基于风险中性FBR和PNN的金融资产收益率序列预测模型的研究

Financial Asset Yield Series Forecasting Based on Risk-Neutral Fuzzy Bilinear Regression and Probabilistic Neural Network

在传统的时间序列预测模型难以揭示复杂非线性动态金融系统的内在规律的情况下,定量方法在金融市场中的应用引起了研究者和管理者的极大关注。本文基于模糊技术,结合统计工具和人工神经网络,构造了一个新的三阶段混合预测系统。在第一阶段以最小化平方和误差来拟合模糊自回归模型阶段的系数,并对样本数据进行分组。第二阶段引入风险观点,建立基于二次规划算法的模糊双线性回归模型,该模型既反映了最小二乘法的性质,又反映了无专家知识的可能性方法的性质。在第三阶段,结合概率神经网络,得到更大可能性的预测区间。通过对上证指数历史金融资产收益率序列的统计验证和分析,证明该混合预测模型的有效性。

Application of quantitative methods for forecasting purposes in financial markets has attracted significant attention from researchers and managers in recent years when conventional time series forecasting models can hardly develop the inherent rules of complex nonlinear dynamic financial systems. In this paper, based on the fuzzy technique integrated with the statistical tools and artificial neural network, a new hybrid forecasting system consisting of three stages is constructed. The sum of squared errors is minimized to determine the coefficients in fitting the fuzzy autoregression model stage for formulating sample groups to deal with data containing outliers. Fuzzy bilinear regression model introducing risk view based on quadratic programming algorithm that reflects the properties of both least squares and possibility approaches without expert knowledge is developed in the second stage. In the third stage, fuzzy bilinear regression forecasting combining with the optimal architecture of probabilistic neural network classifiers indicates that the proposed method has great contribution to control over-wide interval financial data with a certain confidence level. Statistical validation and performance analysis using historical financial asset yield series on Shanghai Stock Exchange composite index all exhibit the effectiveness of the proposed hybrid forecasting formulation compared with other forecasting methods.