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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 8017510, 15 pages
https://doi.org/10.1155/2017/8017510
Research Article

New JLS-Factor Model versus the Standard JLS Model: A Case Study on Chinese Stock Bubbles

College of Finance and Statistics, Hunan University, Hunan, China

Correspondence should be addressed to Chao Li; moc.qq@207814727

Received 10 September 2016; Revised 30 November 2016; Accepted 14 December 2016; Published 18 January 2017

Academic Editor: Vincenzo Scalzo

Copyright © 2017 Zongyi Hu and Chao Li. 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

In this paper, we extend the Johansen-Ledoit-Sornette (JLS) model by introducing fundamental economic factors in China (including the interest rate and deposit reserve rate) and the historical volatilities of targeted and US equity indices into the original model, which is a flexible tool to detect bubbles and predict regime changes in financial markets. We then derive a general method to incorporate these selected factors in addition to the log-periodic power law signature of herding and compare the prediction accuracy of the critical time between the original and the new JLS models (termed the JLS-factor model) by applying these two models to fit two well-known Chinese stock indices in three bubble periods. The results show that the JLS-factor model with Chinese characteristics successfully depicts the evolutions of bubbles and “antibubbles” and constructs efficient end-of-bubble signals for all bubbles in Chinese stock markets. In addition, the results of standard statistical tests demonstrate the excellent explanatory power of these additive factors and confirm that the new JLS model provides useful improvements over the standard JLS model.