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A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET
作者姓名:庞素琳  邓飞其  王燕鸣
作者单位:[1]Department of Mathematics, Jinan University, Guangzhou 510632, China [2]School of Automatic Science & Engineering, South China University of Technology, Guangzhou 510640, China [3]School of Mathematics & Lingnan College, Zhongshan University, Guangzhou 510275, China
基金项目:国家自然科学基金,广东省软科学研究计划
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:深圳股票市场  波动性  预测模型  逻辑回归模型  比较
收稿时间:2004-10-14
修稿时间:2005-09-18

A comparison of forecasting models of the volatility in Shenzhen Stock Market
Sulin Pang, Feiqi Deng,Yanming Wang.A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET[J].Acta Mathematica Scientia,2007,27(1):125-136.
Authors:Sulin Pang  Feiqi Deng  Yanming Wang
Institution:aDepartment of Mathematics, Jinan University, Guangzhou 510632, China;bSchool of Automatic Science & Engineering, South China University of Technology, Guangzhou 510640, China;cSchool of Mathematics & Lingnan College, Zhongshan University, Guangzhou 510275, China
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(l) model and the Logistic regression model.
Keywords:Logistic regression model  AR(1) model  AR(2) model  volatility
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