| 基于趋势平滑和GARCH的证券市场预测 |
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| 引用本文： | 王炳雪,史忠科,阎东明.基于趋势平滑和GARCH的证券市场预测[J].西安理工大学学报,2002,18(1):93-97. |
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| 作者姓名： | 王炳雪 史忠科 阎东明 |
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| 作者单位： | 1. 西北工业大学,自动控制系,陕西,西安,710072
2. 西安理工大学,工商管理学院,陕西,西安,710048 |
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| 摘 要： | 首先将证券市场运动用局部多项式趋势模型进行平滑，然后分别用AR模型和GARCH模型考虑序列之间自相关性和波动的变化性。参数和条件最大似然估计应用了状态空间模型的卡尔曼滤子递推和GARCH模型的条件方差递推，模型阶数的选取应用了Akaike的最小化信息矩阵方法。计算实例表明了这种组合方法预测能力的优越性。
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| 关 键 词： | 证券市场 多项式趋势模型 状态空间模型 卡尔曼滤子 GARCH模型 |
| 文章编号： | 1006-4710(2002)01-0093-05 |
| 修稿时间： | 2001年3月28日 |
The Prediction of Security Market based on Trend Smoothing and GARCH |
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| WANG Bing xue ,SHI Zhong ke ,YAN Dong ming.The Prediction of Security Market based on Trend Smoothing and GARCH[J].Journal of Xi'an University of Technology,2002,18(1):93-97. |
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| Authors: | WANG Bing xue SHI Zhong ke YAN Dong ming |
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| Institution: | WANG Bing xue 1,SHI Zhong ke 1,YAN Dong ming 2 |
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| Abstract: | The operation of securities market is first smoothed by local polynomial trend model, then AR and GARCH models are used to study the variations in auto correlation and fluctuation between time series.The state space Kalman filter recursive computation and GARCH model's conditional variance recursive computation are used to estimate the conditional likelihood of parameters. Akaike's minimum AIC procedure is used to select the best model fitted to the data within and between the alternative model classes. The calculated real examples indicate that this combination method has distinct superiority in forecast. |
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| Keywords: | securities market polynomial trend mode state space model Kalman filter GARCH model |
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