天气衍生品中时变均值回复的气温预测模型研究  

The Time-varying Mean-reverting Model of Forecasting Temperature in Weather Derivatives

作　　者：陈百硕 李守伟[1] 何建敏[1] 曹杰[2] 

机构地区：[1]东南大学经济管理学院,江苏南京211189 [2]南京信息工程大学经济管理学院,江苏南京210044

出　　处：《管理工程学报》2014年第2期145-150,共6页Journal of Industrial Engineering and Engineering Management

基　　金：公益性行业(气象)科研专项资助项目(GYHY201106019);国家自然科学基金资助项目(71071034)

摘　　要：天气衍生品的出现,为天气风险管理提供了一种新的手段。由于大多数天气衍生品都是以气温为标的物,因此对气温变化准确预测是天气衍生品定价的关键。针对此,本文在O-U均值回复模型的基础上,构建了时变均值回复速度的气温预测模型。基于全国代表性城市的1951—2011年日平均气温数据对气温均值回复速度进行了实证分析,研究表明,我国主要代表性城市气温年均值回复速度均能通过ARIMA模型进行拟合估计。利用两种模型对各城市的日平均气温进行预测分析,其误差衡量指标的协方差比表明,时变均值回复模型优于O-U均值回复模型。因此,本文提出的时变均值回复速度的气温预测模型适用于我国的气温预测,有利于我国天气衍生品的进一步研究。The purpose of weather derivatives is to allow businesses and other organizations,which are affected by the weather,to insure themselves against weather changes.Most of researches about weather derivatives mainly focus on temperature derivatives based on HDD and CDD indices.Hence,forecasting the trend of temperature accurately is critical to pricing the weather derivatives.Therefore,this paper mainly studies the forecasting temperature in pricing weather derivatives.In the first part,we give a short overview of weather derivatives including their concepts,the application prospects and the research trend.We further summarize recent studies using the O-U mean-reverting model to price temperature derivatives.The previous studies based on the O-U mean-reverting model considered the mean-reverting speed to be a fixed constant.However,in real life human activity,solar activity,atmospheric changes and other factors will have impact on the climate,thereby affecting the temperature mean-reverting effect.To forecast the change of the temperature more accurately,we considered the mean-reverting speed to change over time.Therefore,this paper proposes a new forecasting temperature model which is called Time-varying mean-reverting model based on the O-U process.The unknown parameters in the model are estimated using historical temperature data.Since we only have discrete observations,the estimation of mean-reverting speed in the model is based on the use of martingale estimation functions.The quadratic variation of the temperature＇s volatility varies across different months of the year,but nearly constant within each month.In the second part,to obtain the characteristic of the mean-reverting speed we analyzed the data in each year from 1951 to 2011 collected from China＇s main cities,including Nanjing,Hangzhou,Wuhan and Beijing.The ADF test of Wuhan and Hangzhou shows that their annual mean-reverting speed sequences are stationary series,and can be further treated as white noise sequence.After firstorder difference processi

关 键 词：天气衍生品 O-U均值回复模型 时变均值回复速度 ARIMA模型 

分 类 号：F830.9[经济管理—金融学]