Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption tha...Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption that the variance is a constant or it changes with the seasons.However,hydrological processes in the real world are often heteroscedastic,which can be tested by McLeod-Li test and Engle Lagrange multiplier test.In such cases,the GARCH model of hydrological processes is established in this article.First,the seasonal factors in the sequence are removed.Second,the traditional ARMA model is established.Then,the GARCH model is used to correct the residual.At last,the daily runoff data in 1949-2001 of Yichang Hydrological Station is taken to be an example.The result shows that compared to the traditional ARMA model,the GARCH model has the ability to predict more accurate confidence intervals under the same confidence level.展开更多
基金supported by the National Hi-Tech Research and Development Program of China ("863" Project) (Grant No. 2012BAB02B04)
文摘Uncertainty analysis and risk analysis are two important areas of modern water resource management,in which accurate variance estimation is required.The traditional runoff model is established under the assumption that the variance is a constant or it changes with the seasons.However,hydrological processes in the real world are often heteroscedastic,which can be tested by McLeod-Li test and Engle Lagrange multiplier test.In such cases,the GARCH model of hydrological processes is established in this article.First,the seasonal factors in the sequence are removed.Second,the traditional ARMA model is established.Then,the GARCH model is used to correct the residual.At last,the daily runoff data in 1949-2001 of Yichang Hydrological Station is taken to be an example.The result shows that compared to the traditional ARMA model,the GARCH model has the ability to predict more accurate confidence intervals under the same confidence level.
文摘股票价格买卖差是衡量金融市场流动性和有效性的重要指标,已经得到学术界的广泛研究。相比而言,作为衡量股票市场风险的重要因素的股票价格买卖价差的波动率却没有得到相同的重视。在广义自回归条件异方差(generalized autoregressive conditional heteroscedasticity,GARCH)模型的基础上,提出了GARCH-neural network(GARCH-NN)混合模型分析股票价格买卖价差波动率的动态性。以深圳证券交易所成分股价指数的高频数据为样本对所提模型进行了实证分析。运用GARCH家族模型对股票价格买卖差波动率的动态性进行分析,得出预测效果最优的GARCH模型。在最优GARCH模型的基础上结合神经网络分析方法即GARCH-NN混合模型对样本数据进行了实证分析。比较分析最优GARCH模型和GARCH-NN混合模型对股票价格买卖差波动率的预测效果,并以AIC(Akaike information criterion)和BIC(Bayesian information criterion)作为检验模型预测效果的指标。实证结果表明,提出的GARCH-NN混合模型更优。
