天气衍生品气温预测模型对比研究

Comparison of Temperature Forecasting Model Using in Weather Derivatives Designing

气温衍生品是天气衍生品交易中最活跃的合约之一,确定合理预测气温动态变化的模型,是气温衍生品开发设计的基础。考虑到气温在时间变化上具有趋势性、季节性和周期性等特点,文中使用了以O-U均值回复过程为基础的Continuous Time Autoregressive Model(CAR)模型、Seasonal Autoregressive Integrated Moving Average(SARIMA)模型和小波神经网络算法,并选择漠河、北京、乌鲁木齐、芜湖、昆明和海口具有地域性代表的城市气温进行拟合,使用无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差检验指标检验了模型的预测精度。研究结果表明,小波神经网络算法在预测6个城市的无偏绝对百分比误差、绝对百分比误差和平均绝对比例误差的值最小;同时,相比CAR模型、SARIMA模型,其预测效果最优。因此,小波神经网络算法能够很好地拟合气温数据的变化,可以为我国气温天气衍生品的定价提供一定的指导。

Temperature derivatives is one of the most active contracts in the weather derivatives transactions,so making an appropriate temperature forecasting model is the basis for the design of temperature derivatives.Considering the temperature time series always saccompanied by trend characteristic,seasonality pattern and cycle,this paper uses the continuous time autoregressive model(CAR)based on ornstein-uhlenbeck process,seasonal autoregressive integrated moving average(SARIMA)model and wavelet neural network algorithm these three models to fit the temperature of Mohe,Beijing,Urumqi Wuhu,Kunming and Hai-kou,which are the regional representative cities overall the China.In the study,unbiased absolute percentage error,standard absolute percentage Error and Mean Absolute Scaled Error are used to test forecasting accuracy of these three temperature models.The forecasting accuracy results show that compared with the continuous time autoregressive model and SARIMA model,wavelet neural network has the smallest the values of the unbiased absolute percentage error,standard absolute percentage error and mean absolute scaled error,which shows the best forecasting performance.Wavelet neural network can well fit the changes of temperature’s process and provide significance for temperature derivatives pricing.