar
  13. L. Blume, D. Easley, and M. O'Hara, “Market statistics and technical analysis: the role of volume,” The Journal of Finance, vol. 49, no. 1, pp. 153–181, 1994. View at Publisher · View at Google Scholar
  14. G. McQueen, M. Pinegar, and S. Thorley, “Delayed reaction to good news and the cross-autocorrelation of portfolio returns,” Journal of Finance, vol. 51, no. 3, pp. 889–919, 1996. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Llorente, R. Michaely, G. Saar, and J. Wang, “Dynamic volume-return relation of individual stocks,” Review of Financial Studies, vol. 15, no. 4, pp. 1005–1047, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. S.-S. Chen, “Revisiting the empirical linkages between stock returns and trading volume,” Journal of Banking & Finance, vol. 36, no. 6, pp. 1781–1788, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. C. F. Lee and O. M. Rui, “Does trading volume contain information to predict stock returns? Evidence from China's stock markets,” Review of Quantitative Finance and Accounting, vol. 14, no. 4, pp. 341–360, 2000. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Säfvenblad, “Trading volume and autocorrelation: empirical evidence from the Stockholm Stock Exchange,” Journal of Banking & Finance, vol. 24, no. 8, pp. 1275–1287, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Bissoondoyal-Bheenick and R. D. Brooks, “Does volume help in predicting stock returns? An analysis of the Australian market,” Research in International Business and Finance, vol. 24, no. 2, pp. 146–157, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. G. M. Chen, M. Firth, and O. M. Rui, “The dynamic relation between stock returns, trading volume, and volatility,” The Financial Review, vol. 36, no. 3, pp. 153–174, 2001. View at Publisher · View at Google Scholar
  21. C.-C. Chuang, C.-M. Kuan, and H.-Y. Lin, “Causality in quantiles and dynamic stock return–volume relations,” Journal of Banking & Finance, vol. 33, no. 7, pp. 1351–1360, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. J. M. Karpoff, “The relation between price changes and trading volume: a survey,” Journal of Financial and Quantitative Analysis, vol. 22, no. 1, pp. 109–126, 1987. View at Publisher · View at Google Scholar
  23. W.-I. Chuang, H.-H. Liu, and R. Susmel, “The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility,” Global Finance Journal, vol. 23, no. 1, pp. 1–15, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. R. Koenker and G. Bassett Jr., “Regression quantiles,” Econometrica, vol. 46, no. 1, pp. 33–50, 1978. View at Publisher · View at Google Scholar · View at MathSciNet
  25. T. C. Chiang, J. Li, and L. Tan, “Empirical investigation of herding behavior in Chinese stock markets: evidence from quantile regression analysis,” Global Finance Journal, vol. 21, no. 1, pp. 111–124, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Gebka and M. E. Wohar, “The determinants of quantile autocorrelations: evidence from the UK,” International Review of Financial Analysis, vol. 29, pp. 51–61, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. D. G. Baur, T. Dimpfl, and R. C. Jung, “Stock return autocorrelations revisited: a quantile regression approach,” Journal of Empirical Finance, vol. 19, no. 2, pp. 254–265, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. M. D. McKenzie and R. W. Faff, “The determinants of conditional autocorrelation in stock returns,” Journal of Financial Research, vol. 26, no. 2, pp. 259–274, 2003. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Tan, T. C. Chiang, J. R. Mason, and E. Nelling, “Herding behavior in Chinese stock markets: an examination of A and B shares,” Pacific-Basin Finance Journal, vol. 16, no. 1-2, pp. 61–77, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Yao, C. Ma, and W. P. He, “Investor herding behaviour of Chinese stock market,” International Review of Economics and Finance, vol. 29, pp. 12–29, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model,” Abstract and Applied Analysis, vol. 2013, Article ID 143194, 13 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. R. Rezvanian, R. A. Turk, and S. M. Mehdian, “Investors' reactions to sharp price changes: evidence from equity markets of the People's Republic of China,” Global Finance Journal, vol. 22, no. 1, pp. 1–18, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. T. T.-L. Chong, T.-H. Lam, and I. K.-M. Yan, “Is the Chinese stock market really inefficient?” China Economic Review, vol. 23, no. 1, pp. 122–137, 2012. View at Publisher · View at Google Scholar · View at Scopus
  34. E. C. Chang, Y. Luo, and J. Ren, “Short-selling, margin-trading, and price efficiency: evidence from the Chinese market,” Journal of Banking & Finance, vol. 48, pp. 411–424, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. K. A. Kim and S. G. Rhee, “Price limit performance: evidence from the Tokyo Stock Exchange,” Journal of Finance, vol. 52, no. 2, pp. 885–901, 1997. View at Publisher · View at Google Scholar · View at Scopus
  36. K. A. Kim, H. Liu, and J. J. Yang, “Reconsidering price limit effectiveness,” Journal of Financial Research, vol. 36, no. 4, pp. 493–518, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. H. Li, D. Zheng, and J. Chen, “Effectiveness, cause and impact of price limit—evidence from China's cross-listed stocks,” Journal of International Financial Markets, Institutions and Money, vol. 29, no. 1, pp. 217–241, 2014. View at Publisher · View at Google Scholar · View at Scopus