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基于时间权重序列的GM(1,1)初始条件优化模型 被引量:14
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作者 郑坚 陈斌 《控制与决策》 EI CSCD 北大核心 2018年第3期529-534,共6页
灰色GM(1,1)模型的初始条件对其精度有着重要的影响,为了提高模型的精度,提出一种初始条件的优化方法.首先,综合考虑1-AGO序列中各分量的作用,取所有分量的加权平均作为初始条件,权重由各分量的大小决定;然后,基于时间权重序列,对模拟... 灰色GM(1,1)模型的初始条件对其精度有着重要的影响,为了提高模型的精度,提出一种初始条件的优化方法.首先,综合考虑1-AGO序列中各分量的作用,取所有分量的加权平均作为初始条件,权重由各分量的大小决定;然后,基于时间权重序列,对模拟序列与原始序列的误差平方和进行加权;最后,求解最优问题,确定时间参数,从而建立优化模型.算例表明,所提出的优化模型具有可行性,可以有效地提高模型的精度. 展开更多
关键词 初始条件 加权平均 GM(1 1)模型 时间权重序列
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Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application 被引量:1
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作者 蒋爱华 梅炽 +1 位作者 鄂加强 时章明 《Journal of Central South University》 SCIE EI CAS 2010年第4期863-867,共5页
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept... In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system. 展开更多
关键词 nonlinear combined forecasting nonlinear time series method of fuzzy adaptive variable weight relative error adaptive control coefficient
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AMERICAN OPTION PRICING UNDER GARCH DIFFUSION MODEL: AN EMPIRICAL STUDY 被引量:2
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作者 WU Xinyu YANG Wenyu +1 位作者 MA Chaoqun ZHAO Xiujuan 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2014年第1期193-207,共15页
The GARCH diffusion model has received much attention in recent years, as it describes financial time series better when compared to many other models. In this paper, the authors study the empirical performance of Ame... The GARCH diffusion model has received much attention in recent years, as it describes financial time series better when compared to many other models. In this paper, the authors study the empirical performance of American option pricing model when the underlying asset follows the GARCH diffusion. The parameters of the GARCH diffusion model are estimated by the efficient importance sampling-based maximum likelihood (EIS-ML) method. Then the least-squares Monte Carlo (LSMC) method is introduced to price American options. Empirical pricing results on American put options in Hong Kong stock market shows that the GARCH diffusion model outperforms the classical constant volatility (CV) model significantly. 展开更多
关键词 American option efficient importance sampling GARCH diffusion model least-squaresMonte Carlo maximum likelihood.
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