数学物理学报(英文版)

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A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

庞素琳; 邓飞其; 王燕鸣   

  1. 暨南大学数学系, 广州 510632
  • 收稿日期:2004-10-14 修回日期:2005-09-18 出版日期:2007-01-20 发布日期:2007-01-20
  • 通讯作者: 庞素琳
  • 基金资助:

    The research is supported by the National Natural Science Foundation of China (60574069) and the Soft Science Foundation of Guangdong Province (2005B70101044)

A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

Pang Sulin; Deng Feiqi; Wang Yanming   

  1. Department of Mathematics, Jinan University, Guangzhou 510632, China
  • Received:2004-10-14 Revised:2005-09-18 Online:2007-01-20 Published:2007-01-20
  • Contact: Pang Sulin

摘要:

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models: Logistic, AR(1) and AR(2). The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR(1) model and the AR(2) model of zero-mean series of the weekly closing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price. Six common statistical methods for error prediction are used to test the predicting results. These methods are: mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), Akaike's information criterion (AIC), and Bayesian information criterion (BIC). The investigation shows that
AR(1) model exhibits the best predicting result, whereas AR(2) model exhibits predicting results that is intermediate between AR(1) model and the Logistic regression model.

关键词: Logistic regression model, AR(1) model, AR(2) model, volatility

Abstract:

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models: Logistic, AR(1) and AR(2). The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR(1) model and the AR(2) model of zero-mean series of the weekly closing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price. Six common statistical methods for error prediction are used to test the predicting results. These methods are: mean error (ME), mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), Akaike's information criterion (AIC), and Bayesian information criterion (BIC). The investigation shows that
AR(1) model exhibits the best predicting result, whereas AR(2) model exhibits predicting results that is intermediate between AR(1) model and the Logistic regression model.

Key words: Logistic regression model, AR(1) model, AR(2) model, volatility

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