Threshold quantile autoregressive model with application to auto-correlation analysis of stock returns
XU Qi-fa1,2, KANG Ning1,3
1. School of Management, Hefei University of Technology, Hefei 230009, China; 2. MOE Key Laboratory of Process Optimization and Intelligent Decision-making, Hefei University of Technology, Hefei 230009, China; 3. School of Economics, Fuyang Normal University, Fuyang 236037, China
Abstract：The threshold quantile autoregressive (TQAR) model is a kind of nonlinear quantile regression model. It can be mainly used to test the threshold effects in a system. It is important to decide the optimal lag order of the autoregression and the threshold value that have huge impact on the performance of the TQAR model. To this end, we propose some methods for model selection, threshold parameter estimation, and threshold effect test. The numeric simulation results show that the TQAR model is superior to the TAR and TAR-GARCH model in terms of the accuracy of the threshold parameter estimation and regression coefficients estimation. Finally, we apply the TQAR model to reveal auto-correlation of stock returns in China. The empirical results indicate that there are threshold effects and heterogeneity effects in the auto-correlation. The findings are helpful to give a reasonable description of the dynamics in stock returns, which provides an empirical basis for re-understanding operating mechanism of financial market.
许启发, 康宁. 门限分位数自回归模型及在股市收益自相关分析中应用[J]. 系统工程理论与实践, 2015, 35(12): 2993-3007.
XU Qi-fa, KANG Ning. Threshold quantile autoregressive model with application to auto-correlation analysis of stock returns. Systems Engineering - Theory & Practice, 2015, 35(12): 2993-3007.
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2015, Vol. 35 
