Semiparametric Smooth Transition Quantile Autoregreeive Model and Its Application

KANG Ning, MO Luyao, JING Ke

Journal of Systems Science and Mathematical Sciences ›› 2024, Vol. 44 ›› Issue (3) : 844-861.

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Journal of Systems Science and Mathematical Sciences ›› 2024, Vol. 44 ›› Issue (3) : 844-861. DOI: 10.12341/jssms22446

Semiparametric Smooth Transition Quantile Autoregreeive Model and Its Application

  • KANG Ning1, MO Luyao1, JING Ke2
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Abstract

Smooth transition autoregressive model (STAR) often sets the transition function as logistic function or exponential function, and obtains the estimation, test and forecasting results by mean regression. We propose a new semi-parametric smooth transition quantile autoregressive model. Its main advantages are as follows: First, the smooth transition function is constructed by the barycentric rational interpolation function, which has more flexible form and can better reduce the risk of mispecification. Second, in the framework of quantile regression, the genetic algorithm is applied to obtain the coefficient estimation of new model, which is more informative than the mean regression. Numerical simulation results show that the autoregressive coefficient estimators have good performance in unbiasedness, effectiveness and consistency. Finally, the new model is applied to reveal and forecast the dynamic trend of stock returns of the Shanghai Composite Index, and the empirical results indicate that there exist nonlinear and heterogeneous characteristics of the return series.

Key words

Barycentric rational interpolation / quantile regression / smooth transition autoregression / genetic algorithm

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KANG Ning , MO Luyao , JING Ke. Semiparametric Smooth Transition Quantile Autoregreeive Model and Its Application. Journal of System Science and Mathematical Science Chinese Series, 2024, 44(3): 844-861 https://doi.org/10.12341/jssms22446

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