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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.

Abstract

We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7) and China. The quantile autoregression model (QAR) enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.