Abstract：Data acquisition of four time series of day trading on the whole Chinese P2P lending market is completed for empirical analysis. Using BDS nonlinear test method, the nonlinear dependence of the Chinese P2P lending market is tested and confirmed,In addition, an empirical analysis about whether there exists long memory in Chinese P2P lending market is conducted, using the classical R/S analysis method and the modified R/S analysis method. Results show that time series of P2P lending is nonlinear and a maybe low-dimensional chaotic process. It is found that there doesn’t show any long memory characteristics in each of P2P time series. Still staying in the primary stage of development and evolution at present, the P2P lending market is on transition stage from simple linear system to complicated open giant system.
[1]Luo B,Lin Z. A decision tree model for herd behavior and empirical evidence from the online P2P lending market [J]. Information Systems and E-Business Management,2013,11(1)141160. [2]孙柏. 基于非线性依赖关系分析的人民币汇率多元描述与预测 [D]. 湖南:湖南大学,2014. Sun Bo. The multivariate discription and forecasting of renminbi exchange rate based on nonlinear dependency analysis[D]. Hunan: Hunan University, 2014. [3]Caraiani P. Nonlinear dynamics in CEE stock markets indices [J]. Economics Letters,2012,114(3):329331. [4]Onali E,Goddard J. Are European equity markets efficient? new evidence from fractal analysis [J]. International Review of Financial Analysis,2011,20(2):5967. [5]Serletis A,Rosenberg A. Mean reversion in the US stock market [J]. Chaos Solitions & Fractals,2009,40(18):20072015. [6]Grech D,Pamula G. The local hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market [J]. Physica A: Statistical Mechanics and Its Applications,2008,387(16 17):42994308. [7]Lee C Y. Characteristics of the volatility in the Korea composite stock price index [J]. Physica A: Statistical Mechanics and Its Applications,2009,388(18):38373850. [8]汪冬华,索园园. 我国沪深300股指期货和现货市场的交叉相关性及其风险 [J]. 系统工程理论与实践,2014,34(3):631639. Wang Donghua, Suo Yuanyuan. Cross-correlation and risk measurement between CSI 300 index futures and spot markets in China[J]. Systems Engineering Theory and Practice, 2014,34(3):631639. [9]庄新田,张鼎,苑莹,等. 中国股市复杂网络中的分形特征 [J]. 系统工程理论与实践,2015,35(02):273282. Zhuang Xintian, Zhang Ding, Yuan Ying, et al. Fractal characteristic of the Chinese stock market complex network[J]. Systems Engineering Theory and Practice, 2015,35(2):273282. [10] 孙柏,李小静. 基于GARCH类模型的人民币汇率非线性依赖关系研究 [J]. 财经理论与实践,2016,37(199):4147. Sun Bo, Li Xiaojing. The study of the nonlinear dependency of the RMB exchange rate based on GARCH models[J]. The Theory and Practice of Finance and Economics,2016,37(199):4147. [11] 蒋勇,吴武清,叶五一,等. 股指期货基差的非线性特征和均值回复机制研究 [J]. 中国科学技术大学学报,2013,43(12):989996. Jiang Yong, Wu Wuqing, Ye Wuyi, et al. Nonlinear features and mean reversion mechanism research based on the basis of stock index futures[J]. Journal of University of Science and Technology of China, 2013,43(12):989996. [12] Brock W A,Dechert W,Scheinkman J. A test for independence based on the correlation dimension [J]. Econometric Reviews,1996,15(3):197235. [13] Lo A W. Long-term memory in stock market prices [J]. Econometrica,1991,59(5):12791313. [14] Bollerslev T,Zhou H. Volatility puzzles: a simple framework for gauging return-volatility regressions [J]. Journal of Econometrics,2006,131(12):123150.
